JournalTOCs

Social Science Computer Review
Journal Prestige (SJR): 1.229

Citation Impact (citeScore): 3
Number of Followers: 13

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0894-4393 - ISSN (Online) 1552-8286
Published by Sage Publications

[1176 journals]

Noteworthy Disparities With Four CAQDAS Tools: Explorations in Organising
Live Twitter Data
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  Authors: Travis Noakes, Patricia Harpur, Corrie Uys
  Abstract: Social Science Computer Review, Ahead of Print.
  Qualitative data analysis software (QDAS) packages that support live data extraction are a relatively recent innovation. Little has been written concerning the research implications of differences in such QDAS packages’ functionalities, and how such disparities might contribute to contrasting analytical opportunities. Consequently, early-stage researchers may experience difficulties in choosing an apt QDAS for Twitter analysis. In response to both methodological gaps, this paper presents a software comparison across the four QDAS tools that support live Twitter data imports, namely, ATLAS.ti™, NVivo™, MAXQDA™ and QDA Miner™. The authors’ QDAS features checklist for these tools spotlights many differences in their functionalities. These disparities were tested through data imports and thematic coding that was derived from the same queries and codebook. The authors’ resultant QDAS experiences were compared during the first activity of a broad qualitative analysis process, ‘organising data’. Notwithstanding large difference in QDAS pricing, it was surprising how much the tools varied for aspects of qualitative research organisation. Notably, the quantum of data extracted for the same query differed, largely due to contrasts in the types and amount of data that the four QDAS could extract. Variations in how each supported visual organisation also shaped researchers’ opportunities for becoming familiar with Twitter users and their tweet content. Such disparities suggest that choosing a suitable QDAS for organising live Twitter data must dovetail with a researcher’s focus: ATLAS.ti accommodates scholars focused on wrangling unstructured data for personal meaning-making, while MAXQDA suits the mixed-methods researcher. QDA Miner’s easy-to-learn user interface suits a highly efficient implementation of methods, whilst NVivo supports relatively rapid analysis of tweet content. Such findings may help guide Twitter social science researchers and others in QDAS tool selection. Future research can explore disparities in other qualitative research phases, or contrast data extraction routes for a variety of microblogging services.
  Citation: Social Science Computer Review
  PubDate: 2023-09-26T03:04:04Z
  DOI: 10.1177/08944393231204163
The Influence of Political Fit, Issue Fit, and Targeted Political
Advertising Disclosures on Persuasion Knowledge, Party Evaluation, and
Chilling Effects
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  Authors: Melanie Hirsch, Alice Binder, Jörg Matthes
  Abstract: Social Science Computer Review, Ahead of Print.
  The availability of online data has altered the role of social media. By offering targeted online advertising, that is, persuasive messages tailored to user groups, political parties profit from large data profiles to send fine-grained advertising appeals to susceptible voters. This between-subject experiment (N = 421) investigates the influence of targeted political advertising disclosures (targeting vs. no-targeting disclosure), political fit (high vs. low), and issue fit (high vs. low) on recipients’ party evaluation and chilling effect intentions. The mediating role of targeting knowledge (TK) and perceived manipulative intent (PMI), two dimensions of persuasion knowledge, are investigated. The findings show that disclosing a targeting strategy and a high political fit activated individuals’ TK, that is, their recognition that their data had been used to show the ads, which then increased the evaluation of the political party and individuals’ intentions to engage in future chilling effect behaviors. High political fit decreased individuals’ reflections about the appropriateness of the targeted political ads (i.e., PMI), which then increased party evaluation. Issue fit did not affect individuals’ persuasion knowledge.
  Citation: Social Science Computer Review
  PubDate: 2023-09-12T10:19:10Z
  DOI: 10.1177/08944393231193731
Cheap, Quick, and Rigorous: Artificial Intelligence and the Systematic
Literature Review
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  Authors: Cameron F. Atkinson
  Abstract: Social Science Computer Review, Ahead of Print.
  The systematic literature review (SLR) is the gold standard in providing research a firm evidence foundation to support decision-making. Researchers seeking to increase the rigour, transparency, and replicability of their SLRs are provided a range of guidelines towards these ends. Artificial Intelligence (AI) and Machine Learning Techniques (MLTs) developed with computer programming languages can provide methods to increase the speed, rigour, transparency, and repeatability of SLRs. Aimed towards researchers with coding experience, and who want to utilise AI and MLTs to synthesise and abstract data obtained through a SLR, this article sets out how computer languages can be used to facilitate unsupervised machine learning for synthesising and abstracting data sets extracted during a SLR. Utilising an already known qualitative method, Deductive Qualitative Analysis, this article illustrates the supportive role that AI and MLTs can play in the coding and categorisation of extracted SLR data, and in synthesising SLR data. Using a data set extracted during a SLR as a proof of concept, this article will include the coding used to create a well-established MLT, Topic Modelling using Latent Dirichlet allocation. This technique provides a working example of how researchers can use AI and MLTs to automate the data synthesis and abstraction stage of their SLR, and aide in increasing the speed, frugality, and rigour of research projects.
  Citation: Social Science Computer Review
  PubDate: 2023-08-26T07:21:31Z
  DOI: 10.1177/08944393231196281
Novelty in News Search: A Longitudinal Study of the 2020 US Elections
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  Authors: Roberto Ulloa, Mykola Makhortykh, Aleksandra Urman, Juhi Kulshrestha
  Abstract: Social Science Computer Review, Ahead of Print.
  The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, we conduct a longitudinal study of news results of five search engines collected in short bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries (“joe biden,” “donald trump,” and “us elections”) compared to topical (e.g., “coronavirus”) or stable (e.g., “holocaust”) queries. We demonstrate that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.
  Citation: Social Science Computer Review
  PubDate: 2023-08-14T01:05:49Z
  DOI: 10.1177/08944393231195471
The Impact of Economic Degradation on the Uí Bhriain Civil War
(1276–1318): An Agent-Based Modeling Approach
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  Authors: Vinicius Marino Carvalho
  Abstract: Social Science Computer Review, Ahead of Print.
  Between 1276 and 1318, English magnates unsuccessfully attempted to establish a lordship in the Irish kingdom of Thomond, southwestern Ireland, by exploiting a dynastic feud dividing the then-ruling lineage, the Uí Bhriain. The conflict coincided with a series of extreme events that beset western Europe in the late 13th and early 14th centuries, such as the beginning of the Little Ice Age and the Great European Famine of 1315–1322. The goal of this work was to evaluate to the extent to which economic degradation at the turn of the 14th century affected the outcome of the war. The hypothesis that such degradation affected the war’s outcome was tested using agent-based modeling, which involved the virtual reconstruction of Late Medieval Thomond to study past conditions by proxy. This article describes the historical research carried out to elaborate the conceptual model, the implementation of the model as a computer simulation, and the experiments carried out to virtually explore the Uí Bhriain Civil War. A quantitative analysis of the experimental results revealed some correlation between late 13th century economic degradation and the fortunes of belligerent factions in the wars of 1276–1318, although the effect was not sufficiently strong to have been a crucial factor in the outcome of the conflict.
  Citation: Social Science Computer Review
  PubDate: 2023-08-12T12:52:53Z
  DOI: 10.1177/08944393231194983
Cyberbullying and Traditional Bullying Victimization, Depressive Symptoms,
and Suicidal Ideation Among Chinese Early Adolescents: Cognitive
Reappraisal and Emotion Invalidation as Moderators
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  Authors: Jianhua Zhou, Haiyan Zhao, Yan Zou
  Abstract: Social Science Computer Review, Ahead of Print.
  This study examined how depressive symptoms play mediating roles between cyberbullying and traditional bullying victimization and suicidal ideation and the moderating roles of cognitive reappraisal and emotion invalidation. A total of 1,823 Chinese adolescents (Mean age = 11.20, SD = 1.21, 47.8% girls) participated this study. Results showed that cyberbullying victimization was more strongly related to suicidal ideation than traditional bullying victimization. Depressive symptoms played mediating roles between cyberbullying and traditional bullying victimization and suicidal ideation. Cognitive reappraisal mitigated the effects of cyberbullying and traditional bullying victimization on depressive symptoms, and perceived emotion invalidation strengthened the effect of depressive symptoms on suicidal ideation. Results further showed that the mediating effect of depressive symptoms was more prominent when there were low levels of cognitive reappraisal and more perceived emotion invalidation. Promoting youths’ cognitive reappraisal and providing validating responses to their depressive symptoms could mitigate the destructive effects of bullying victimization on suicidal ideation.
  Citation: Social Science Computer Review
  PubDate: 2023-08-02T10:40:11Z
  DOI: 10.1177/08944393231192237
Studyholism and Health Outcomes: Could Internet Addiction Make the
Difference'
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  Authors: Danila Molinaro, Yura Loscalzo, Carmela Buono, Ludovica Del Giudice, Alessio Lustro, Chiara Ghislieri, Paola Spagnoli
  Abstract: Social Science Computer Review, Ahead of Print.
  Recently, Loscalzo and Giannini have proposed Studyholism as a possible new clinical condition, highlighting its potential antecedents and outcomes. The present study investigates the moderating role of Internet addiction in the relationship between Studyholism, academic exhaustion and insomnia. Three hundred and eighteen Italian university students (85.50% female; mean age = 22.98 ± 4.34) participated in the survey during the third wave of the COVID-19 pandemic. The two moderation models were tested using the structural equation model with Mplus 7. Results showed a significant direct effect of Studyholism on both academic exhaustion and insomnia and also confirmed the moderating role of Internet addiction in the aforementioned relationships. Although there is a linear relationship between Studyholism and the outcomes, at lower levels of Internet addiction, there is a greater effect of Studyholism on both academic exhaustion and insomnia than at medium and high levels of Internet addiction. Based on these findings, we suggest screening students who report insomnia and academic exhaustion for both Internet addiction and Studyholism, as they might both contribute to these negative health-related aspects.
  Citation: Social Science Computer Review
  PubDate: 2023-07-28T10:44:59Z
  DOI: 10.1177/08944393231192233
Guaranteed Incentives and Prize Drawings: Effects on Participation, Data
Quality, and Costs in a Web Survey of College Students on Sensitive Topics
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  Authors: Jennifer Dykema, John Stevenson, Cameron P. Jones, Brendan Day
  Abstract: Social Science Computer Review, Ahead of Print.
  Many studies rely on traditional web survey methods in which all contacts with sample members are through email and the questionnaire is administered exclusively online. Because it is difficult to effectively administer prepaid incentives via email, researchers frequently employ lotteries or prize draws as incentives even though their influence on survey participation is small. The current study examines whether a prize draw is more effective if it is divided into a few larger amounts versus several smaller amounts and compares prize draws to a small but guaranteed postpaid incentive. Data are from the 2019 Campus Climate Survey on Sexual Assault and Sexual Misconduct. Sample members include 38,434 undergraduate and graduate students at a large Midwestern university who were randomly assigned to receive: a guaranteed $5 Amazon gift card; entry into a high-payout drawing for one of four $500 prizes; or entry into a low-payout drawing for one of twenty $100 prizes. Results indicate the guaranteed incentive increased response rates, with no difference between the prize draws. While results from various data quality outcomes show the guaranteed incentive reduced break-off rates and the high-payout drawing increased item nonresponse, there were no differences across incentive conditions in rates of speeding, reporting of sensitive data, straightlining, or sample representativeness. As expected, the prize draws had much lower overall and per complete costs.
  Citation: Social Science Computer Review
  PubDate: 2023-07-21T02:48:39Z
  DOI: 10.1177/08944393231189853
Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on
Optical Unmanned Aerial Vehicles Data as a Solution
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  Authors: Mark Altaweel, Adel Khelifi, Mohammad Maher Shana’ah
  Abstract: Social Science Computer Review, Ahead of Print.
  The looting of cultural heritage sites has been a growing problem and threatens national economies, social identity, destroys research potential, and traumatizes communities. For many countries, the challenge in protecting heritage is that there are often too few resources, particularly paid site guards, while sites can also be in remote locations. Here, we develop a new approach that applies deep learning methods to detect the presence of looting at heritage sites using optical imagery from unmanned aerial vehicles (UAVs). We present results that demonstrate the accuracy, precision, and recall of our approach. Results show that optical UAV data can be an easy way for authorities to monitor heritage sites, demonstrating the utility of deep learning in aiding the protection of heritage sites by automating the detection of any new damage to sites. We discuss the impact and potential for deep learning to be used as a tool for the protection of heritage sites. How the approach could be improved with new data is also discussed. Additionally, the code and data used are provided as part of the outputs.
  Citation: Social Science Computer Review
  PubDate: 2023-07-10T12:31:37Z
  DOI: 10.1177/08944393231188471
TikTok and Civic Activity Among Young Adults
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  Authors: Kenneth W. Moffett, Laurie L. Rice
  Abstract: Social Science Computer Review, Ahead of Print.
  TikTok is known for its lighthearted dance and lip-synch videos, yet videos with the hashtag #politics have garnered nearly 14 billion views. Does young adults’ politically oriented expression on TikTok lead to increased civic engagement offline' TikTok helps incorporate young adults into political social networks that may encourage additional civic activity. In addition, the playful, humorous nature of TikTok-based political expression encourages young adults to develop participatory, political selves. Using data from a 2020 survey of Americans between 18 and 25 years old, we find that posting political videos on TikTok connects with higher offline civic engagement. The results suggest that playful political expression is an important feature for promoting young adult civic engagement.
  Citation: Social Science Computer Review
  PubDate: 2023-07-10T09:18:40Z
  DOI: 10.1177/08944393231188470
Not All Bots are Created Equal: The Impact of Bots Classification
Techniques on Identification of Discursive Behaviors Around the COVID-19
Vaccine and Climate Change
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  Authors: Rui Wang, Dror Walter, Yotam Ophir
  Abstract: Social Science Computer Review, Ahead of Print.
  As concerns about social bots online increase, studies have attempted to explore the discourse they produce, and its effects on individuals and the public at large. We argue that the common reliance on aggregated scores of binary classifiers for bot detection may have yielded biased or inaccurate results. To test this possibility, we systematically compare the differences between non-bots and bots using binary and non-binary classifiers (classified into the categories of astroturf, self-declared, spammers, fake followers, and Other). We use two Twitter corpora, about COVID-19 vaccines (N = 1,697,280) and climate change (N = 1,062,522). We find that both in terms of volume and thematic content, the use of binary classifiers may hinder, distort, or mask differences between humans and bots, that could only be discerned when observing specific bot types. We discuss the theoretical and practical implications of these findings.
  Citation: Social Science Computer Review
  PubDate: 2023-07-06T05:49:31Z
  DOI: 10.1177/08944393231188472
Integrating Human Insights Into Text Analysis: Semi-Supervised Topic
Modeling of Emerging Food-Technology Businesses’ Brand Communication on
Social Media
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  Authors: Leona Yi-Fan Su, Tianli Chen, Yee Man Margaret Ng, Ziyang Gong, Yi-Cheng Wang
  Abstract: Social Science Computer Review, Ahead of Print.
  Textual social media data have become indispensable to researchers’ understanding of message strategies and other marketing practices. In a new departure for the field of brand communication, this study adopts and extends a semi-supervised machine-learning approach, guided latent Dirichlet allocation (LDA), which incorporates human insights into the discovery and classification of topics. We used it to analyze tweets from businesses involved with an emerging food technology, cultured meat, and delineated four key message strategies used by these brands: providing functional, educational, corporate social responsibility, and relational content. We further ascertained the relationships between brands and the key topics embedded in their Twitter data. A comparison of model performance suggests that guided LDA can be an advantageous alternative to traditional LDA, which is characterized by high efficiency and immense popularity among researchers, but—because of its unsupervised nature—yields findings that can be difficult to interpret. The present study therefore has critical theoretical and methodological implications for communication and marketing scholars.
  Citation: Social Science Computer Review
  PubDate: 2023-06-26T08:24:51Z
  DOI: 10.1177/08944393231184532
Incorporating Virtual Reality in Public Health Campaigns: COVID-19 as the
Context
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  Authors: Zhan Xu, Veronica Weser, Lulu Peng, Mary Laffidy
  Abstract: Social Science Computer Review, Ahead of Print.
  One of the greatest challenges for public health campaigns is communicating health risks due to the existence of psychological distance. Using COVID-19 as a context, this study designed and tested virtual reality (VR) campaigns based on construal level theory. It assessed the immediate and after-effects of VR on COVID-19 preventive intentions/behaviors and risk perceptions. A total of 120 participants were randomly assigned to see one of four messages: a VR message emphasizing self-interest, a VR message emphasizing other-interest, a print message emphasizing self-interest, or a print message emphasizing other-interest. Preventive intentions/behaviors were assessed at three different times: before, immediately after, and one week after the experimental treatment. Immediately following message exposure, participants exposed to the VR messages perceived a higher level of self-risk than those exposed to print messages. Disgust and fear mediated these effects. One week following message exposure, unvaccinated participants exposed to the VR messages had a higher intention to get vaccinated than those exposed to print messages. Recommendations on how to effectively utilize VR in health interventions are provided.
  Citation: Social Science Computer Review
  PubDate: 2023-06-21T03:39:33Z
  DOI: 10.1177/08944393231185257
Dynamic Analysis of the Timing of Survey Participation: An Application of
Event History Analysis of the Stochastic Process of Response in a
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  Authors: Rolf Becker
  Abstract: Social Science Computer Review, Ahead of Print.
  The response patterns across the fieldwork period are analyzed in the context of a panel study with a sequential mixed-mode design including a self-administered online questionnaire and a computer-assisted telephone interview. Since the timing of participation is modelled as a stochastic process of individuals’ response behaviour, event history analysis is applied to reveal time-constant and time-varying factors that influence this process. Different distributions of panelists’ propensity for taking part in the web-based survey or, alternatively, in the computer-assisted telephone interview can be considered by hazard rate analysis. Piecewise constant rate models and analysis of sub-episodes demonstrate that it is possible to describe the time-related development of response rates by reference to individuals’ characteristics, resources and abilities, as well as panelists’ experience with previous panel waves. Finally, it is shown that exogenous factors, such as a mixed-mode survey design, the incentives offered to participants and the reminders that are sent out, contribute significantly to time-related response after the invitation to participate in a survey with a sequential mixed-mode design. Overall, this contribution calls for a dynamic analysis of response behaviour instead of the categorization of response groups.
  Citation: Social Science Computer Review
  PubDate: 2023-06-15T03:09:20Z
  DOI: 10.1177/08944393231183871
Emoji as a Social Presence Tool Among Arab Digital Media Users: Do the
Demographic Variables of the Sender Play a Role'
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  Authors: Shuaa Aljasir
  Abstract: Social Science Computer Review, Ahead of Print.
  To contribute to the current knowledge, this research was conducted, perhaps for the first time, among 1354 Arab users of digital media platforms to investigate emoji as a social presence tool and how the variables of the gender, generation, and the sender’s relationship to the receiver affect the usage and interpretation of the appropriateness of these graphical icons. Among the significant results of this study, generation and gender explained a significant amount of the variance in the frequency and motivation index. Interestingly, there was a significant, three-way interaction among senders’ gender, raters’ gender, and salience. The analysis also showed that the generation and relationship of the sender had a statistically significant effect on appropriateness ratings.
  Citation: Social Science Computer Review
  PubDate: 2023-06-13T11:28:13Z
  DOI: 10.1177/08944393231181638
Translation and Validation of the Brief Inventory of Technology
Self-Efficacy (BITS): Simplified and Traditional Chinese Versions
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  Authors: Arne Weigold, Ingrid K. Weigold, Xiangling Zhang, Ning Tang, Yun Kai Chong
  Abstract: Social Science Computer Review, Ahead of Print.
  Computer self-efficacy (CSE) continues to be an important construct in research and application. Two measures of CSE, the Brief Inventory of Technology Self-Efficacy (BITS) and the Brief Inventory of Technology Self-Efficacy – Short Form (BITS-SF) were recently developed to correct for issues in other available measures. The BITS and BITS-SF were originally written in English, and their psychometric properties assessed in samples from the United States. The current two studies translated the BITS and BITS-SF into simplified Chinese (Mainland China) and traditional Chinese (Taiwan) and assessed their psychometric properties. In Study 1, 207 adults in Mainland China completed the simplified Chinese BITS and BITS-SF, as well as measures given to assess convergent, discriminant, and concurrent validity. In Study 2, 273 adults in Taiwan did the same, except that they completed the traditional Chinese BITS and BITS-SF. In both studies, the translated BITS showed evidence of a three-factor correlated structure, and the translated BITS-SF yielded several underlying classes consistent with theory and scoring interpretation. Additionally, the translated measures’ scores showed solid evidence of convergent, discriminant, and concurrent validity. The results replicate the findings using the original BITS and BITS-SF and extend them to simplified Chinese and traditional Chinese translated versions. These versions are recommended for use in research and applied settings to assess CSE and are available for use. Both the original and translated measures are available for download at www.bitssurvey.com.
  Citation: Social Science Computer Review
  PubDate: 2023-05-30T01:16:01Z
  DOI: 10.1177/08944393231176596
Seeded Sequential LDA: A Semi-Supervised Algorithm for Topic-Specific
Analysis of Sentences
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  Authors: Kohei Watanabe, Alexander Baturo
  Abstract: Social Science Computer Review, Ahead of Print.
  Topic models have been widely used by researchers across disciplines to automatically analyze large textual data. However, they often fail to automate content analysis, because the algorithms cannot accurately classify individual sentences into pre-defined topics. Aiming to make topic classification more theoretically grounded and content analysis in general more topic-specific, we have developed Seeded Sequential Latent Dirichlet allocation (LDA), extending the existing LDA algorithm, and implementing it in a widely accessible open-source package. Taking a large corpus of speeches delivered by delegates at the United Nations General Assembly as an example, we explain how our algorithm differs from the original algorithm; why it can classify sentences more accurately; how it accepts pre-defined topics in deductive or semi-deductive analysis; how such ex-ante topic mapping differs from ex-post topic mapping; how it enables topic-specific framing analysis in applied research. We also offer practical guidance on how to determine the optimal number of topics and select seed words for the algorithm.
  Citation: Social Science Computer Review
  PubDate: 2023-05-29T06:56:23Z
  DOI: 10.1177/08944393231178605
The Impact of day of Mailing on Web Survey Response Rate and Response
Speed
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  Authors: Peter Lynn, Annamaria Bianchi, Alessandra Gaia
  Abstract: Social Science Computer Review, Ahead of Print.
  The day of the week on which sample members are invited to participate in a web survey might influence propensity to respond, or to respond promptly (within two days from the invitation). This effect could differ between sample members with different characteristics. We explore such effects using a large-scale experiment implemented on the Understanding Society Innovation Panel, in which some people received an invitation on a Monday and some on a Friday. Specifically, we test whether any effect of the invitation day is moderated by economic activity status (which may result in a different organisation of time by day of the week), previous participation in the panel, or whether the invitation was sent only by post or by post and email simultaneously. Overall, we do not find any effect of day of invitation in survey participation or in prompt participation. However, sample members who provided an email address, and, thus, were contacted by email in addition to postal letter, are less likely to participate if invited on Friday (email reminders: Sunday and Tuesday) as opposed to Monday (email reminders: Wednesday and Friday). Given that no difference between the two protocols is found for prompt response, the effect seems to be due to the day of mailing of reminders. With respect to sample members' economic activity status, those not having a job and the retired are less likely to participate when invited on a Friday; this result holds also for prompt participation, but only for retired respondents. Also, sample members who work long hours are less likely to participate when invited on a Friday; however, no effect is found for prompt response.
  Citation: Social Science Computer Review
  PubDate: 2023-05-27T01:27:14Z
  DOI: 10.1177/08944393231173887
Integrating Street Views, Satellite Imageries and Remote Sensing Data Into
Economics and the Social Sciences
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  Authors: Guan-Yuan Wang
  Abstract: Social Science Computer Review, Ahead of Print.
  Street views, satellite imageries and remote sensing data have been integrated into a wide spectrum of topics in the social sciences. Computer vision methods not only help analysts and policymakers make better decisions and produce more effective solutions but they also enable models to achieve more precise predictions and greater interpretability. In this paper, we review the growing literature applying such methods to economic issues and the social sciences, in which social scientists employ deep learning approaches to utilise image data to retrieve additional information. Typically, image data produce better results than traditional approaches and can provide detailed results and helpful insights to improve society and people’s well-being.
  Citation: Social Science Computer Review
  PubDate: 2023-05-26T11:50:48Z
  DOI: 10.1177/08944393231178604
Social Network and Semantic Analysis of Roe v. Wade’s Reversal on
Twitter
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  Authors: Zehui Dai, Cory Higgs
  Abstract: Social Science Computer Review, Ahead of Print.
  On June 24, 2022, the Supreme Court released its decision in Dobbs v. Jackson Women’s Health, which officially repealed Roe v. Wade and its subsequent rulings. Employing social network analysis and semantic analysis methods, the current project reviews the public reaction among Twitter users shared from the May 2 draft leak to the June 24 official repeal, using a series of Twitter hashtags related to Roe v. Wade. The project identified the main influencers within the network, namely, journalist/news organizations, Internet celebrities, activists/activist groups, professional/non-profit organizations, and politicians/political organizations through social network analysis. Through semantic analysis, the authors found prominent themes such as legal concerns, discourse on reproductive rights, distrusting of Supreme Court’s authority, and political nepotism. The results offer policy implications and communication message strategies to healthcare providers and policymakers. The authors believe that the polarizing nature of Roe v. Wade-related issues will be a crucial factor in shaping voters’ decisions during the upcoming 2024 presidential election.
  Citation: Social Science Computer Review
  PubDate: 2023-05-25T06:11:11Z
  DOI: 10.1177/08944393231178602
Like, Comment, and Share on TikTok: Exploring the Effect of Sentiment and
Second-Person View on the User Engagement with TikTok News Videos
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  Authors: Zicheng Cheng, Yanlin Li
  Abstract: Social Science Computer Review, Ahead of Print.
  TikTok—the world’s most downloaded app since 2020, has become a place for more than silly dancing and lip-syncing. TikTok users are increasingly turning to TikTok for news content. Meanwhile, news publishers are embracing TikTok to reach a younger audience. We aim to examine the content strategy adopted by the most-followed news publishers on TikTok and how effective their TikTok strategy is in spurring audience engagement in terms of liking, commenting, and sharing. This study retrieved 101,292 TikTok news videos as of November 22, 2022. With the help of computer vision, natural language processing, and sentiment analysis, we found that TikTok news videos containing negative sentiment and more second-person view shots are associated with significantly higher audience engagement. In addition, this study demonstrated that the TikTok video features and engagement levels differ between the news publishers and other TikTok creators. Moderator analysis shows that both the effect of negative sentiment on engagement and the effect of the second-person view on engagement are moderated by the TikTok account type. The impact of negative sentiment and second-person view on engagement behaviors becomes smaller or even insignificant for news publisher TikTok videos. Theoretical and practical implications are discussed in this study.
  Citation: Social Science Computer Review
  PubDate: 2023-05-25T03:20:56Z
  DOI: 10.1177/08944393231178603
Cutting Through the Comment Chaos: A Supervised Machine Learning Approach
to Identifying Relevant YouTube Comments
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  Authors: A. Marthe Möller, Susan A. M. Vermeer, Susanne E. Baumgartner
  Abstract: Social Science Computer Review, Ahead of Print.
  Social scientists often study comments on YouTube to learn about people’s attitudes towards and experiences of online videos. However, not all YouTube comments are relevant in the sense that they reflect individuals’ thoughts about, or experiences of the content of a video or its artist/maker. Therefore, the present paper employs Supervised Machine Learning to automatically assess comments written in response to music videos in terms of their relevance. For those comments that are relevant, we also assess why they are relevant. Our results indicate that most YouTube comments are relevant (approx. 78%). Among those, most are relevant because they include a positive evaluation of the video, describe a viewer’s personal experience related to the video, or express a sense of community among the video viewers. We conclude that Supervised Machine Learning is a suitable method to find those YouTube comments that are relevant to scholars studying viewers’ reactions to online videos, and we present suggestions for scholars wanting to apply the same technique in their own projects.
  Citation: Social Science Computer Review
  PubDate: 2023-05-16T07:29:05Z
  DOI: 10.1177/08944393231173895
Do Shy Individuals Engage in Cyber Aggression' The Multiple Mediation of
Passive Use and Relative Deprivation and the Moderation of Moral
Sensitivity
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  Authors: Jinzhe Zhao, Zhen Guo, Liying Jiao, Mengke Yu, Huiyue Shi, Yan Xu
  Abstract: Social Science Computer Review, Ahead of Print.
  Shyness has been shown to be linked to aggression. However, whether this relationship occurs in cyberspace and the mechanisms that might affect it are largely unexplored. Based on the social fitness model, the current study examined the relationship between shyness and cyber aggression, as well as the mediating roles of passive use and relative deprivation. Moreover, according to the integration of the social information processing model and moral domain theory, moral sensitivity serves as a moderator in the direct and indirect links between shyness and cyber aggression. A total of 700 Chinese college students (Mage = 18.68, 53.57% women) participated in the current study and completed multiple questionnaires, namely, the Shyness Scale, Cyber-Aggression Scale, Passive Use of Social Network Site Scale, Relative Deprivation Scale, and Ethical Sensitivity Scale. The results showed that shyness was positively associated with cyber aggression through the multiple mediating effects of passive use and relative deprivation. Additionally, moderated mediation analysis indicated that moral sensitivity moderated the direct and indirect relationship between shyness and cyber aggression. A high level of moral sensitivity weakened the association of shyness with cyber aggression and the association of relative deprivation with cyber aggression, supporting the moderated mediation model. This study implicates the underlying mechanisms of the relationship between shyness and cyber aggression and preventative interventions to reduce the risk of cyber aggression.
  Citation: Social Science Computer Review
  PubDate: 2023-05-16T05:28:54Z
  DOI: 10.1177/08944393231176326
Why Device-Related Digital Inequalities Matter for E-Government
Engagement'
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  Authors: Matías Dodel
  Abstract: Social Science Computer Review, Ahead of Print.
  Mobile devices were key drivers for recent Internet expansion in lower-income countries, democratizing access. Nonetheless, concerns arose regarding their role in the creation of new digital underclass related to the capital-enhancing consequences of Internet use. Among these, e-government engagement allows individuals to reduce the administrative burdens of governmental interactions. Nonetheless, its uptake has been proven to be highly stratified in Latin American countries where most services are not digital-by-default. The article argues that disparities in digital access play a role in this e-government divides. It examines the antecedents and determinants of household computer access and mobile-only Internet use, and e-government engagement in Brazil. Based on “TIC Domicilios 2019” survey, using logistic regressions to predict household access to computers, mobile-only Internet access, and e-government engagement. Mediation analyses of the latter models are conducted, testing the sequential nature of socio-digital inequalities based on the DiSTO framework. Findings show that living in a household with computers reduces the chances of being a mobile-only user and increases the odds of e-government engagement. Mobile-only access reduces e-government engagement. The effects of socioeconomic status and digital inequalities are mediated by household access to computers and mobile-only use. Implications for digital inclusion policies are discussed.
  Citation: Social Science Computer Review
  PubDate: 2023-05-15T03:08:47Z
  DOI: 10.1177/08944393231176595
Mind the Like-Minded. The Role of Social Identity in Prosocial
Crowdfunding
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  Authors: Anna Monik, Michał Parzuchowski
  Abstract: Social Science Computer Review, Ahead of Print.
  Current social challenges have increased the interest in globally spread collective actions, especially those taking place in virtual space. Crowdfunding is one form of online activism that has recently gained importance. Although research conducted so far indicates the significance of social motives among participants of crowdfunding campaigns, knowledge about the psychosocial mechanisms involved in its effectiveness is limited. This article attempts to reinforce the position of crowdfunding as one of the forms of collective action and to expand knowledge about possible psychosocial factors that could shape participation in crowdfunding campaigns. In three pre-registered studies (N = 823), we found that the social identity based on a shared worldview positively correlated with the intention to participate in prosocial crowdfunding. Moreover, the relationship between opinion-based group identity and collective action varied depending on participation type (predicted vs. experienced engagement in a campaign). In other words, when people gather in communities built around shared opinions on a given social issue, they develop a sense of community, which can translate into activities for the benefit of the group such as supporting crowdfunding campaigns. However, in the case of actual behaviour, unlike with the declaration of participation, the strength of the relationship with social identity significantly diminishes. The results are discussed in relation to the theory of collective action.
  Citation: Social Science Computer Review
  PubDate: 2023-05-04T12:28:14Z
  DOI: 10.1177/08944393231173889
Retraction Notice: “Taxation Issues for Digital Financial
Assets”
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  Abstract: Social Science Computer Review, Ahead of Print.
  
  Citation: Social Science Computer Review
  PubDate: 2023-04-26T04:41:09Z
  DOI: 10.1177/08944393231171468
From Calculations to Reasoning: History, Trends and the Potential of
Computational Ethnography and Computational Social Anthropology
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  Authors: Manolis Peponakis, Sarantos Kapidakis, Martin Doerr, Eirini Tountasaki
  Abstract: Social Science Computer Review, Ahead of Print.
  The domains of computational social anthropology and computational ethnography refer to the computational processing or computational modelling of data for anthropological or ethnographic research. In this context, the article surveys the use of computational methods regarding the production and the representation of knowledge. The ultimate goal of the study is to highlight the significance of modelling ethnographic data and anthropological knowledge by harnessing the potential of the semantic web. The first objective was to review the use of computational methods in anthropological research focusing on the last 25 years, while the second objective was to explore the potential of the semantic web focusing on existing technologies for ontological representation. For these purposes, the study explores the use of computers in anthropology regarding data processing and data modelling for more effective data processing. The survey reveals that there is an ongoing transition from the instrumentalisation of computers as tools for calculations, to the implementation of information science methodologies for analysis, deduction, knowledge representation, and reasoning, as part of the research process in social anthropology. Finally, it is highlighted that the ecosystem of the semantic web does not subserve quantification and metrics but introduces a new conceptualisation for addressing and meeting research questions in anthropology.
  Citation: Social Science Computer Review
  PubDate: 2023-04-12T07:40:46Z
  DOI: 10.1177/08944393231167692
Machines As Social Entities (MASE) Scale: Validation of a New Scale
Measuring Beliefs in the Sociality of Intelligent Machine Agents
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  Authors: Joo-Wha Hong
  Abstract: Social Science Computer Review, Ahead of Print.
  Researchers examining the social relationship between humans and machine agents have been faced with a series of obstacles, mainly due to the lack of appropriate study tools. To address this need for measurement toolkits, this article examines the development and validation of the Machines As Social Entities (MASE) scale. MASE was created to measure people’s beliefs in machine agents as social entities. Together, the results from a series of studies, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), demonstrate that the MASE is a reliable and valid measure. Potential uses of the scales are then discussed.
  Citation: Social Science Computer Review
  PubDate: 2023-04-07T12:54:41Z
  DOI: 10.1177/08944393231167211
Performative Quantification: Design Choices Impact the Lessons of
Empirical Surveys About the Ethics of Autonomous Vehicles
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  Authors: Hubert Etienne, Florian Cova
  Abstract: Social Science Computer Review, Ahead of Print.
  In recent years, researchers have emphasized the relevance of data about commonsense moral judgments for ethical decision-making, notably in the context of debates about autonomous vehicles (AVs). As such, the results of empirical studies such as the Machine Moral Experiment have been influential in debates about the ethics of AVs and some researchers have even put forward methods to automatize ethical decision-making on the basis of such data. In this paper, we argue that data collection is not a neutral process, and the difference in study design can change participants’ answers and the ethical conclusions that can be drawn from them. After showing that participants’ individual answers are stable in the sense that providing them with a second occasion to reflect on their answers does not change them (Study 1), we show that different conclusions regarding participants’ moral preferences can be reached when participants are given a third option allowing AVs to behave randomly (Study 2), and that preference for this third option can be increased in the context of a collective discussion (Study 3). We conclude that design choices will influence the lessons that can be drawn from surveys about participants’ moral judgments about AVs and that these choices are not morally neutral.
  Citation: Social Science Computer Review
  PubDate: 2023-03-21T03:23:51Z
  DOI: 10.1177/08944393231164329
Online Hate Speech as a Moral Issue: Exploring Moral Reasoning of Young
Italian Users on Social Network Sites
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  Authors: Francesca Ieracitano, Caterina Balenzano, Sabrina Girardi, Cataldo Giuliano Gemmano, Francesca Comunello
  Abstract: Social Science Computer Review, Ahead of Print.
  Taking a neo-Kohlbergian approach, we explore the moral reasoning of 486 young Italian users of social network sites exposed to moral dilemmas concerning online hate speech. The aims are to understand what moral reasoning schemas they use as they face homophobic, racist, or sexist online hate speech, and what influence personal values and moral disengagement might have on their moral reasoning process. The results reveal the prevalence of Maintaining Norms reasoning (conformity to rules and authority) in making moral decisions concerning online hate speech and confirms the mediating role of Hate Speech Moral Disengagement in the relationship between personal values and the moral reasoning process.
  Citation: Social Science Computer Review
  PubDate: 2023-03-06T10:28:06Z
  DOI: 10.1177/08944393231161124
Confirmation Bias in Seeking Climate Information: Employing Relative
Search Volume to Predict Partisan Climate Opinions
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  Authors: Yifei Wang, Kokil Jaidka
  Abstract: Social Science Computer Review, Ahead of Print.
  In an increasingly digitized world, online information-seeking (OIS) behaviors have reflected people’s intentions and constituted a critical component in synthesizing public opinion. Climate change is among the gravest threats facing the world today, and previous studies have adopted OIS data to gauge public interest in climate change. However, such studies have ignored the psychological attributes of search keywords and the role of social identities in influencing OIS. This study explores whether search strategies align with the expected confirmation biases of regions with different partisan beliefs. We use spatial web search trends to show the significant differences in the search keywords adopted by the Democrat-majority (“climate change”) versus the Republican-majority (“global warming”) regions of the United States. Furthermore, using the region-level search and survey data (2008–2018), we demonstrate that the preferential use of search keywords can predict climate opinions. This study concludes by discussing the significant findings and the open questions for future work.
  Citation: Social Science Computer Review
  PubDate: 2023-03-03T09:35:54Z
  DOI: 10.1177/08944393231160963
An Informed Neural Network for Discovering Historical Documentation
Assisting the Repatriation of Indigenous Ancestral Human Remains
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  Authors: Md Abul Bashar, Richi Nayak, Gareth Knapman, Paul Turnbull, Cressida Fforde
  Abstract: Social Science Computer Review, Ahead of Print.
  Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.
  Citation: Social Science Computer Review
  PubDate: 2023-03-01T11:31:15Z
  DOI: 10.1177/08944393231158788
Dual Identity in Repressive Contexts: An Agent-Based Model of Protest
Dynamics
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  Authors: Alexander Petrov, Andrei Akhremenko, Sergey Zheglov
  Abstract: Social Science Computer Review, Ahead of Print.
  Protest campaign movements are often carried out by coalitions rather than by homogeneous groups. Accordingly, an opposition member has both a narrow partisan identity and a broad all-opposition identity. Seeking to prevent mass political participation, autocracies can repress protesters regardless of their group membership or apply the “divide and conquer” principle, targeting specific groups. Any strategy of repression drives some dynamics of identity. For instance, broad repression may rally the opposition by increasing broad identity at the expense of narrow identity. This dynamic of identity causes complex dynamics of motivation for participation in the protest, which affects the turnout. The paper introduces a computational model to describe the dynamics of the turnout. The model employs the social identity model of collective action (SIMCA) and accounts for the dual protest identity.
  Citation: Social Science Computer Review
  PubDate: 2023-03-01T08:39:24Z
  DOI: 10.1177/08944393231159953
Do (Not!) Track Me: Relationship Between Willingness to Participate and
Sample Composition in Online Information Behavior Tracking Research
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  Authors: Teresa Gil-López, Clara Christner, Ernesto de León, Mykola Makhortykh, Aleksandra Urman, Michaela Maier, Silke Adam
  Abstract: Social Science Computer Review, Ahead of Print.
  This paper offers a critical look at the promises and drawbacks of a popular, novel data collection technique—online tracking—from the point of view of sample composition. Using data from two large-scale studies about political attitudes and information consumption behavior carried out in Germany and Switzerland, we find that the likelihood of participation in a tracking study at several critical dropout points is systematically related to the gender, age, and education of participants, with men, young, and more educated participants being less likely to dropout of the studies. Our findings also show that these patterns are incremental, as changes in sample composition accumulate over successive study stages. Political interest and ideology were also significantly related to the likelihood of participation in tracking research. The study explores some of the most common concerns associated with tracking research leading to non-participation, finding that they also differ across demographic groups. The implications of these findings are discussed.
  Citation: Social Science Computer Review
  PubDate: 2023-02-13T06:14:33Z
  DOI: 10.1177/08944393231156634
Textual Indicators of Deliberative Dialogue: A Systematic Review of
Methods for Studying the Quality of Online Dialogues
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  Authors: Alex Goddard, Alex Gillespie
  Abstract: Social Science Computer Review, Ahead of Print.
  High-quality online dialogues help sustain democracy. Deliberative theory, which predates the internet, provides the primary model for assessing the quality of online dialogues. It conceptualizes high-quality online dialogue as civil, rational, constructive, equal, interactive, and for the common good. More recently, advances in computation have driven an upsurge of empirical studies using automated methods for operationalizing online dialogue and measuring its quality. While related in their aims, deliberative theory and the wider empirical literature generally operate independently. To bridge the gap between the two literatures, we introduce Textual Indicators of Deliberative Dialogue (TIDDs). TIDDs are defined as text-based measures of online dialogue quality under a deliberative model (e.g., disagreement, incivility, and justifications). In this study, we identified 123 TIDDs by systematically reviewing 67 empirical studies of online dialogue. We found them to have mid-low reliability, low criterion validity, and high construct validity for measuring two deliberative dimensions (civility and rationality). Our results highlight the limitations of deliberative theory for conceptualizing the variety of ways online dialogues can be operationalized. We report the most promising TIDDs for measuring the quality of online dialogue and suggest deliberative theory would benefit from altering its models in line with the broader empirical literature.
  Citation: Social Science Computer Review
  PubDate: 2023-02-13T05:45:56Z
  DOI: 10.1177/08944393231156629
The Robot-Gender Divide: How and Why Men and Women Differ in Their
Attitudes Toward Social Robots
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  Authors: Elyakim Kislev
  Abstract: Social Science Computer Review, Ahead of Print.
  Recent developments foretell that social robots will soon become an integral part of everyday life, offering companionship and intimate closeness of different kinds. While research thus far is limited in scope and data, the current research offers two studies into how and why gender affects social robots’ acceptance among European and American participants. Study 1 (N = 26,344) is used to identify overall patterns, while Study 2 (N = 426), divided into quantitative and qualitative analyses, is used to investigate specific differences in accepting four types of robots: helpers, companions, lovers, and sex partners. Results show that women have significantly less positive attitudes toward social robots as lovers and sex partners than men. The qualitative analyses of Study 2 show that this is due to women seeing such robots more negatively in terms of social norms, psychological health, morality, and functionality. The study further offers an axis system, on which attitudes toward robots can be theorized and examined.
  Citation: Social Science Computer Review
  PubDate: 2023-02-06T05:26:55Z
  DOI: 10.1177/08944393231155674
Potential Pitfalls With Automatic Sentiment Analysis: The Example of
Queerphobic Bias
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  Authors: Eddie L. Ungless, Björn Ross, Vaishak Belle
  Abstract: Social Science Computer Review, Ahead of Print.
  Automated sentiment analysis can help efficiently detect trends in patients’ moods, consumer preferences, political attitudes and more. Unfortunately, like many natural language processing techniques, sentiment analysis can show bias against marginalised groups. We illustrate this point by showing how six popular sentiment analysis tools respond to sentences about queer identities, expanding on existing work on gender, ethnicity and disability. We find evidence of bias against several marginalised queer identities, including in the two models from Google and Amazon that seem to have been subject to superficial debiasing. We conclude with guidance on selecting a sentiment analysis tool to minimise the risk of model bias skewing results.
  Citation: Social Science Computer Review
  PubDate: 2023-02-03T01:23:40Z
  DOI: 10.1177/08944393231152946
Divided by the Algorithm' The (Limited) Effects of Content- and
Sentiment-Based News Recommendation on Affective, Ideological, and
Perceived Polarization
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  Authors: Katharina Ludwig, Alexander Grote, Andreea Iana, Mehwish Alam, Heiko Paulheim, Harald Sack, Christof Weinhardt, Philipp Müller
  Abstract: Social Science Computer Review, Ahead of Print.
  Recent rises in political polarization across the globe are often ascribed to algorithmic content filtering on social media, news platforms, or search engines. The widespread usage of news recommendation systems (NRS) is theorized to drive users in homogenous information environments and, thereby, drive affective, ideological, and perceived polarization. To test this assumption, we conducted an online experiment (n = 750) with running algorithms that enriches content-based NRS with negative or neutral sentiment. Our experiment finds only limited evidence for polarization effects of content-based NRS. Nevertheless, the time spent with an NRS and its recommended articles seems to play a crucial role as a moderator of polarization. The longer participants were using an NRS enriched with negative sentiment, the more they got affectively polarized, whereas participants using an NRS incorporating balanced sentiment ideologically depolarized over time. Implications for future research are discussed.
  Citation: Social Science Computer Review
  PubDate: 2023-01-09T03:53:55Z
  DOI: 10.1177/08944393221149290