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Social Science Computer Review
Journal Prestige (SJR): 1.229
Citation Impact (citeScore): 3
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0894-4393 - ISSN (Online) 1552-8286
Published by Sage Publications Homepage  [1176 journals]
  • Online and Social Media Political Participation: Political Discussion
           Network Ties and Differential Social Media Platform Effects Over Time

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      Authors: Timilehin Durotoye; Manuel Goyanes, Rosa Berganza, Homero Gil de Zúñiga1Donald P. Bellisario College of Communications Pennsylvania State University, USA216726Carlos III University of Madrid, Spain316776Universidad Rey Juan Carlos, España416779College of Law & Public Administration University of Salamanca, Spain5Facultad de Comunicación y Letras Universidad Diego Portales, Chile
      Abstract: Social Science Computer Review, Ahead of Print.
      Prior research has largely documented the overall mobilizing effects of social media news consumption and political discussion linked to citizens’ political participatory behaviors. However, limited empirical research has considered the informational and ...
      Citation: Social Science Computer Review
      PubDate: 2025-04-19T02:24:42Z
      DOI: 10.1177/08944393251332640
       
  • Minority-Owned, Claimed Status, and Profile Attributes of Businesses on
           Google Maps: COVID-19 Pandemic Survival

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      Authors: Gi Woong Yun; Sung-Yeon Park1University of Nevada, Reno
      Abstract: Social Science Computer Review, Ahead of Print.
      Theoretical frameworks Resource-Based View (RBV) and competitive advantages have served as conceptual foundations for investigating the role of Google Maps in business success. This research has two key findings: First, an analysis of a dataset obtained ...
      Citation: Social Science Computer Review
      PubDate: 2025-04-14T11:35:04Z
      DOI: 10.1177/08944393251333365
       
  • Automated Detection of Media Bias Using Artificial Intelligence and
           Natural Language Processing: A Systematic Review

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      Authors: Mar Castillo-Campos; David Becerra-Alonso, Hajo G. Boomgaarden1376603Loyola Andalucía University, Sevilla, Spain227258University of Vienna, Wien, Austria
      Abstract: Social Science Computer Review, Ahead of Print.
      Media bias has long been a subject of scholarly interest due to its potential to shape public perceptions and behaviors. This systematic review leverages advances in natural language processing (NLP) to explore automated methods to detect media bias, ...
      Citation: Social Science Computer Review
      PubDate: 2025-04-03T10:00:55Z
      DOI: 10.1177/08944393251331510
       
  • Electoral Forecasting in Volatile Party System Settings: Assessing and
           Improving Pre-Election Poll Predictions in Italy

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      Authors: Kenneth Bunker1Universidad San Sebastián; Chile
      Abstract: Social Science Computer Review, Ahead of Print.
      This study examines electoral forecasting in volatile party systems, focusing on factors contributing to deviations between poll predictions and actual election outcomes. Using Italy as a case study, it identifies biases in polling data and proposes a ...
      Citation: Social Science Computer Review
      PubDate: 2025-03-24T02:56:09Z
      DOI: 10.1177/08944393251328309
       
  • Exploring the Use of a Large Language Model for Inductive Content Analysis
           in a Discourse Network Analysis Study

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      Authors: Steve Randerson; Thomas Graydon-Guy, En-Yi Lin, Sally Casswell1385563Massey University, New Zealand
      Abstract: Social Science Computer Review, Ahead of Print.
      Large language models show promising capability in some qualitative content analysis tasks; however, research reporting their performance in identifying initial codes that underpin subsequent analysis is scarce. This paper explores the suitability of GPT-...
      Citation: Social Science Computer Review
      PubDate: 2025-03-14T11:51:48Z
      DOI: 10.1177/08944393251326175
       
  • Unveiling the Making of Trending Topics on a Digital Platform: A Research
           Note on Chinese Sina Weibo

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      Authors: Xiaoyan Liu; Jiarui Zhao, Zhiyao Li, Dan Wang, Anfan Chen1School of Languages Law, Beijing, China3Guangming Daily, Beijing, China4School of Communication, Hong Kong Baptist University, Kowloon, Hong Kong
      Abstract: Social Science Computer Review, Ahead of Print.
      Understanding the dynamics and duration of trending topics on digital platforms has been deemed a crucial issue of research in computer-mediated communication. Employing event history analysis (EHA), this study attempts to examine the antecedents of the ...
      Citation: Social Science Computer Review
      PubDate: 2025-03-11T11:01:53Z
      DOI: 10.1177/08944393251324647
       
  • Response Times and Self-Reporting: Response Patterns Across Countries and
           World Regions Using Data From a Large Scale Computer-Based Assessment

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      Authors: Hana Vonkova, Ondrej Papajoanu, Martin Bosko; Ondrej Papajoanu, Martin Bosko1The Anchoring Center for Educational Research, Faculty of Education, 37740Charles University, Prague, Czech Republic
      Abstract: Social Science Computer Review, Ahead of Print.
      Understanding reporting behavior in questionnaires is a key issue in enhancing cross-national data comparability and policy decisions. Computers help improve the analysis of careless or insufficient effort (C/IE) responding by logging response times and other response behavior, ensuring data quality. We introduce a response-time based approach, built on an analysis of the relationship between a survey item and a related external variable, to cross-national research. Using PISA 2015 data from 58 countries/economies, we analyze patterns of correlations between the enjoyment of science and science test scores across response time. We focus on C/IE responding towards the beginning of the response time spectrum. Results indicate rather diligent responding in Eastern Asia and a part of Northern Europe. Yet in other regions (e.g., part of Latin America and the Caribbean, and Eastern Europe), C/IE responding might be distorting the data. We provide other researchers with information regarding when and to what extent C/IE responding can occur across countries. We enhance the understanding of heterogeneity in reporting behavior across countries.
      Citation: Social Science Computer Review
      PubDate: 2025-02-25T01:35:12Z
      DOI: 10.1177/08944393251322160
       
  • Anything but Politics: Connectedness in Networked Social Groups for
           Addressing Prejudice

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      Authors: Brandon C. Bouchillon; USA
      Abstract: Social Science Computer Review, Ahead of Print.
      Research indicates that prejudice has been growing in America. Citizens feel increasingly threatened by immigrants, and hate crimes against immigrant groups have risen. Declining interpersonal contact has also made it more difficult to address prejudice directly. This study examines whether nonpolitical social media groups can foster connections that reduce prejudice. These groups allow users to connect on the basis of shared interests, enabling diverse individuals to form close relationships which may improve attitudes toward immigrants. Using a national web survey matched to U.S. Census percentages for sex, race, ethnicity, age, and region of residence (N = 1500), along with a two-wave panel conducted over six weeks (N = 752), results indicate that blatant prejudice is more prevalent than subtle prejudice. Respondents were more likely to feel threatened by immigrants than to withhold positive emotions from them. As a remedy, social connectedness in nonpolitical groups was associated with diminished blatant prejudice and lower levels of global prejudice, a measure that includes both subtle and blatant components. Findings suggest that feeling connected with different people remotely can improve attitudes toward racial and ethnic diversity, helping individuals feel less threatened by immigrants and less prejudiced overall.
      Citation: Social Science Computer Review
      PubDate: 2025-02-24T07:11:26Z
      DOI: 10.1177/08944393251320059
       
  • AI Chatbots in Political Campaigns: A Practical Experience in the EU’s
           2024 Parliament Elections

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      Authors: Davide Tosi, Marco Chiappa, Dario Pizzul; Marco Chiappa, Dario Pizzul119045University of Insubria, Italy29304University of Milan, Italy319001University of Pavia, Italy
      Abstract: Social Science Computer Review, Ahead of Print.
      As the application of artificial intelligence in various domains and sectors grows, politics—especially political communication—is no exception. However, academic considerations on the topic remain limited, partly due to its novelty. To contribute to the ongoing discussions at the intersection of AI and political campaigns, this research report presents the development and use of an AI chatbot employed by an Italian candidate during the 2024 European Parliament elections. The aim of this work is to engage with the technical aspects of the tool’s development and implementation by outlining the challenges and strategies involved in creating an AI chatbot that supports a political campaign using OpenAI APIs. Furthermore, this report offers reflections on the role of AI in politics and communication, focusing on the concepts of intermediation and participation, also addressing issues of compliance and trustworthiness of these new AI tools.
      Citation: Social Science Computer Review
      PubDate: 2025-02-12T02:12:03Z
      DOI: 10.1177/08944393251320063
       
  • The Use of Religion Online by Indian Political Entities During the 2024
           Lok Sabha Election: Religiopolitical Propaganda on Social Media'

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      Authors: Mridha Md. Shiblee Noman, Md. Sayeed Al-Zaman; Md. Sayeed Al-Zaman1Department of Journalism Media Studies, 115523Jahangirnagar University, Dhaka, Bangladesh
      Abstract: Social Science Computer Review, Ahead of Print.
      The intersection of politics and religion in India has gained significant scholarly attention, particularly since the Bharatiya Janata Party’s (BJP) rise to power in 2014. The increasing impact of social media on Indian politics has intensified this concern. However, it is yet to be fully explored how social media was used for religiopolitical purposes during the Indian election in 2024. We computationally analyzed 3082 Facebook posts using BERTopic, word embedding, and cluster analysis to understand how politicians, political candidates, political organizations, and political parties intertwined religion and politics during the 2024 Lok Sabha election. We identified the presence of religiopolitical propaganda, primarily aimed at reviving and recreating Hindu nationalist history and targeting religious minorities, mainly Muslims. The major topics of the posts included ideological legacy, political landscape, party and leadership, celebrations, crime and justice, local politics and governance, politicized demographic trends, public engagements, spiritual and philosophical themes, and the misrepresented reservation issue. The interconnectedness of these issues suggests that the BJP and its allies concentrated on religious matters, from Hindu–Muslim debates to reservations for Muslims and the inauguration of Hindu temples. Data from non-political entities, such as influencers, as well as cross-platform analysis from Twitter and YouTube, can extend and enrich these insights.
      Citation: Social Science Computer Review
      PubDate: 2025-02-07T06:49:50Z
      DOI: 10.1177/08944393251319740
       
  • The Role of Physical Appearance Comparison, Self-Esteem, and Emotional
           Control in the Association Between Social Media Addiction and Masculine
           Depression

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      Authors: Maya Kagan, Uzi Ben-Shalom, Michal Mahat-Shamir; Uzi Ben-Shalom, Michal Mahat-Shamir1School of Social Work, 42732Ariel University, Ariel, Israel2Department of Sociology Anthropology, Ariel University, Kiryat Hamada, Ariel, Israel
      Abstract: Social Science Computer Review, Ahead of Print.
      Social media has become an integral part of daily life, shaping behaviors, self-perception, and emotional well-being. However, its addictive use raises concerns about its potential to aggravate psychological challenges, particularly in the context of societal expectations of masculinity. The current report presents a study exploring the pathways through which social media addiction contributes to masculine depression, specifically examining the roles of physical appearance comparison, self-esteem, and emotional control among men. By investigating these relationships, it aims to provide insights into the psychological consequences of social media addiction for men. Structured questionnaires were administered to 849 Israeli men aged 18 and older. Employing a moderated sequential mediation model with social media addiction as the independent variable, physical appearance comparison and self-esteem as mediators, and masculine depression as the dependent variable, this study also investigates emotional control as a moderator in the associations between social media addiction, physical appearance comparison, self-esteem, and masculine depression. The analysis, conducted using model 89 PROCESS v4.2 macro, reveals that conforming to the masculine norm of emotional control intensifies men’s vulnerability to distress resulting from maladaptive behaviors such as social media addiction, which can lead to masculine depression. Furthermore, addiction to social media can trigger masculine depression via psychosocial factors such as physical appearance comparison and low self-esteem, which have yet to be explored in the context of masculine depression. These findings underscore the importance of targeted interventions that address the societal pressures of masculinity and the psychological repercussions of excessive social media use among men. They also emphasize the necessity of raising awareness about these issues among both the public and therapists.
      Citation: Social Science Computer Review
      PubDate: 2025-01-30T01:18:48Z
      DOI: 10.1177/08944393251315915
       
  • Survey on Deep Learning for Misinformation Detection: Adapting to Recent
           Events, Multilingual Challenges, and Future Visions

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      Authors: Ansam Khraisat, Manisha, Lennon Chang, Jemal Abawajy; Manisha, Lennon Chang, Jemal Abawajy1School of Information Technology, 2104Deakin University, Melbourne, Australia2School of Computing Information Technology, 529993REVA University, Bengaluru, India3School of Information Technology, 2104Deakin University, Geelong, Australia
      Abstract: Social Science Computer Review, Ahead of Print.
      The proliferation of misinformation in the digital age has emerged as a pervasive and pressing challenge, threatening the integrity of information dissemination across online platforms. In response to this growing concern, this survey paper offers a comprehensive analysis of the landscape of misinformation detection methodologies. Our survey delves into the intricacies of model architectures, feature engineering, and data sources, providing insights into the strengths and limitations of each approach. Despite significant advancements in misinformation detection, this survey identifies persistent challenges. The paper accentuates the need for adaptive models that can effectively tackle rapidly evolving events, such as the COVID-19 pandemic. Language adaptability remains another substantial frontier, particularly in the context of low-resource languages like Chinese. Furthermore, it draws attention to the dearth of balanced, multilingual datasets, emphasizing their significance for robust model training and assessment. By addressing emerging challenges and offering a comprehensive view, our paper enriches the understanding of deep learning techniques in misinformation detection.
      Citation: Social Science Computer Review
      PubDate: 2025-01-24T11:56:37Z
      DOI: 10.1177/08944393251315910
       
  • “Depression is not Real; I Don’t Need Help”: Stigmatizing Depression
           on Social Media and Help Avoidance

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      Authors: Piper Liping Liu, Lianshan Zhang; Lianshan Zhang147890Shenzhen University, China212474Shanghai Jiao Tong University, China
      Abstract: Social Science Computer Review, Ahead of Print.
      Despite being influential spaces for disseminating information, social media platforms often contribute to the perpetuation of harmful stereotypes and misconceptions surrounding depression. This study investigates the relationship between exposure to stigmatizing depression on social media and help avoidance among young adults, while examining the mediating roles of depression knowledge and stigmatizing attitudes. A sample of 428 Chinese young adults aged between 18 and 35 responded to the anonymous questionnaires. Results indicate a positive association between exposure to stigmatizing information on social media and help avoidance. Furthermore, depression knowledge and stigmatizing attitudes were found to mediate this relationship, highlighting the cognitive mechanisms underlying the impact of social media on mental health attitudes and behaviors. The findings underscore the importance of addressing stigmatizing content on social media platforms and promoting accurate depression knowledge among young adults to mitigate help avoidance tendencies. Implications and limitations are discussed.
      Citation: Social Science Computer Review
      PubDate: 2025-01-22T01:17:37Z
      DOI: 10.1177/08944393251315918
       
  • Machine Learning and Political Events: Application of a Semi-supervised
           Approach to Produce a Dataset on Presidential Cabinets

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      Authors: Bastián González-Bustamante; Leiden University, The Netherlands2School of Public Administration, Universidad Diego Portales, Chile
      Abstract: Social Science Computer Review, Ahead of Print.
      This paper describes the creation of a novel dataset on ministerial turnover and resignation calls in 12 presidential cabinets in Latin America from the mid-1970s to the early 2020s. The indicators on resignation calls and reallocations of cabinet members are entirely novel. Both constitute a relevant empirical contribution not only to the study of political dynamics in presidential systems and cabinet politics but also to public opinion and public policy topics. We focus on the creation of the dataset using optical recognition algorithms on press report archives together with machine learning models. The models permitted the training of ensemble semi-supervised classifiers over a period of almost 50 years. Subsequently, we provide a number of measurement validity checks to cross-validate the dataset by comparing it with similar existing data and an exploratory analysis.
      Citation: Social Science Computer Review
      PubDate: 2025-01-20T08:11:49Z
      DOI: 10.1177/08944393251315917
       
  • A Dual-Role Trust Model for Social Media Influencers: The Paradox of
           Perceived Friendship

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      Authors: Shang Chen, Xuefei Xu, Qingfei Min, Lin Liu; Xuefei Xu, Qingfei Min, Lin Liu156650Qufu Normal University, China212399Dalian University of Technology, China
      Abstract: Social Science Computer Review, Ahead of Print.
      As companies come to appreciate social media’s economic advantages, it has transformed into a dichotomous social-commercial landscape. In this specific situation, followers evaluate social media influencers that play both the friend and marketer roles in the decision-making process. This study creates a dual-role trust model based on the role theory to investigate trust processes across different roles. More importantly, this study goes deeper into examining the potential paradoxical positive and negative moderating roles of perceived friendship in the dual-role trust mechanism. The structural equation model approach is first conducted to test our hypotheses using a survey of 465 TikTok respondents. Next, the study model’s hypotheses are further tested using a post hoc analysis to see if they change based on followers’ regulatory focus. The findings support perceived friendship’s complementary and replacement functions in a dual role-based trust mechanism, as well as the transfer of trust between dual roles. There is a discussion of the implications for theory and practice.
      Citation: Social Science Computer Review
      PubDate: 2024-12-24T01:18:05Z
      DOI: 10.1177/08944393241311586
       
  • Do Narcissistic People Exhibit More Authentic Self-Disclosure to
           Generative AI' The Roles of Short-Form Video Addiction, Loneliness, and
           Usage Intention

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      Authors: Pengcheng Wang, Yongjie Yue, Mingkun Ouyang, Lipeng Yin, Yulong Yin, Biao Li; Yongjie Yue, Mingkun Ouyang, Lipeng Yin, Yulong Yin, Biao Li1School of Media Communication, 12442Tsinghua University, Beijing, China3School of Education Science, 47874Guangxi Minzu University, Nanning, China4Department of Psychology, 25809The University of Hong Kong, Pokfulam, Hong Kong5School of Psychology, 12435Northwest Normal University, Lanzhou, China6Department of Communication, 12471Renmin University of China, Beijing, China
      Abstract: Social Science Computer Review, Ahead of Print.
      The widespread application of generative artificial intelligence (GenAI) technology has innovated human–AI interactions, making authentic self-disclosure to machines an emerging trend. Drawing on the cognitive–affective personality system theory, this study examined how narcissism, short-form video addiction, and loneliness contribute to the authentic self-disclosure to GenAI, as well as the moderating role of intention to use GenAI. The mediation and moderation analyses of data were collected from 524 college students (357 females, Mage = 21.25) in China. The results indicated that narcissism was positively associated with authentic self-disclosure to GenAI, and short-form video addiction and loneliness sequentially mediated this connection. Intention to use GenAI enhanced the positive association between loneliness and authentic self-disclosure to GenAI. The significance and limitations of the findings were discussed.
      Citation: Social Science Computer Review
      PubDate: 2024-12-18T02:45:39Z
      DOI: 10.1177/08944393241308511
       
  • Testing Motivation-Based vs. Social Exchange Communication Strategies in
           Email Survey Recruitment

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      Authors: Jason Kosakow, Pierce Greenberg; Pierce Greenberg198024The Federal Reserve Bank of Richmond, USA22545Clemson University, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      Despite well-documented challenges, researchers across the social sciences continue to rely on email to recruit research participants. However, few studies examine how different communication strategies impact email open and conversion rates, especially among surveys of establishments. Our paper aims to fill that gap by examining whether motivation-based appeals—which we develop from respondents’ reasons for participating—outperform a communication approach based on social exchange theory. Our study identified the top three motivations why current panel members participate in the Federal Reserve Bank of Richmond Business Survey: (1) access to data and other benefits, (2) the ability to influence economic policy, and (3) to help make their communities better. Then, we crafted email subject lines and messages to match those three motivations and a version based on tenets of social exchange theory. Our results find that the social exchange version outperforms the motivation-based appeals in both email open and conversion rates—with a stronger influence on conversion rates. We discuss the implications of these results for how social science researchers communicate with potential research participants by email.
      Citation: Social Science Computer Review
      PubDate: 2024-12-18T02:28:05Z
      DOI: 10.1177/08944393241308509
       
  • Asking for Traces: A Vignette Study on Acceptability Norms and Personal
           Willingness to Donate Digital Trace Data

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      Authors: Henning Silber, Johannes Breuer, Barbara Felderer, Frederic Gerdon, Patrick Stammann, Jessica Daikeler, Florian Keusch, Bernd Weiß; Johannes Breuer, Barbara Felderer, Frederic Gerdon, Patrick Stammann, Jessica Daikeler, Florian Keusch, Bernd Weiß11259University of Michigan, Ann Arbor, USA239020GESIS – Leibniz Institute for the Social Sciences, Cologne Mannheim, Germany326573University of Mannheim, Mannheim, Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      Digital trace data are increasingly used in the social sciences. Given the risks associated with data access via application programming interfaces (APIs) as well as ethical discussions around the use of such data, data donations have been proposed as a methodologically reliable and ethically sound way of collecting digital trace data. While data donations have many advantages, study participants may be reluctant to share their data, for example, due to privacy concerns. To assess which factors in a data donation request are relevant for participants’ acceptance and decisions, we conducted a vignette experiment investigating the general acceptability and personal willingness to donate various data types (i.e., data from GPS, web browsing, LinkedIn/Xing, Facebook, and TikTok) for research purposes. The preregistered study was implemented in the probability-based German Internet Panel (GIP) and gathered responses from n = 3821 participants. Results show that people rate the general acceptability of data donation requests higher than their own willingness to donate data. Regarding the different data types, respondents indicated that they would be more willing to donate their LinkedIn/Xing, TikTok, and GPS data compared to web browsing and Facebook data. In contrast, information about whether the donated data would be shared with other researchers and data security did not affect the responses to the respective donation scenarios. Based on these results, we discuss implications for studies employing data donations.
      Citation: Social Science Computer Review
      PubDate: 2024-12-09T10:13:51Z
      DOI: 10.1177/08944393241305776
       
  • People, Platforms, and Places: The Conditional Effects of Psychological
           Distances on Livestream Viewership

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      Authors: Zhang Hao Goh, Minzheng Hou, Edson C. Tandoc; Minzheng Hou, Edson C. Tandoc1Wee Kim Wee School of Communication Information, Nanyang Technological University, Singapore.2Institute of Policy Studies, National University of Singapore, Singapore
      Abstract: Social Science Computer Review, Ahead of Print.
      Reducing the social distance between livestreamers and viewers is known to enhance viewership as well as generate desirable psychosocial and economic outcomes. However, apart from the social dimension, scholars have not explored other distance dimensions that may induce the same benefits. Leveraging the construal level theory, the current study explicates the concept of distance in the form of three different perceived psychological distance dimensions (i.e., perceived response latency, heterophily, and spatialization) to examine how it can induce viewership in the context of livestreaming. A survey conducted in Singapore (n = 401) found that the effect of perceived heterophily affecting viewership in livestream was moderated by viewers’ chronic construal tendencies, demonstrating a construal fit. The current study provides insights into how chronic differences in viewers’ construal can influence viewership.
      Citation: Social Science Computer Review
      PubDate: 2024-12-02T05:34:27Z
      DOI: 10.1177/08944393241305780
       
  • Has ChatGPT Disrupted the Education Sector in the U.S.'

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      Authors: Erik Haugom, Štefan Lyócsa, Martina Halousková; Štefan Lyócsa, Martina Halousková1639938Inland Norway University of Applied Sciences, Lillehammer, Norway2Department of Finance, 69730Masaryk University Brno, Czech Republic3Institute of Economic Research Slovak Academy of Sciences, Bratislava, Slovak Republic4Faculty of Management Business, University of Prešov, Prešov, Slovak Republic
      Abstract: Social Science Computer Review, Ahead of Print.
      The introduction of ChatGPT and other tools based on artificial intelligence (AI) has the potential to revolutionize the field of education. We study how the public release of ChatGPT and the increased attention on this new large language model from OpenAI are associated with the expected returns of publicly traded firms that operate in the education sector. We also perform separate subgroup analyses for the traditional education sector and the so-called education technology sector. Using linear and threshold CAPM-GARCH models, we find that after the public release of ChatGPT, both the education sector as a whole and the education technology sector have underperformed benchmarks. Our results show that increased attention leads to lower next-day returns in the education sector as a whole and the education technology sector in particular. Additionally, during periods of higher attention, expected returns tend to decline in these two sectors. We also show that the introduction of ChatGPT or the increased interest in this AI tool in the population does not affect the traditional education sector. The introduction of ChatGPT thus has a heterogeneous effect across the various education sectors we examine, with the education technology sector receiving most of the disruption.
      Citation: Social Science Computer Review
      PubDate: 2024-11-22T10:38:37Z
      DOI: 10.1177/08944393241301330
       
  • Cyberbalkanization Without Monotonic Polarization: Temporal Dynamics and
           User Heterogeneity in Online Debates on Traditional Chinese Medicine

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      Authors: Jiao Shen, Deya Xu; Deya Xu112474Shanghai Jiao Tong University, China212655East China Normal University, China
      Abstract: Social Science Computer Review, Ahead of Print.
      This paper examines the polarization and cyberbalkanization of opinions within a Traditional Chinese Medicine (TCM) community on Zhihu, a prominent Chinese social question-answering platform. The study explores the impact of online interactions on opinion dynamics during the COVID-19 pandemic. Utilizing a hybrid content and network analysis approach, the research identifies distinct subcommunities within the TCM debate and analyzes the influence of opinion leaders and user participation. The study finds a high level of cyberbalkanization, with users forming segregated communities based on their views towards TCM. However, contrary to expectations, the overall polarization levels do not increase monotonically over time, despite fluctuations during peak discussion periods. Further analysis reveals that the influx of moderate views from low-engagement users during heated debates counterbalances the extreme rhetoric of high-engagement partisan users. The longitudinal patterns suggest a potential convergence of views between the entrenched and peripheral users over time. These findings highlight the nuanced interplay between cyberbalkanization and polarization within online communities, challenging assumptions of an inevitable polarizing effect. The study underscores the importance of considering user heterogeneity and temporal dynamics when examining opinion polarization in digital spaces, offering insights into the complex forces shaping public discourse in the contemporary media environment.
      Citation: Social Science Computer Review
      PubDate: 2024-11-15T10:37:53Z
      DOI: 10.1177/08944393241301043
       
  • The Moderating Role of Self-Esteem in the Relationship Between Social
           Media Use and Life Satisfaction Among Older Adults

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      Authors: Yesolran Kim; Public Relations, School of Media·Advertising, 34967Kookmin University, South Korea
      Abstract: Social Science Computer Review, Ahead of Print.
      This study examines the relationship between social media use and life satisfaction among older adults, with a focus on the moderating role of self-esteem. Cross-sectional data from the 2021 Korea Media Panel Survey were analyzed, focusing on responses from 192 older adults aged 65 and older who had experience using social media platforms. The findings reveal that among older adults with low self-esteem, social media use is negatively associated with life satisfaction, whereas for those with high self-esteem, this association reverses and becomes positive. However, for older adults with medium self-esteem, the relationship between social media use and life satisfaction is not significant. This study contributes to the scholarly understanding of the structural relationship between social media use, self-esteem, and life satisfaction among older adults and offers insights for tailored interventions aimed at enhancing well-being in this demographic.
      Citation: Social Science Computer Review
      PubDate: 2024-11-15T10:12:13Z
      DOI: 10.1177/08944393241301045
       
  • Feminist Identity and Online Activism in Four Countries From 2019 to 2023

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      Authors: Shelley Boulianne, Katharina Heger, Nicole Houle, Delphine Brown; Katharina Heger, Nicole Houle, Delphine Brown17423University of Southampton, UK2648518Weizenbaum Institute for the Networked Society, Germany33151MacEwan University, Canada
      Abstract: Social Science Computer Review, Ahead of Print.
      The COVID-19 pandemic heightened burdens on caregivers, but also the visibility of caregiving inequalities. These grievances may activate a feminist identity which in turn leads to greater civic and political participation. During a pandemic, online forms of participation are particularly attractive as they require less effort than offline forms of participation and pose less health risks compared to collective forms of offline activism. Using survey data from four countries (Canada, France, the United States, and the United Kingdom) collected in 2019 (prior to the pandemic), 2021 (during the pandemic), and 2023 (post-pandemic), we examine the relationship between self-identifying as a feminist and signing online petitions (n = 18,362). Our multivariate analyses show that having a feminist identity is positively related to signing online petitions. We consider the differential effects of this identity on participation for men, women, non-binary people; caregivers versus non-caregivers; and respondents in different countries with varying levels of restrictions due to the pandemic. A feminist identity is more important for mobilizing caregivers than non-caregivers, whether or not the caregiver is a man or a woman. While grievance theory suggests differential effects by country and time period, we find a consistent role of feminist identity in predicting the signing of online petitions across time and across countries. These findings offer insights into how different groups in varying contexts are mobilized to participate.
      Citation: Social Science Computer Review
      PubDate: 2024-11-15T05:44:36Z
      DOI: 10.1177/08944393241301050
       
  • Improving the Quality of Individual-Level Web Tracking: Challenges of
           Existing Approaches and Introduction of a New Content and Long-Tail
           Sensitive Academic Solution

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      Authors: Silke Adam, Mykola Makhortykh, Michaela Maier, Viktor Aigenseer, Aleksandra Urman, Teresa Gil Lopez, Clara Christner, Ernesto de León, Roberto Ulloa; Mykola Makhortykh, Michaela Maier, Viktor Aigenseer, Aleksandra Urman, Teresa Gil Lopez, Clara Christner, Ernesto de León, Roberto Ulloa127210University of Bern, Switzerland2122292University of Kaiserslautern-Landau, Germany327217University of Zurich, Switzerland427210Universidad Carlos III de Madrid, Spain527210University of Konstanz/GESIS, Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      This article evaluates the quality of data collection in individual-level desktop web tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard for the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic web tracking solution, WebTrack, an open-source tracking tool maintained by a major European research institution, GESIS. The design logic, the interfaces, and the backend requirements for WebTrack are discussed, followed by a detailed examination of the strengths and weaknesses of the tool. Finally, using data from 1,185 participants, the article empirically illustrates how an improvement in data collection through WebTrack leads to innovative shifts in the use of tracking data. As WebTrack allows for collecting the content people are exposed to beyond the classical news platforms, it can greatly improve the detection of politics-related information consumption in tracking data through automated content analysis compared to traditional approaches that rely on the source-level analysis.
      Citation: Social Science Computer Review
      PubDate: 2024-10-16T12:19:10Z
      DOI: 10.1177/08944393241287793
       
  • Can AI Lie' Chabot Technologies, the Subject, and the Importance of
           Lying

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      Authors: Jack Black; UK
      Abstract: Social Science Computer Review, Ahead of Print.
      This article poses a simple question: can AI lie' In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically considering the chatbot’s ability to engage in natural language conversations and provide contextually relevant responses, it is argued that what separates the AI chatbot from anthropocentric debates, which allude to some form of conscious recognition on behalf of AI, is the importance of the lie – an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies can undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority – an authority that is easily reflected in its honest responses and frank admissions – a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’ but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie.
      Citation: Social Science Computer Review
      PubDate: 2024-10-15T06:30:23Z
      DOI: 10.1177/08944393241282602
       
  • Using Google Trends Data to Study High-Frequency Search Terms: Evidence
           for a Reliability-Frequency Continuum

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      Authors: Tobias Gummer, Anne-Sophie Oehrlein; Anne-Sophie Oehrlein139020GESIS Leibniz Institute for the Social Sciences, Germany226573University of Mannheim, Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      Google Trends (GT) data are increasingly used in the social sciences and adjacent fields. However, previous research on the quality of GT data has raised concerns regarding their reliability. In the present study, we investigated whether reliability differs between low- and high-frequency search terms. In other words, we explored the existence of a reliability-frequency continuum in GT data. Our study adds to previous research by investigating a more comprehensive set of search terms and different aspects of reliability (e.g., differences in relative search volume distributions, correctly identified maxima). For this purpose, we collected samples of GT data for ten high- and two low-frequency search terms. We obtained one real-time sample and 62 non–realtime samples per search term (30 non–realtime samples for low-frequency search terms). Data collection was restricted to search data for Germany. Our data support the existence of a reliability-frequency continuum—low-frequency search terms are subject to greater reliability issues compared to high-frequency search terms. Based on our findings, we have derived practical recommendations for the use of GT data and have outlined future research opportunities.
      Citation: Social Science Computer Review
      PubDate: 2024-10-12T03:30:36Z
      DOI: 10.1177/08944393241279421
       
  • Rethinking the Effects of Smart City Implementation on Industrial
           Structure Change

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      Authors: Mengmeng Wang, Tao Zhou; Tao Zhou1School of Management, Zhengzhou University, China2School of Management Science Real Estate, Chongqing University, China
      Abstract: Social Science Computer Review, Ahead of Print.
      Understanding the relationship between smart city implementation (SCI) and industrial structure change is necessary for improving SCI and achieving high-quality development of the urban economy. This study aims to comprehensively investigate how the two core SCI investments—information and communication technology (ICT) infrastructure and human capital—influence industrial structure. To this end, both SCI investments are measured using composite indexes calculated by the vertical and horizontal scatter degree method, and spatial panel models are applied using data from 156 smart cities in China from 2013 to 2019, classified according to their economic development level and population density. The results show that industrial structure upgrading produces positive spatial spillover effects and that the impacts of SCI investments on industrial structure change vary in different city classifications. Overall, ICT infrastructure promotes the upgrading of industrial structure, while human capital hinders industrial development due to human capital mismatch. Mechanism analysis shows that SCI investments can influence industrial structure through technological innovation and consumption upgrading. Moreover, policy implications are proposed to promote industrial structure upgrading for effective future SCI.
      Citation: Social Science Computer Review
      PubDate: 2024-10-03T04:30:01Z
      DOI: 10.1177/08944393241269414
       
  • Why People Accept Mental Health-Related Misinformation: Role of Social
           Media Metrics in Users’ Information Processing

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      Authors: Shiyi Zhang, Huiyu Zhou, Yimei Zhu; Huiyu Zhou, Yimei Zhu1School of Arts, Media Mathematical Sciences, 4488University of Leicester, UK
      Abstract: Social Science Computer Review, Ahead of Print.
      Drawing on dual-process theories, this study aims to investigate the factors associated with social media users’ acceptance of mental health-related misinformation (MHRM). We conducted a case study of Chinese microblogging Weibo on conversations that emerged following a publicised celebrity suicide of South Korean superstar Sulli. This incident sparked an extensive discussion on mental health issues as Sulli was reported to have suffered from depression prior to her death. Whilst previous studies on users’ information acceptance mainly adopted survey methods, our study employs a mixed-method approach (i.e. computational data collection method, content analysis and statistical analysis), which opens up new directions to utilise secondary social media data. We identified MHRM from the discussions on Weibo and labelled the responses to the misinformation as whether they indicate an acceptance of the MHRM. Binary logistic regression was used to examine the associations of receivers’ acceptance of MHRM with its information features (e.g. number of likes) and information sources (e.g. gender). Inconsistent with previous studies, our findings suggest that MHRM is less likely to be accepted when published by male users, underscoring the context-specific nature of heuristic cues. This study also revealed some novel findings, such as MHRM with more pictures or with more words is less likely to be accepted. A theoretical model was proposed based on the findings, which highlights the importance of heuristic cues and individuals’ pre-existing knowledge in information processing.
      Citation: Social Science Computer Review
      PubDate: 2024-09-26T02:26:12Z
      DOI: 10.1177/08944393241287791
       
  • Large Language Models Outperform Expert Coders and Supervised Classifiers
           at Annotating Political Social Media Messages

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      Authors: Petter Törnberg; 171892University of Amsterdam, Amsterdam, Netherlands2Institut de Géographie, Université de Neuchâtel, Neuchâtel, Switzerland
      Abstract: Social Science Computer Review, Ahead of Print.
      Instruction-tuned Large Language Models (LLMs) have recently emerged as a powerful new tool for text analysis. As these models are capable of zero-shot annotation based on instructions written in natural language, they obviate the need of large sets of training data—and thus bring potential paradigm-shifting implications for using text as data. While the models show substantial promise, their relative performance compared to human coders and supervised models remains poorly understood and subject to significant academic debate. This paper assesses the strengths and weaknesses of popular fine-tuned AI models compared to both conventional supervised classifiers and manual annotation by experts and crowd workers. The task used is to identify the political affiliation of politicians based on a single X/Twitter message, focusing on data from 11 different countries. The paper finds that GPT-4 achieves higher accuracy than both supervised models and human coders across all languages and country contexts. In the US context, it achieves an accuracy of 0.934 and an inter-coder reliability of 0.982. Examining the cases where the models fail, the paper finds that the LLM—unlike the supervised models—correctly annotates messages that require interpretation of implicit or unspoken references, or reasoning on the basis of contextual knowledge—capacities that have traditionally been understood to be distinctly human. The paper thus contributes to our understanding of the revolutionary implications of LLMs for text analysis within the social sciences.
      Citation: Social Science Computer Review
      PubDate: 2024-09-23T05:12:21Z
      DOI: 10.1177/08944393241286471
       
  • Status Spill-Over in Cryptomarket for Illegal Goods

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      Authors: Filippo Andrei, Giuseppe Alessandro Veltri; Giuseppe Alessandro Veltri119034University of Trento, Italy
      Abstract: Social Science Computer Review, Ahead of Print.
      Information technologies have transformed many aspects of social life, including how illegal goods are exchanged. Illegal online markets are now flourishing on various channels: the surface web (all websites accessible through a standard browser), the dark web (an encrypted internet network only accessible via anonymous browsers), and encrypted messaging applications installed on smartphones. These marketplaces take many forms, including simple web shops, chat rooms, forums, social media marketplaces, and platforms. This study focuses on the largest known darknet platform to date: AlphaBay. This cryptomarket operated from December 2014 until July 2017, when an international police operation shut it down. The dataset contains 6033 vendor profiles collected in January 2017. Using three generalized additive models (GAMs), we show that seller status positively affects sales, revenue, and sales through finalized early payment. Once sellers gain status on the platforms, they make more sales without a semi-institutionalized form of payment (e.g. escrow). On the other hand, buyers relying on status metrics as cognitive shortcuts tend to choose vendors even if they do not offer payment protection tools.
      Citation: Social Science Computer Review
      PubDate: 2024-09-21T10:51:12Z
      DOI: 10.1177/08944393241286339
       
  • Network Issue Agenda Setting on Facebook: Exploring the Interplay Between
           Polarized Campaigns and Party Supporters

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      Authors: Zahedur Rahman Arman; Media, Performance, 3412Framingham State University, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      This study undertook an analysis of network agenda setting during the 2020 U.S. Presidential campaign, focusing on the interactions between the campaigns and their respective supporters within the context of a polarized social media environment. By employing social network analysis techniques to examine issue agendas, the study revealed a relatively weak correlation between the agendas of the campaigns and their affiliated supporters on Facebook. Conversely, it found a notable association between entities sharing the same ideological orientation—party supporters displayed a higher degree of engagement with their own party’s campaign, and vice versa. The implications of these findings, from a theoretical, methodological, and practical standpoint, have been thoroughly discussed.
      Citation: Social Science Computer Review
      PubDate: 2024-09-20T08:58:53Z
      DOI: 10.1177/08944393241286149
       
  • Unveiling the Veiled Threat: The Impact of Bots on COVID-19 Health
           Communication

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      Authors: Ali Unlu, Sophie Truong, Nitin Sawhney, Tuukka Tammi; Sophie Truong, Nitin Sawhney, Tuukka Tammi13837Finnish Institute for Health Welfare (THL), Finland2174277Aalto University, Finland32358University of Virginia, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was implemented to distinguish COVID-19 specific bot accounts from regular bots. Network analysis illuminated the communication patterns of COVID-19 bots within retweet and mention networks. The findings reveal that these bots exhibit distinct characteristics and tactics that enable them to influence public discourse, particularly showing an increased activity in COVID-19-related conversations. Topic modeling analysis uncovers that COVID-19 bots predominantly focused on themes such as safety, political/conspiracy theories, and personal choice. The study highlights the critical need to develop effective strategies for detecting and countering bot influence. Essential actions include using clear and concise language in health communications, establishing strategic partnerships during crises, and ensuring the authenticity of user accounts on digital platforms. The findings underscore the pivotal role of bots in propagating misinformation related to COVID-19 and vaccines, highlighting the necessity of identifying and mitigating bot activities for effective intervention.
      Citation: Social Science Computer Review
      PubDate: 2024-09-09T05:19:45Z
      DOI: 10.1177/08944393241275641
       
  • To Follow or Not to Follow: Estimating Political Opinion From Twitter Data
           Using a Network-Based Machine Learning Approach

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      Authors: Nils Brandenstein, Christian Montag, Cornelia Sindermann; Christian Montag, Cornelia Sindermann19144Heidelberg University, Germany29189Ulm University, Germany39149University of Stuttgart, Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studies using SMD to extract individuals’ political opinions are limited and differ greatly in their methodological approaches and levels of success. Recent studies highlight the benefits of analyzing individuals’ social media network structure to estimate political opinions. Nevertheless, current methodologies exhibit limitations, including the use of simplistic linear models and a predominant focus on samples from the United States. Addressing these issues, we employ an unsupervised Variational Autoencoder (VAE) machine learning model to extract individual opinion estimates from SMD of N = 276 008 German Twitter (now called ’X’) users, compare its performance to a linear model and validate model estimates on self-reported opinion measures. Our findings suggest that the VAE captures Twitter users’ network structure more precisely, leading to higher accuracy in following decision predictions and associations with self-reported political ideology and voting intentions. Our study emphasizes the need for advanced analytical approaches capable to capture complex relationships in social media networks when studying political opinion, at least in non-US contexts.
      Citation: Social Science Computer Review
      PubDate: 2024-09-03T02:24:27Z
      DOI: 10.1177/08944393241279418
       
  • Does the Media’s Partisanship Influence News Coverage on Artificial
           Intelligence Issues' Media Coverage Analysis on Artificial Intelligence
           Issues

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      Authors: Mikyung Chang; South Korea
      Abstract: Social Science Computer Review, Ahead of Print.
      This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.
      Citation: Social Science Computer Review
      PubDate: 2024-09-02T01:27:31Z
      DOI: 10.1177/08944393241268526
       
  • TikTok Brain: An Investigation of Short-Form Video Use, Self-Control, and
           Phubbing

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      Authors: Meredith E. David, James A. Roberts; James A. Roberts114643Baylor University, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      Phubbing (phone snubbing) has become the norm in (im)polite society. A vast majority of US adults report using their phones during a recent social interaction. Using one’s phone in the presence of others has been shown to have a negative impact on relationships among co-workers, friends, family, and romantic partners. Recent research suggests viewing short-form videos (SFVs) (e.g., TikTok) is more addictive/immersive than traditional social media (e.g., Facebook) leading to a greater likelihood of phubbing others. Across two studies, the present research investigates the relationship between SFV viewing and phubbing and the possible mediating effect of self-control. We also test whether TikTok has a stronger relationship with phubbing than Instagram Reels and YouTube Shorts, two popular SFV purveyors. Study 1 (282 college students) finds that viewing TikTok videos is positively associated with phubbing others and this relationship is mediated by self-control. Interestingly, Study 1 also finds that this relationship does not hold for Instagram Reels and YouTube shorts. Using two different measures of self-control, Study 2 (198 adults) provides additional support for the mediating effect of self-control on the SFV viewing—phubbing relationship. Again, the model is only supported for TikTok SFV viewing, not Instagram or YouTube. In sum, the viewing of carefully curated short TikTok videos, often 30–60 seconds in length, undermines self-control which is associated with increased phubbing behavior. Implications of the present study’s findings expand far beyond phubbing. Self-control plays a central role in nearly all human decision making and behavior. Suggestions for future research are offered.
      Citation: Social Science Computer Review
      PubDate: 2024-08-29T09:24:51Z
      DOI: 10.1177/08944393241279422
       
  • CORA: An Open-Source Software Tool for Combinational Regularity Analysis

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      Authors: Lusine Mkrtchyan, Alrik Thiem, Zuzana Sebechlebská; Alrik Thiem, Zuzana Sebechlebská130731University of Lucerne, Switzerland
      Abstract: Social Science Computer Review, Ahead of Print.
      Modern Configurational Comparative Methods (CCMs), such as Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), have gained in popularity among social scientists over the last thirty years. A new CCM called Combinational Regularity Analysis (CORA) has recently joined this family of methods. In this article, we provide a software tutorial for the open-source package CORA, which implements the eponymous method. In particular, we demonstrate how to use CORA to discover shared causes of complex effects and how to interpret corresponding solutions correctly, how to mine configurational data to identify minimum-size tuples of solution-generating inputs, and how to visualize solutions by means of logic diagrams.
      Citation: Social Science Computer Review
      PubDate: 2024-08-29T02:36:43Z
      DOI: 10.1177/08944393241275640
       
  • How Does Internet Use Affect Citizen Political Participation' The
           Mediating Role of Social Networks

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      Authors: Yunfan Zhang, Yalin Qin, Jiaojiao Liu, Weidong Li; Yalin Qin, Jiaojiao Liu, Weidong Li1School of Journalism Technology, China
      Abstract: Social Science Computer Review, Ahead of Print.
      This study investigates the complex relationship between internet use and citizen political participation, emphasizing the mediating role of social networks. Using cross-sectional data from the 2021 Chinese Social Survey (CSS), we classify political participation into government-led and citizen-led activities and internet use into informational, entertainment-oriented, and service-oriented types. Overall, our findings reveal a positive correlation between internet use and both citizen-led participation and government-led participation (excluding voting), with a stronger association observed in the former. From a categorical perspective, informational and service-oriented internet use significantly influences all types of political participation, while entertainment-oriented use shows no significant correlation. Additionally, social networks mediate the relationship between internet use and political participation. Service-oriented internet use is more dependent on social networks to connect with political participation compared to information-oriented internet use. This study contributes to the ongoing debate on the impact of digital media on civic engagement by providing nuanced insights into how specific internet activities and social networks shape political participation in China.
      Citation: Social Science Computer Review
      PubDate: 2024-08-28T01:22:58Z
      DOI: 10.1177/08944393241277555
       
  • Remember, You Can Complete This Survey Online! Web Survey Links and QR
           Codes in a Mixed-Mode Web and Mail General Population Survey

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      Authors: Kristen Olson, Amanda Ganshert; Amanda Ganshert114719University of Nebraska-Lincoln, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      Recruitment materials for concurrent mixed-mode self-administered web and mail surveys must communicate information about multiple modes simultaneously. Providing the link to the web survey on the cover of the paper questionnaire or including a QR code to access the web survey may increase the visibility of the web mode and thus increase the proportion of people who participate via the web, but whether including either piece of information does so has received surprisingly little empirical attention. In this paper, we examine the results of experiments embedded in two general population probability-based concurrent mixed-mode surveys of Nebraska adults. First, in the Labor Availability Survey of the Greater Omaha Area, respondents were randomly assigned to receive the web link and login information on the cover or the paper questionnaire without this information (all had web information in the cover letter). We then replicated and extended this experiment in the Labor Availability Survey of Northeast Nebraska. The questionnaire cover experiment was fully crossed with the presence or absence of a QR code to access the web survey. Neither of these design features affected response rates or speed of response, but the link on the questionnaire significantly increased the proportion of respondents who participated by web and the QR code significantly increased the proportion of respondents who participated by smartphone. Sample composition was largely unaffected on most characteristics, although the respondent pool was less similar to the population on education when the link was on the questionnaire. About 20% of respondents used a smartphone when typing in a survey link, but virtually all respondents used a smartphone when scanning the QR code. Survey researchers can include a link on the cover of the questionnaire to increase web participation rates in mixed-mode surveys. QR codes can be used when smartphone participation is desired.
      Citation: Social Science Computer Review
      PubDate: 2024-08-24T03:59:05Z
      DOI: 10.1177/08944393241277553
       
  • Understanding Narratives of Uncertainty in Fertility Intentions of Dutch
           Women: A Neural Topic Modeling Approach

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      Authors: Xiao Xu, Anne Gauthier, Gert Stulp, Antal van den Bosch; Anne Gauthier, Gert Stulp, Antal van den Bosch12865Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW), the Netherlands2403520University of Groningen, the Netherlands3Inter-University Center for Social Science Theory Methodology (ICS), the Netherlands48125Utrecht University, the Netherlands
      Abstract: Social Science Computer Review, Ahead of Print.
      Uncertainty in fertility intentions is a major obstacle to understanding contemporary trends in fertility decision-making and its outcomes. Quantifying this uncertainty by structural factors such as income, ethnicity, and housing conditions is recognized as insufficient. A recently proposed framework on subjective narratives has opened up a new way to gauge factors behind fertility decision-making and uncertainty. Through surveys, such narratives can be elicited with open-ended questions (OEQs). However, analyzing answers to OEQs typically involves extensive human coding, imposing constraints on sample size. Natural Language Processing (NLP) techniques assist researchers in grasping aspects of the underlying reasoning behind responses with much less human effort. In this study, using automatic neural topic modeling methods, we identify and interpret topics and themes underlying the narratives on fertility intention uncertainty of women in the Netherlands. We used Contextualized Topic Models (CTMs), a neural topic model using pre-trained representations of Dutch language, to conduct our analyses. Our results show that nine topics dominate the narratives about fertility planning, with age and health-related issues as the most prominent ones. In addition, we found that uncertainty in fertility intentions is not homogeneous, as women who feel uncertain due to real-life constraints and those who have no fertility plans at all put their stress on vastly different narratives.
      Citation: Social Science Computer Review
      PubDate: 2024-08-24T01:42:39Z
      DOI: 10.1177/08944393241269406
       
  • Video Game Feedback Learning and Aggressive or Prosocial Effects

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      Authors: Boyu Qiu, Wei Zhang; Wei Zhang1School of Health Management, 26468Guangzhou Medical University, China2School of Psychology, 12451South China Normal University, China
      Abstract: Social Science Computer Review, Ahead of Print.
      There is a close connection between video games and social life, and researchers are interested in whether and how video games shape aggression and prosocial behaviors. However, there are great inconsistencies across studies on this topic. These mixed results may be due in part to a focus on learning models that were relevant in research on traditional media like television but are less useful in research on video games. Unlike other media, video games are characterized by frequent game-player interactions and immediate feedback, and there is evidence that in-game rewards and punishments can shape aggressive or prosocial behavior inside and outside the game. We argue that reinforcement learning may help us to understand the effects of video games on aggressive and prosocial behaviors, and propose a conceptual model based on this argument.
      Citation: Social Science Computer Review
      PubDate: 2024-08-23T02:03:00Z
      DOI: 10.1177/08944393241277556
       
  • Agents of Discord: Modeling the Impact of Political Bots on Opinion
           Polarization in Social Networks

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      Authors: Hsiu-Chi Lu, Hsuan-wei Lee; Hsuan-wei Lee134913National Chengchi University, Taiwan2Academia Sinica, Taiwan
      Abstract: Social Science Computer Review, Ahead of Print.
      The pervasive presence and influence of political bots have become the subject of extensive research in recent years. Studies have revealed that a significant percentage of active accounts are bots, contributing to the polarization of public sentiment online. This study employs an agent-based model in conducting computer simulations of complex social networks, to elucidate how bots, representing diverse ideological perspectives, exacerbate societal divisions. To investigate the dynamics of opinion diffusion and shed light on the phenomenon of polarization caused by the activities of political bots, we introduced bots into a bounded-confidence opinion dynamic model for different social networks, whereby the effects of bots on other agents were studied to provide a comprehensive understanding of their influence on opinion dynamics. The simulations showed that the symmetrical deployment of bots on both sides of the opinion spectrum intensifies polarization. These effects were observed within specific tolerance and homophily ranges, with low and high user tolerances slowing down polarization. Moreover, the average path length of the network and the centrality of the bots had a significant impact on the result. Finally, polarization tends to be lower when humans exhibit reduced confidence in bots. This research not only offers valuable insights into the implications of bot activities on the polarization of public opinion and current state of digital society but also provides suggestions to mitigate bot-driven polarization.
      Citation: Social Science Computer Review
      PubDate: 2024-08-16T11:04:49Z
      DOI: 10.1177/08944393241270382
       
  • Using Twitter to Detect Polling Place Issue Reports on U.S. Election Days

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      Authors: Prathm Juneja, Luciano Floridi; Luciano Floridi16396University of Oxford, UK29296University of Bologna, Italy35755Yale University, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      In this article, we analyze whether Twitter can be used to detect relative reports of issues at polling places. We use 20,322 tweets geolocated to U.S. states that match a series of keywords on the 2010, 2012, 2014, 2016, and 2018 general election days. We fine-tune BERTweet, a pre-trained language model, using a training set of 6,365 tweets labeled as issues or non-issues. We develop a model with an accuracy of 96.9% and a recall of 72.2%, and another model with an accuracy of 90.5% and a recall of 93.5%, far exceeding the performance of baseline models. Based on these results, we argue that these BERTweet-based models are promising methods for detecting reports of polling place issues on U.S. election days. We suggest that outputs from these models can be used to supplement existing voter protection efforts and to research the impact of policies, demographics, and other variables on voting access.
      Citation: Social Science Computer Review
      PubDate: 2024-08-10T12:00:10Z
      DOI: 10.1177/08944393241269420
       
  • Airbnb on TikTok: Brand Perception Through User Engagement and Sentiment
           Trends

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      Authors: Julia Marti-Ochoa, Eva Martin-Fuentes, Berta Ferrer-Rosell; Eva Martin-Fuentes, Berta Ferrer-Rosell1R16739University of Lleida, Spain
      Abstract: Social Science Computer Review, Ahead of Print.
      This study delves into Airbnb’s brand presence on TikTok by analyzing textual content in posts, and human audio in videos. This approach aims to decipher the brand narrative and gauge user engagement. In the dynamic realm of social media marketing, TikTok has emerged as a key platform in shaping brand perception. This research specifically concentrates on Airbnb’s content, distinguishing between official narratives and user-generated content (UGC). Notably, themes of “Travel” dominate official posts, contrasting with “Real Estate” and “Business” in UGC. The methodology employed involves advanced data collection techniques, including web scraping for textual data and artificial intelligence for transcribing human audio to text. The findings reveal that UGC commands greater engagement and volume compared to Airbnb’s own brand content, underscoring the increasing significance of user involvement in brand storytelling. An analysis of the study results is conducted using linguistic natural processing (LNP) for the sentiment base, and the vector space model for emotion analysis. Sentiment analysis reveals a predominance of the emotion “happiness” and a significant presence of “surprise” in the posts, both of which are critical for audience engagement. Moreover, the study indicates a high approval rate for Airbnb-related content, reflecting a positive reception of the brand. Additionally, the research observes that influencers, particularly nano influencers, have higher engagement rates, indicating that their authenticity and relatability appeal especially to Generation Z audiences. This study not only sheds light on the intricate relationship between brand narrative, user engagement, and sentiment on TikTok but also offers valuable insights into effective brand image construction and propagation in the digital era, highlighting the importance of diverse emotions in enhancing audience engagement.
      Citation: Social Science Computer Review
      PubDate: 2024-08-08T11:47:06Z
      DOI: 10.1177/08944393241260242
       
  • Gender Gap in All Academic Fields Over Time

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      Authors: Dariusz Jemielniak, Maciej Wilamowski; Maciej Wilamowski149777Kozminski University, Poland2Harvard University, USA349605University of Warsaw, Poland
      Abstract: Social Science Computer Review, Ahead of Print.
      Academic publishing gender gap has been surprisingly under covered across all disciplines and over a longer timeframe. Our study fills this gap, by analyzing how the proportions of women authors change in academic publications over 20 years in all fields from 31,219 journals from 2001 to 2021. Our results indicate that the ratio of female to male authors keeps increasing steadily across disciplines. The increases are field-neutral—in other words, they are not bigger, for example, in science, technology, engineering, and mathematics, in spite of multiple initiatives focusing specifically on STEM. The increases are also decelerating in time, which could suggest that the equilibrium of female to male authors may be plateauing. Finally, although the within-field gender gap is decreasing, it actually widened between fields. Thus, our results have major consequences for science policy in the area of the gender gap.
      Citation: Social Science Computer Review
      PubDate: 2024-08-08T04:02:00Z
      DOI: 10.1177/08944393241270633
       
  • Using OpenStreetMap, Census, and Survey Data to Predict Interethnic Group
           Relations in Belgium: A Machine Learning Approach

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      Authors: Daria Dementeva, Cecil Meeusen, Bart Meuleman; Cecil Meeusen, Bart Meuleman126657Centre for Sociological Research, KU Leuven, Belgium
      Abstract: Social Science Computer Review, Ahead of Print.
      Neighborhoods are important contexts in shaping interethnic group relationships and sites in which these may materialize through everyday routines in shared local spaces. In this paper, we approach neighborhoods as a small-scale set of spaces of encounter, defined as local public or semi-public spaces, where residents of different ethnic backgrounds may meet. Relying on the classical contact and group threat theories, the main assumption is that local spaces of encounter are facets of an intergroup neighborhood context and may shape intergroup relations, defined as perceived ethnic threat and intergroup friendship. Drawing on the georeferenced survey data from the Belgian National Election Study 2020 enriched with spatial features from OpenStreetMap, an innovative big geospatial data source, and census-based neighborhood characteristics, the study employs machine learning algorithms to investigate whether, which, and how neighborhood spaces of encounter can predict perceived ethnic threat and intergroup friendship, while also taking into account traditional local ethnic, socioeconomic, and individual indicators. By using OpenStreetMap to measure spaces of encounter as a novel neighborhood indicator, we develop a fine-grained typology of local spaces that is rooted in urban and intergroup relations research. The results show that for predicting intergroup friendship, the important spaces were educational, functional, public open, and user-selecting spaces, while for predicting threat functional, third, retail, and other spaces stood out prediction-wise. The results also revealed the predictive importance of individual characteristics for intergroup relations, while neighborhood characteristics were not so important, both in absolute and relative terms. We conclude by reflecting on what local spaces might matter and discuss the combination of OpenStreetMap and intergroup relations as a proof of concept and prospects for future research.
      Citation: Social Science Computer Review
      PubDate: 2024-08-08T02:58:26Z
      DOI: 10.1177/08944393241269098
       
  • Sexism and Media Communication. An Application to the Italian Case

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      Authors: Elia A. G. Arfini, Luigi Curini, Fabiana G. Giannuzzi; Luigi Curini, Fabiana G. Giannuzzi19304University of Milan, Italy
      Abstract: Social Science Computer Review, Ahead of Print.
      Acknowledging the importance of focusing on media’s communication for studying linguistic sexism, we propose a new method to analyze a corpus of texts via a machine learning approach built around an original training-set. We seek to establish a framework of the current use of talking about women in newspapers that expands beyond merely the objective forms of discrimination by also measuring the degree to which it implicitly conveys sexist messages through combination of words, expressions, and lexical aspects of language. As an illustrative example, we then apply such an approach to around 15,000 Italian newspapers’ headlines to investigate the impact of newspapers’ political orientations on the linguistic choices made by journalists in writing articles’ headlines.
      Citation: Social Science Computer Review
      PubDate: 2024-08-06T07:32:11Z
      DOI: 10.1177/08944393241269415
       
  • Journalists’ Ethical Responsibility: Tackling Hate Speech Against Women
           Politicians in Social Media Through Natural Language Processing Techniques
           

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      Authors: Maria Iranzo-Cabrera, Maria Jose Castro-Bleda, Iris Simón-Astudillo, Lluís-F. Hurtado; Maria Jose Castro-Bleda, Iris Simón-Astudillo, Lluís-F. Hurtado116781Universitat de València, València, Spain216774Universitat Politècnica de València, València, Spain316782Universidad de Valladolid, Valladolid, Spain
      Abstract: Social Science Computer Review, Ahead of Print.
      Social media has led to a redefinition of the journalist’s role. Specifically on Twitter, these professionals assume an influential position and their discourse is dominated by personal opinions. Taking into consideration that this platform has proven to be a breeding ground for polarization, digital harassment and hate speech, notably against women politicians, this research aims to analyze journalists’ involvement in this complex scenario. The investigation aims to determine whether, immersed in online and gender defamation campaigns, journalists enhance the quality of public debate or, on the contrary, they reinforce the visibility of this hostile content. To this end, we examined a sample of 63,926 tweets published from 23 to 25 November 2022 related to a campaign of political violence against the Spanish Minister of Equality using Natural Language Processing tools and qualitative content analysis. Results show that during those three days, at least half of the tweets contained hate speech and improper language. In this climate of hostility, journalists participating in the debate not only have an ability to attract likes and retweets but also exhibit polarization and use hate speech. Each ideological position—for and against the Minister—is also reflected in their own uncivil strategies. Under the umbrella of free speech and regardless of argumentative discourses, those journalists who lean towards ideological progressivism tend to insult their opponents, and those on the political right use divisive constructions, stereotyping and irony as attack techniques.
      Citation: Social Science Computer Review
      PubDate: 2024-08-05T08:49:32Z
      DOI: 10.1177/08944393241269417
       
  • The Spillover Effect of Internet usage on Job Satisfaction in the Digital
           Era: Evidence From Chinese Individual Survey Data

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      Authors: Qinglong Shao; Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      Digitalization has fundamentally reshaped work modes and changed employees’ attitudes toward their job, a trend that was markedly accelerated by the COVID-19 pandemic. This study sheds light on how digitalization-induced work changed levels of job satisfaction at the height of the pandemic by jointly exploring the mediating mechanisms and contextual factors affecting these relationships based on the spillover theory. Using an ordered probit model with data on 26,752 individuals from the 2020 China Family Panel Study, this study confirms the significant impact of Internet usage on job satisfaction. We found that time pressure not only mediated but also strengthened the effect of Internet usage on job satisfaction, whereas time flexibility did not play a moderating role in this relationship in China. The Sobel–Goodman mediation test confirms the robustness of the results. Moreover, the digital divide across socio-demographic groups is revealed. We find that Internet use can significantly improve urban residents’ job satisfaction, while the degree of influence is lower for rural populations. Similarly, female employees’ feelings of job satisfaction are more significantly affected than those of male employees. People who are employed in Information and Communications Technology–related occupations usually enjoy greater satisfaction from Internet use at work than those who are not. In general, by highlighting the critical role of Internet usage at work in promoting job satisfaction mediated by time pressure, this study confirms shifts in job satisfaction amid the rapid digital transformation, particularly during the pandemic.
      Citation: Social Science Computer Review
      PubDate: 2024-08-03T08:13:49Z
      DOI: 10.1177/08944393241263825
       
  • Forty Thousand Fake Twitter Profiles: A Computational Framework for the
           Visual Analysis of Social Media Propaganda

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      Authors: Noel George, Azhar Sham, Thanvi Ajith, Marco Bastos; Azhar Sham, Thanvi Ajith, Marco Bastos191103University College Dublin, School of Information Creative Industries, City, University of London, London, UK
      Abstract: Social Science Computer Review, Ahead of Print.
      Successful disinformation campaigns depend on the availability of fake social media profiles used for coordinated inauthentic behavior with networks of false accounts including bots, trolls, and sockpuppets. This study presents a scalable and unsupervised framework to identify visual elements in user profiles strategically exploited in nearly 60 influence operations, including camera angle, photo composition, gender, and race, but also more context-dependent categories like sensuality and emotion. We leverage Google’s Teachable Machine and the DeepFace Library to classify fake user accounts in the Twitter Moderation Research Consortium database, a large repository of social media accounts linked to foreign influence operations. We discuss the performance of these classifiers against manually coded data and their applicability in large-scale data analysis. The proposed framework demonstrates promising results for the identification of fake online profiles used in influence operations and by the cottage industry specialized in crafting desirable online personas.
      Citation: Social Science Computer Review
      PubDate: 2024-08-02T05:59:04Z
      DOI: 10.1177/08944393241269394
       
  • Combining Natural Language Processing and Statistical Methods to Assess
           Gender Gaps in the Mediated Personalization of Politics

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      Authors: Emanuele Brugnoli, Rosaria Simone, Marco Delmastro; Rosaria Simone, Marco Delmastro1Sony CSL – Rome, Joint Initiative CREF-SONY, Centro Ricerche Enrico Fermi, Italy2Centro Ricerche Enrico Fermi (CREF), Italy39307University of Naples Federico II, Italy419047Ca’ Foscari University of Venice, Italy
      Abstract: Social Science Computer Review, Ahead of Print.
      The media attention to the personal sphere of famous and important individuals has become a key element of the gender narrative. In this setting, we aim at assessing gender gaps in the mediated personalization of a wide range of political office holders in Italy during the period 2017–2020 by means of a combination of NLP and statistical methods. The proposed analysis hinges on the definition of a new score for each word in the corpus that adjusts the incidence rate for the under representation of women in politics. On this basis, evidence is found that political personalization in Italy is more detrimental for women than it is for men, with the persistence of entrenched stereotypes including a masculine connotation of leadership, the resulting women’s unsuitability to hold political functions, and a greater deal of focus on their attractiveness and body parts. In addition, women politicians are covered with a more negative tone than their men counterpart when personal details are reported. By distinguishing between different types of media, we also show that the observed gender differences are primarily found in online news rather than print news. This suggests that the expression of certain stereotypes may be favored when click baiting and personal targeting have a major impact.
      Citation: Social Science Computer Review
      PubDate: 2024-07-31T08:50:09Z
      DOI: 10.1177/08944393241269097
       
  • Tracking Census Online Self-Completion Using Twitter Posts

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      Authors: Mao Li, Frederick Conrad; Frederick Conrad11259University of Michigan, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      From the start of data collection for the 2020 US Census, official and celebrity users tweeted about the importance of everyone being counted in the Census and urged followers to complete the questionnaire (so-called social media campaign.) At the same time, social media posts expressing skepticism about the Census became increasingly common. This study distinguishes between different prototypical Twitter user groups and investigates their possible impact on (online) self-completion rate for the 2020 Census, according to Census Bureau data. Using a network analysis method, Community Detection, and a clustering algorithm, Latent Dirichlet Allocation (LDA), three prototypical user groups were identified: “Official Government Agency,” “Census Advocate,” and “Census Skeptic.” The prototypical Census Skeptic user was motivated by events about which an influential person had tweeted (e.g., “Republicans in Congress signal Census cannot take extra time to count”). This group became the largest one over the study period. The prototypical Census Advocate was motivated more by official tweets and was more active than the prototypical Census Skeptic. The Official Government Agency user group was the smallest of the three, but their messages—primarily promoting completion of the Census—seemed to have been amplified by Census Advocate, especially celebrities and politicians. We found that the daily size of the Census Advocate user group—but not the other two—predicted the 2020 Census online self-completion rate within five days after a tweet was posted. This finding suggests that the Census social media campaign was successful in promoting completion, apparently due to the help of Census Advocate users who encouraged people to fill out the Census and amplified official tweets. This finding demonstrates that a social media campaign can positively affect public behavior regarding an essential national project like the Decennial Census.
      Citation: Social Science Computer Review
      PubDate: 2024-07-30T11:59:49Z
      DOI: 10.1177/08944393241268461
       
  • A Transformer Model for Manifesto Classification Using Cross-Context
           Training: An Ecuadorian Case Study

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      Authors: Fernanda Barzallo, Maria Baldeon-Calisto, Margorie Pérez, Maria Emilia Moscoso, Danny Navarrete, Daniel Riofrío, Pablo Medina-Peréz, Susana K Lai-Yuen, Diego Benítez, Noel Peréz, Ricardo Flores Moyano, Mateo Fierro; Maria Baldeon-Calisto, Margorie Pérez, Maria Emilia Moscoso, Danny Navarrete, Daniel Riofrío, Pablo Medina-Peréz, Susana K Lai-Yuen, Diego Benítez, Noel Peréz, Ricardo Flores Moyano, Mateo Fierro1Departamento de Ingeniería Industrial Management Systems Engineering, University of South Florida, Tampa, FL, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      Content analysis of political manifestos is necessary to understand the policies and proposed actions of a party. However, manually labeling political texts is time-consuming and labor-intensive. Transformer networks have become essential tools for automating this task. Nevertheless, these models require extensive datasets to achieve good performance. This can be a limitation in manifesto classification, where the availability of publicly labeled datasets can be scarce. To address this challenge, in this work, we developed a Transformer network for the classification of manifestos using a cross-domain training strategy. Using the database of the Comparative Manifesto Project, we implemented a fractional factorial experimental design to determine which Spanish-written manifestos form the best training set for Ecuadorian manifesto labeling. Furthermore, we statistically analyzed which Transformer architecture and preprocessing operations improve the model accuracy. The results indicate that creating a training set with manifestos from Spain and Uruguay, along with implementing stemming and lemmatization preprocessing operations, produces the highest classification accuracy. In addition, we found that the DistilBERT and RoBERTa transformer networks perform statistically similarly and consistently well in manifesto classification. Using the cross-context training strategy, DistilBERT and RoBERTa achieve 60.05% and 57.64% accuracy, respectively, in the classification of the Ecuadorian manifesto. Finally, we investigated the effect of the composition of the training set on performance. The experiments demonstrate that training DistilBERT solely with Ecuadorian manifestos achieves the highest accuracy and F1-score. Furthermore, in the absence of the Ecuadorian dataset, competitive performance is achieved by training the model with datasets from Spain and Uruguay.
      Citation: Social Science Computer Review
      PubDate: 2024-07-24T12:05:02Z
      DOI: 10.1177/08944393241266220
       
  • Online Harassment: The Mediating and Moderating Role of Thoughtfully
           Reflective Decision-Making

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      Authors: C. Jordan Howell, Saeed Kabiri, Fangzhou Wang, Caitlyn N. Muniz, Eden Kamar, Mahmoud Sharepour, John Cochran, Seyyedeh Masoomeh (Shamila) Shadmanfaat; Saeed Kabiri, Fangzhou Wang, Caitlyn N. Muniz, Eden Kamar, Mahmoud Sharepour, John Cochran, Seyyedeh Masoomeh (Shamila) Shadmanfaat17831University of South Florida, USA2438298Academic Center for Education Culture Research, Iran312329The University of Texas at Arlington, USA412337The University of Texas at El Paso, USA51373Georgia State University, USA648531University of Mazandaran, Iran7125585University of Guilan, Iran
      Abstract: Social Science Computer Review, Ahead of Print.
      The current study employs a construct from the criminological literature, thoughtfully reflective decision-making (TRDM), to understand cyber offenders’ decision-making and offer relevant insights to prevent online harassment. Using a sample of Iranian high school students (N = 366), we employ OLS and SEM to test whether and how TRDM, perceived deterrence, and prior victimization influence the most common forms of online harassment: cyberbullying and cyberstalking. Findings demonstrate cyberbullying and cyberstalking victimization increase engagement in offending behavior while participants’ fear of sanction reduces engagement in both cyberbullying and cyberstalking perpetration. Notably, results demonstrate that TRDM has a direct, mediating, and moderating effect on both forms of offending. TRDM also has an indirect effect on cyberbullying and cyberstalking perpetration through victimization and participants’ perceptions of sanction. Unlike contemporary, pre-dispositional theories of crime, TRDM is dynamic and can be improved via educational programming. We posit that current cyber hygiene campaigns should include elements aimed to improve individuals’ cognitive decision-making capabilities. Guided by theory, and based on the results of the current study, this translational approach could prevent victimization while simultaneously improving other elements of the participants’ life.
      Citation: Social Science Computer Review
      PubDate: 2024-07-20T07:33:05Z
      DOI: 10.1177/08944393241261983
       
  • Meta-Dominance Analysis – A Tool for the Assessment of the Quality
           of Digital Behavioural Data

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      Authors: Andreas Schneck, Wojtek Przepiorka; Wojtek Przepiorka1Department of Sociology, 9183Ludwig-Maximilians Universität München, Germany2Department of Sociology / ICS, 8125Utrecht University, Netherlands
      Abstract: Social Science Computer Review, Ahead of Print.
      We propose a simple yet comprehensive conceptual framework for the identification of different sources of error in research with digital behavioural data. We use our framework to map potential sources of error in 25 years of research on reputation effects in peer-to-peer online market platforms. Using a meta-dataset comprising 346 effect sizes extracted from 109 articles, we apply meta-dominance analysis to quantify the relative importance of different error components. Our results indicate that 85% of explained effect size heterogeneity can be attributed to the measurement process, which comprises the choice of platform, data collection mode, construct operationalisation and variable transformation. Error components attributable to the sampling process or publication bias capture relatively small parts of the explained effect size heterogeneity. This approach reveals at which stages of the research process researcher decisions may affect data quality most. This approach can be used to identify potential sources of error in established strands of research beyond the literature of behavioural data from online platforms.
      Citation: Social Science Computer Review
      PubDate: 2024-06-18T04:38:48Z
      DOI: 10.1177/08944393241261958
       
  • The Unseen Targets of Hate: A Systematic Review of Hateful Communication
           Datasets

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      Authors: Zehui Yu, Indira Sen, Dennis Assenmacher, Mattia Samory, Leon Fröhling, Christina Dahn, Debora Nozza, Claudia Wagner; Indira Sen, Dennis Assenmacher, Mattia Samory, Leon Fröhling, Christina Dahn, Debora Nozza, Claudia Wagner128363GESIS - Leibniz-Institute for the Social Sciences, Germany29165RWTH Aachen, Germany326567University of Konstanz, Germany4Sapienza University of Rome, Italy518982Bocconi University, Italy6Complexity Science Hub, Austria
      Abstract: Social Science Computer Review, Ahead of Print.
      Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may discriminate against them, we know surprisingly little about the provenance of such bias. To fill this gap, we present a systematic review of the datasets for the automated detection of hateful communication introduced over the past decade, and unpack the quality of the datasets in terms of the identities that they embody: those of the targets of hateful communication that the data curators focused on, as well as those unintentionally included in the datasets. We find, overall, a skewed representation of selected target identities and mismatches between the targets that research conceptualizes and ultimately includes in datasets. Yet, by contextualizing these findings in the language and location of origin of the datasets, we highlight a positive trend towards the broadening and diversification of this research space.
      Citation: Social Science Computer Review
      PubDate: 2024-06-13T04:47:17Z
      DOI: 10.1177/08944393241258771
       
  • Leveraging Open Large Language Models for Multilingual Policy Topic
           Classification: The Babel Machine Approach

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      Authors: Miklós Sebők, Ákos Máté, Orsolya Ring, Viktor Kovács, Richárd Lehoczki; Ákos Máté, Orsolya Ring, Viktor Kovács, Richárd Lehoczki1374456HUN-REN Centre for Social Sciences, Hungary
      Abstract: Social Science Computer Review, Ahead of Print.
      The article presents an open-source and freely available natural language processing system for comparative policy studies. The CAP Babel Machine allows for the automated classification of input files based on the 21 major policy topics of the codebook of the Comparative Agendas Project (CAP). By using multilingual XLM-RoBERTa large language models, the pipeline can produce state-of-the-art level outputs for selected pairs of languages and domains (such as media or parliamentary speech). For 24 cases out of 41, the weighted macro F1 of our language-domain models surpassed 0.75 (and, for 6 language-domain pairs, 0.90). Besides macro F1, for most major topic categories, the distribution of micro F1 scores is also centered around 0.75. These results show that the CAP Babel machine is a viable alternative for human coding in terms of validity at less cost and higher reliability. The proposed research design also has significant possibilities for scaling in terms of leveraging new models, covering new languages, and adding new datasets for fine-tuning. Based on our tests on manifesto data, a different policy classification scheme, we argue that model-pipeline frameworks such as the Babel Machine can, over time, potentially replace double-blind human coding for a multitude of comparative classification problems.
      Citation: Social Science Computer Review
      PubDate: 2024-06-11T11:43:12Z
      DOI: 10.1177/08944393241259434
       
  • The Dark Sides of AI Advertising: The Integration of Cognitive Appraisal
           Theory and Information Quality Theory

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      Authors: Luan-Thanh Nguyen, Tri-Quan Dang, Dang Thi Viet Duc; Tri-Quan Dang, Dang Thi Viet Duc1587480Ho Chi Minh City University of Foreign Languages-Information Technology, Vietnam2121958Posts Telecommunications Institute of Technology, Vietnam
      Abstract: Social Science Computer Review, Ahead of Print.
      Artificial intelligence (AI) is a collection of rapidly evolving disruptive technologies that radically alter various aspects of people, business, society, and the environment. AI increasingly provides significant advertising opportunities for society and business organizations. However, AI could be used to spread disinformation if it were deliberately programmed to produce misleading advertising content. Using cognitive appraisal theory and information quality theory to study how consumers assess threats and develop AI marketing coping strategies from the information generated by AI, this study examines the outcome of the dark side of AI advertising. We collected data from 451 AI-advertising users in Vietnam. The results based on PLS-SEM showed interesting and novelty results. The statistical analysis showed a negative correlation between contextual, representational, accessibility, and threat appraisals. There was also a statistically significant positive correlation between contextual, representational, accessibility, and coping appraisals. Threat appraisals were positively correlated with anger and anxiety but not loneliness. Coping appraisal was significant and negatively correlated with anxiety but not anger or loneliness. This study advances theory and management.
      Citation: Social Science Computer Review
      PubDate: 2024-06-07T11:50:50Z
      DOI: 10.1177/08944393241258760
       
  • Assessing the Interplay Between Public Attention and Government
           Responsiveness With Digital Trace Data: Navigating Leadership and
           Followership in China’s COVID-19 Vaccination Campaign

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      Authors: Yunya Song, Ran Xu, Yi-Hui Christine Huang, Shijun Ni, Yining Fan; Ran Xu, Yi-Hui Christine Huang, Shijun Ni, Yining Fan126679Hong Kong Baptist University, Hong Kong27712University of Connecticut, USA353025City University of Hong Kong, Hong Kong
      Abstract: Social Science Computer Review, Ahead of Print.
      China’s digital campaign to “vaccinate all who can be vaccinated,” officially launched in December 2020, carries global implications. Grounded in the agenda-setting framework to reveal how the responses of different groups are shaped and their agendas interact, this study analyzed two years of Sina Weibo (the largest Chinese microblogging service) and Baidu (the leading search engine in China) search index data to investigate the interrelationships among different groups’ issue foci and the effects of sentiment, rationality, and moral motivation in the agenda-setting process. Large-scale computational analyses were conducted to determine the extent to which the Chinese government followed the public’s issue preferences and identify which segment of the public had a stronger ability to set the agenda. The results indicated that as the central government transitioned from leading to following the public, regional governments had a greater impact on the public agenda compared to the central government or media. The government, public, and media differed in their usage of sentiment and moral motivation on social media during the vaccination campaign, and this varied depending on the campaign’s stage. Notably, all stakeholders emphasized individual-centered values over community-centered values when addressing vaccination. The findings shed light on effective strategies for social mobilization through targeted public health messaging.
      Citation: Social Science Computer Review
      PubDate: 2024-06-04T03:33:09Z
      DOI: 10.1177/08944393241258217
       
  • Effect of Successful Aging on Technology Acceptance: The Moderating Role
           of Selection, Optimization, and Compensation Strategies

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      Authors: Te-Feng Yeh, Yu-Chia Chang, Shang-Yu Yang, Cheng-Chia Yang; Yu-Chia Chang, Shang-Yu Yang, Cheng-Chia Yang1Department of Healthcare Administration, 63340Central Taiwan University of Science Technology, Taichung, Taiwan2Department of Long Term Care, 252857National Quemoy University, Kinmen County, Taiwan3Department of Healthcare Administration, 63267Asia University, Taichung, Taiwan
      Abstract: Social Science Computer Review, Ahead of Print.
      Successful aging improves the health and well-being of older adults. Positive attitudes toward aging gave rise to more confidence in handling changes in their life in old age, thus reducing their barriers to information technology use often exist among older adults. Therefore, the influence of successful aging and technology use behavior on older adults’ aging process is a crucial research topic. The technology acceptance model was applied in this study to understand the relationship between selective optimization with compensation (SOC) strategies and technology acceptance. Individuals (n = 208) aged 60 years or older who possessed a smartphone but lacked knowledge on how to use it beyond basic operations were recruited from nine community care centers in Taiwan. The participants took part in two group-based (n = 6–7 each) training programs covering smartphone applications (16 hours) and SOC strategies (14 hours) related to smartphone use, with the training period lasting 4.5 months. Surveys were conducted after smartphone application training and after SOC strategy training. The results showed that SOC moderated the relationship between perceived usefulness, perceived ease of use, and older adults’ AT toward smartphones. All study hypotheses were supported, and the positive attitude toward aging motivates the elderly to utilize smartphones to compensate for aging-related deficits in daily life. The findings herein can be used as a reference for those wishing to encourage older adults to use smartphones in the pursuit of successful aging.
      Citation: Social Science Computer Review
      PubDate: 2024-05-31T04:36:32Z
      DOI: 10.1177/08944393241258218
       
  • Estimating Measurement Quality in Digital Trace Data and Surveys Using the
           MultiTrait MultiMethod Model

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      Authors: Alexandru Cernat, Florian Keusch, Ruben L. Bach, Paulina K. Pankowska; Florian Keusch, Ruben L. Bach, Paulina K. Pankowska15292University of Manchester, UK2University of Mannheim, Germany38125Utrecht University, Netherlands
      Abstract: Social Science Computer Review, Ahead of Print.
      Digital trace data are receiving increased attention as a potential way to capture human behavior. Nevertheless, this type of data is far from perfect and may not always provide better data compared to traditional social surveys. In this study we estimate measurement quality of survey and digital trace data on smartphone usage with a MultiTrait MultiMethod (MTMM) model. The experimental design included five topics relating to the use of smartphones (traits) measured with five methods: three different survey scales (a 5- and a 7-point frequency scale and an open-ended question on duration) and two measures from digital trace data (frequency and duration). We show that surveys and digital trace data measures have very low correlation with each other. We also show that all measures are far from perfect and, while digital trace data appears to have often better quality compared to surveys, that is not always the case.
      Citation: Social Science Computer Review
      PubDate: 2024-05-22T06:55:25Z
      DOI: 10.1177/08944393241254464
       
  • Adaptive Self-Reflection as a Social Media Self-Effect: Insights from
           Computational Text Analyses of Self-Disclosures of Unreported Sexual
           Victimization in a Hashtag Campaign

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      Authors: Tien Ee Dominic Yeo, Tsz Hang Chu; Tsz Hang Chu1Department of Communication Studies, 26679Hong Kong Baptist University, Hong Kong2Department of Journalism Communication, 59154Hong Kong Shue Yan University, Hong Kong
      Abstract: Social Science Computer Review, Ahead of Print.
      Hashtag campaigns calling out sexual violence and rape myths offer a unique context for disclosing sexual victimization on social media. This study investigates the applicability of adaptive self-reflection as a potential self-effect from such public disclosures of unreported sexual victimization experiences by analyzing 92,583 tweets that invoked #WhyIDidntReport. A supervised machine learning classifier determined that 61.8% of the tweets were self-disclosures of sexual victimization. Linguistic Inquiry and Word Count (LIWC) analysis showed statistically significant differences in four psycholinguistic dimensions (greater use of past focus, cognitive processes, insight, and causation words) connected with reflective processing in tweets with self-disclosed sexual victimization compared to those without. Additionally, topic modeling and thematic analysis identified nine salient topics within the self-disclosing tweets, comprising three self-distanced representations (i.e., relatively abstract and insightful construals) of the unwanted experiences: (a) acknowledging one’s previously unacknowledged victimization, (b) reaffirming one’s rationale for not reporting, and (c) decrying invalidating response to one’s disclosure. Moving beyond reception effects and social support in extant research about social media as a coping tool, this study provides new empirical insights into the potential of social media to promote expressive meaning-making of upsetting and traumatic experiences in ways that support recovery and resilience.
      Citation: Social Science Computer Review
      PubDate: 2024-05-21T03:19:22Z
      DOI: 10.1177/08944393241252640
       
  • Nonparticipation Bias in Accelerometer-Based Studies and the Use of
           Propensity Scores

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      Authors: Christopher Antoun, Alexander Wenz; Alexander Wenz1College of Information Studies Joint Program in Survey Methodology, 1068University of Maryland, USA2Mannheim Centre for European Social Research, 26573University of Mannheim, Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      Relatively little attention has been paid to the effects of nonparticipation on data quality in population-based studies that use accelerometers to measure physical activity. We examine these issues using data from the 2013 Longitudinal Internet Studies for the Social Sciences (LISS) panel and 2013–2014 National Health and Nutrition Examination Survey (NHANES) accelerometer studies, both of which collected survey data in advance and therefore permit comparisons of self-reported physical activity between participants and nonparticipants to the accelerometer studies. While individuals with high levels of self-reported physical activity are overrepresented in the participant samples, the differences are modest in both studies. However, in the LISS panel this difference led to overestimates of physical activity that are not fully corrected by propensity score weighting adjustments (i.e., non-ignorable selection bias). This finding underscores the importance of assessing the potential influence of nonparticipation on accelerometer-derived estimates of physical activity.
      Citation: Social Science Computer Review
      PubDate: 2024-05-16T04:13:12Z
      DOI: 10.1177/08944393241254463
       
  • Unveiling Public Perception and Interpretation of China’s National
           Self-Image: Analyzing Chinese Online Commentary Data

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      Authors: Shuo Wang; China
      Abstract: Social Science Computer Review, Ahead of Print.
      This research study aims to address the oversight in previous studies that focused on constructing China’s image through the media without investigating how audiences perceive and interpret that depiction. This study aims to investigate how Chinese internet users perceive China’s self-image to understand Chinese citizens’ attitudes and reactions to political propaganda circulated by Chinese authorities online more effectively. To achieve this, we have chosen “This is China,” an online political program that presents China’s self-image. A total of 60,648 comments were collected and analyzed. For the analysis, T-LDA, Sentiment Analysis, and Semantic Network Analysis were employed. The study reveals six significant factors: Real-Life Stress, Patriotic Sentiment, Rational Emotion, Program Style, Presenter’s Public Persona, and Ironic Remarks, all of which shape the public’s perception of China’s image. Specifically, the study finds that Patriotism and Program Style have a positive influence on the audience’s perception, while the other factors hinder a favorable interpretation of the nation’s image.
      Citation: Social Science Computer Review
      PubDate: 2024-05-14T01:53:35Z
      DOI: 10.1177/08944393241253494
       
  • Use and Abuse of Social Media as a Punitive Remedy in Light of Criminal
           Law: A Tool or a Court' Analysis of the Chilean Regulation

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      Authors: Alejandra Castillo Ara; Law Faculty, 11240Universidad Diego Portales, Santiago, Chile
      Abstract: Social Science Computer Review, Ahead of Print.
      Over the last few years, Chile’s judicial system has witnessed a rise in criminal assumptions generated through social networks, under its hypotheses of funas, doxing, flagging or, in general, the exposure of the personal data of an individual, whether motivated by the performance of conduct of criminal relevance or simply of dubious morality or social appropriateness. Although these conducts originated as a form of digital social empowerment, they have turned into a criminal matter on their own right, posing a series of issues regarding the correct application of the law and casting doubt on the appropriate legal mechanisms to handle these types of accusations, as cases involving these kinds of conducts have been resolved both on a criminal and constitutional level. Thus, the purpose of this article is to determine whether the current Chilean national regulation provides sufficient tools that, with a reasonable interpretation, allow it to correctly handle these sorts of cases. Moreover, this article aims to, through a comprehensive analysis of Germany’s current legislation, determine whether Chile requires a new all-encompassing regulatory approach to these hypotheses similar to the German solution, or if the current rules provide a proper solution to the problem of public personal data exposure on social media as a dangerous behaviour for the person whose data is being exposed.
      Citation: Social Science Computer Review
      PubDate: 2024-05-07T12:36:55Z
      DOI: 10.1177/08944393241252639
       
  • Human or Not': An Experiment With Chatbot Manipulations to Test Machine
           Heuristics and Political Self-Concepts

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      Authors: Ke M. Huang-Isherwood, Jaeho Cho, Joo-Wha Hong, Eugene Lee; Jaeho Cho, Joo-Wha Hong, Eugene Lee15116University of Southern California, USA28789University of California Davis, USA335017Sungkyunkwan University, South Korea
      Abstract: Social Science Computer Review, Ahead of Print.
      Chatbots have a growing role to play in political discourse, including in political campaigns, voter mobilization ventures, and dissemination of political news, though chatbots in the political domain are relatively understudied. While testing the machine heuristics and political self-concepts frameworks, we carried out a 2 × 2 experiment where both perceived conversational partner (i.e., bot, human) and topic (i.e., political, casual) were manipulated (N = 126). During the experiment, participants exchanged chat messages with trained research confederates for 30 min. In support of the machine heuristics and political self-concepts frameworks, participants assigned to human partners reported more positive relationships and higher political interest. Through moderation analysis, liking the partner was found to differ between the perceived partner conditions, with perceived political knowledge varying more in the human conditions. Thus, the experimental findings add nuance to interpersonal (i.e., impression management and social identity theory) and human-computer interaction theories (i.e., machine heuristics and Computers Are Social Actors), and have broader implications for online political interactions and for decisionmakers of online political discourse spaces.
      Citation: Social Science Computer Review
      PubDate: 2024-05-07T02:01:30Z
      DOI: 10.1177/08944393241252027
       
  • Assessing Data Quality in the Age of Digital Social Research: A Systematic
           Review

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      Authors: Jessica Daikeler, Leon Fröhling, Indira Sen, Lukas Birkenmaier, Tobias Gummer, Jan Schwalbach, Henning Silber, Bernd Weiß, Katrin Weller, Clemens Lechner; Leon Fröhling, Indira Sen, Lukas Birkenmaier, Tobias Gummer, Jan Schwalbach, Henning Silber, Bernd Weiß, Katrin Weller, Clemens Lechner139020GESIS - Leibniz Institute for the Social Sciences, Germany228363University of Konstanz, Germany3University of Mannheim, Mannheim, Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data' To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.
      Citation: Social Science Computer Review
      PubDate: 2024-04-27T09:53:55Z
      DOI: 10.1177/08944393241245395
       
  • Are Large-Scale Data From Private Companies Reliable' An Analysis of
           Machine-Generated Business Location Data in a Popular Dataset

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      Authors: Nikolitsa Grigoropoulou, Mario L. Small; Mario L. Small19168University of Bremen, Germany25798Columbia University, USA
      Abstract: Social Science Computer Review, Ahead of Print.
      Large-scale data from private companies offer new opportunities to examine topics of scientific and social significance, such as racial inequality, partisan polarization, and activity-based segregation. However, because such data are often generated through automated processes, their accuracy and reliability for social science research remain unclear. The present study examines how quality issues in large-scale data from private companies can afflict the reporting of even ostensibly uncomplicated values. We assess the reliability with which an often-used device tracking data source, SafeGraph, sorted data it acquired on financial institutions into categories, such as banks and payday lenders, based on a standard classification system. We find major classification problems that vary by type of institution, and remarkably high rates of unidentified closures and duplicate records. We suggest that classification problems can affect research based on large-scale private data in four ways: detection, efficiency, validity, and bias. We discuss the implications of our findings, and list a set of problems researchers should consider when using large-scale data from companies.
      Citation: Social Science Computer Review
      PubDate: 2024-04-15T08:10:47Z
      DOI: 10.1177/08944393241245390
       
  • Measuring Smartphone Use: Survey Versus Digital Behavioral Data

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      Authors: Alexander Wenz, Florian Keusch, Ruben L. Bach; Florian Keusch, Ruben L. Bach126573University of Mannheim, Germany
      Abstract: Social Science Computer Review, Ahead of Print.
      While digital technology use and skills have typically been measured with surveys, digital behavioral data that are passively collected from individuals’ digital devices have recently emerged as an alternative method of measuring technology usage patterns in a more unobtrusive and detailed way. In this paper, we evaluate how passively collected smartphone usage data compare to self-reported measures of smartphone use, considering the three usage dimensions amount of use, variety of use, and activities of use. Based on a sample of smartphone users in Germany who completed a survey and had a tracking app installed on their smartphone, we find that the alignment between the survey and digital behavioral data varies by dimension of smartphone use. Whereas amount of use is considerably overreported in the survey data, variety of use aligns more closely across the two data sources. For activities of use, the alignment differs by type of activity. The results also show that the alignment between survey and digital behavioral data is systematically related to individuals’ sociodemographic characteristics, including age, gender, and educational attainment. Finally, latent class analyses conducted separately for the survey and digital behavioral data suggest similar typologies of smartphone use, although the overlap between the typologies on the individual level is rather small.
      Citation: Social Science Computer Review
      PubDate: 2024-01-11T08:03:25Z
      DOI: 10.1177/08944393231224540
       
 
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