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  Subjects -> SOCIAL SERVICES AND WELFARE (Total: 193 journals)
Journal of Social Inclusion     Open Access   (Followers: 6)
Journal of Social Issues     Hybrid Journal   (Followers: 18)
Journal of Social Philosophy     Hybrid Journal   (Followers: 16)
Journal of Social Policy     Hybrid Journal   (Followers: 22)
Journal of Social Policy and Social Work in Transition     Full-text available via subscription   (Followers: 7)
Journal of Social Service Research     Hybrid Journal   (Followers: 8)
Journal of Social Work     Hybrid Journal   (Followers: 84)
Journal of Social Work Education     Hybrid Journal   (Followers: 4)
Journal of Social Work in Disability & Rehabilitation     Hybrid Journal   (Followers: 10)
Journal of Social Work Practice in the Addictions     Hybrid Journal   (Followers: 10)
Journal of the Society for Social Work and Research     Full-text available via subscription  
Just Policy: A Journal of Australian Social Policy     Full-text available via subscription   (Followers: 6)
L'Orientation scolaire et professionnelle     Open Access   (Followers: 1)
Learning in Health and Social Care     Hybrid Journal   (Followers: 12)
Maltrattamento e abuso all’infanzia     Full-text available via subscription  
Mental Health and Social Inclusion     Hybrid Journal   (Followers: 19)
Mental Health and Substance Use: dual diagnosis     Hybrid Journal   (Followers: 18)
Merrill-Palmer Quarterly     Full-text available via subscription  
Migration Action     Full-text available via subscription   (Followers: 3)
Mortality: Promoting the interdisciplinary study of death and dying     Hybrid Journal   (Followers: 7)
Mundos do Trabalho     Open Access  
National Emergency Response     Full-text available via subscription   (Followers: 3)
New Zealand Journal of Occupational Therapy     Full-text available via subscription   (Followers: 10)
Nonprofit Policy Forum     Hybrid Journal   (Followers: 3)
Nordic Social Work Research     Hybrid Journal   (Followers: 2)
Northwestern Journal of Law & Social Policy     Open Access   (Followers: 7)
Nouvelles pratiques sociales     Full-text available via subscription   (Followers: 1)
Parity     Full-text available via subscription   (Followers: 3)
Partner Abuse     Full-text available via subscription   (Followers: 4)
Pedagogia i Treball Social : Revista de Cičncies Socials Aplicades     Open Access  
Personality and Social Psychology Bulletin     Hybrid Journal   (Followers: 84)
Personality and Social Psychology Review     Hybrid Journal   (Followers: 28)
Philosophy & Social Criticism     Hybrid Journal   (Followers: 13)
Practice: Social Work in Action     Hybrid Journal   (Followers: 12)
Psychoanalytic Social Work     Hybrid Journal   (Followers: 8)
Public Policy and Aging Report     Hybrid Journal   (Followers: 2)
Qualit@s Revista Eletrônica     Open Access  
Qualitative Social Work     Hybrid Journal   (Followers: 18)
Quality in Ageing and Older Adults     Hybrid Journal   (Followers: 32)
Race and Social Problems     Hybrid Journal   (Followers: 4)
Research in Social Stratification and Mobility     Hybrid Journal   (Followers: 9)
Research on Economic Inequality     Hybrid Journal   (Followers: 4)
Research on Language and Social Interaction     Hybrid Journal   (Followers: 16)
Research on Social Work Practice     Hybrid Journal   (Followers: 52)
Review of Social Economy     Hybrid Journal   (Followers: 3)
Revista Internacional De Seguridad Social     Hybrid Journal  
Revista Katálysis     Open Access  
Revista Trabajo Social     Open Access  
Safer Communities     Hybrid Journal   (Followers: 33)
Science and Public Policy     Hybrid Journal   (Followers: 22)
Self and Identity     Hybrid Journal   (Followers: 11)
Service social     Full-text available via subscription  
Serviço Social & Sociedade     Open Access   (Followers: 1)
Sexual Abuse in Australia and New Zealand     Full-text available via subscription   (Followers: 5)
Sexualidad, Salud y Sociedad (Rio de Janeiro)     Open Access  
Social & Legal Studies     Hybrid Journal   (Followers: 9)
Social Action : The Journal for Social Action in Counseling and Psychology     Free   (Followers: 1)
Social and Personality Psychology Compass     Hybrid Journal   (Followers: 8)
Social Behavior and Personality : An International Journal     Full-text available via subscription   (Followers: 8)
Social Care and Neurodisability     Hybrid Journal   (Followers: 4)
Social Choice and Welfare     Hybrid Journal   (Followers: 8)
Social Cognition     Full-text available via subscription   (Followers: 13)
Social Compass     Hybrid Journal   (Followers: 6)
Social Influence     Hybrid Journal   (Followers: 10)
Social Justice Research     Hybrid Journal   (Followers: 11)
Social Philosophy and Policy     Full-text available via subscription   (Followers: 12)
Social Policy & Administration     Hybrid Journal   (Followers: 12)
Social Policy and Society     Hybrid Journal   (Followers: 69)
Social Science Japan Journal     Hybrid Journal   (Followers: 5)
Social Semiotics     Hybrid Journal   (Followers: 6)
Social Studies of Science     Hybrid Journal   (Followers: 19)
Social Work     Hybrid Journal   (Followers: 21)
Social Work & Social Sciences Review     Open Access   (Followers: 13)
Social Work Education: The International Journal     Hybrid Journal   (Followers: 11)
Social Work Research     Hybrid Journal   (Followers: 20)
Social Work Review     Full-text available via subscription   (Followers: 13)
Social Work With Groups     Hybrid Journal   (Followers: 7)
Sociedade e Estado     Open Access  
Sociedade em Debate     Open Access  
Society and Mental Health     Hybrid Journal   (Followers: 8)
SourceOCDE Questions sociales/Migrations/Sante     Full-text available via subscription  
Soziale Passagen     Hybrid Journal   (Followers: 1)
Sozialer Fortschritt     Full-text available via subscription   (Followers: 1)
Technical Aid to the Disabled Journal     Full-text available via subscription   (Followers: 1)
Tempo Social     Open Access   (Followers: 1)
The Milbank Quarterly     Hybrid Journal   (Followers: 10)
Third Sector Review     Full-text available via subscription   (Followers: 2)
Third World Planning Review     Hybrid Journal   (Followers: 8)
Transnational Social Review     Hybrid Journal  
unsere jugend     Full-text available via subscription  
Violence and Victims     Full-text available via subscription   (Followers: 35)
Youth Studies Australia     Full-text available via subscription   (Followers: 4)
Zeitschrift für Hochschulrecht, Hochschulmanagement und Hochschulpolitik: zfhr     Hybrid Journal   (Followers: 3)

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Journal Cover   Annals of the American Academy of Political and Social Science
  [SJR: 0.861]   [H-I: 50]   [26 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0002-7162 - ISSN (Online) 1552-3349
   Published by Sage Publications Homepage  [814 journals]
  • Big Data, Digital Media, and Computational Social Science: Possibilities
           and Perils
    • Authors: Shah, D. V; Cappella, J. N, Neuman, W. R.
      Pages: 6 - 13
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215572084
      Issue No: Vol. 659, No. 1 (2015)
  • From Big Data to Knowledge in the Social Sciences
    • Authors: Hesse, B. W; Moser, R. P, Riley, W. T.
      Pages: 16 - 32
      Abstract: One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the Big Data to Knowledge or BD2K initiative. The concept of translating "big data to knowledge" is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570007
      Issue No: Vol. 659, No. 1 (2015)
  • On Building Better Mousetraps and Understanding the Human Condition:
           Reflections on Big Data in the Social Sciences
    • Authors: Lin; J.
      Pages: 33 - 47
      Abstract: Over the past few years, we have seen the emergence of "big data": disruptive technologies that have transformed commerce, science, and many aspects of society. Despite the tremendous enthusiasm for big data, there is no shortage of detractors. This article argues that many criticisms stem from a fundamental confusion over goals: whether the desired outcome of big data use is "better science" or "better engineering." Critics point to the rejection of traditional data collection and analysis methods, confusion between correlation and causation, and an indifference to models with explanatory power. From the perspective of advancing social science, these are valid reservations. I contend, however, that if the end goal of big data use is to engineer computational artifacts that are more effective according to well-defined metrics, then whatever improves those metrics should be exploited without prejudice. Sound scientific reasoning, while helpful, is not necessary to improve engineering. Understanding the distinction between science and engineering resolves many of the apparent controversies surrounding big data and helps to clarify the criteria by which contributions should be assessed.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569174
      Issue No: Vol. 659, No. 1 (2015)
  • Building Better Models: Prediction, Replication, and Machine Learning in
           the Social Sciences
    • Authors: Hindman; M.
      Pages: 48 - 62
      Abstract: Analytic techniques developed for big data have much broader applications in the social sciences, outperforming standard regression models even—or rather especially—in smaller datasets. This article offers an overview of machine learning methods well-suited to social science problems, including decision trees, dimension reduction methods, nearest neighbor algorithms, support vector models, and penalized regression. In addition to novel algorithms, machine learning places great emphasis on model checking (through holdout samples and cross-validation) and model shrinkage (adjusting predictions toward the mean to reduce overfitting). This article advocates replacing typical regression analyses with two different sorts of models used in concert. A multi-algorithm ensemble approach should be used to determine the noise floor of a given dataset, while simpler methods such as penalized regression or decision trees should be used for theory building and hypothesis testing.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570279
      Issue No: Vol. 659, No. 1 (2015)
  • Is Bigger Always Better? Potential Biases of Big Data Derived from
           Social Network Sites
    • Authors: Hargittai; E.
      Pages: 63 - 76
      Abstract: This article discusses methodological challenges of using big data that rely on specific sites and services as their sampling frames, focusing on social network sites in particular. It draws on survey data to show that people do not select into the use of such sites randomly. Instead, use is biased in certain ways yielding samples that limit the generalizability of findings. Results show that age, gender, race/ethnicity, socioeconomic status, online experiences, and Internet skills all influence the social network sites people use and thus where traces of their behavior show up. This has implications for the types of conclusions one can draw from data derived from users of specific sites. The article ends by noting how big data studies can address the shortcomings that result from biased sampling frames.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570866
      Issue No: Vol. 659, No. 1 (2015)
  • Data-Driven Content Analysis of Social Media: A Systematic Overview of
           Automated Methods
    • Authors: Schwartz, H. A; Ungar, L. H.
      Pages: 78 - 94
      Abstract: Researchers have long measured people’s thoughts, feelings, and personalities using carefully designed survey questions, which are often given to a relatively small number of volunteers. The proliferation of social media, such as Twitter and Facebook, offers alternative measurement approaches: automatic content coding at unprecedented scales and the statistical power to do open-vocabulary exploratory analysis. We describe a range of automatic and partially automatic content analysis techniques and illustrate how their use on social media generates insights into subjective well-being, health, gender differences, and personality.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569197
      Issue No: Vol. 659, No. 1 (2015)
  • Signals of Public Opinion in Online Communication: A Comparison of Methods
           and Data Sources
    • Authors: Gonzalez-Bailon, S; Paltoglou, G.
      Pages: 95 - 107
      Abstract: This study offers a systematic comparison of automated content analysis tools. The ability of different lexicons to correctly identify affective tone (e.g., positive vs. negative) is assessed in different social media environments. Our comparisons examine the reliability and validity of publicly available, off-the-shelf classifiers. We use datasets from a range of online sources that vary in the diversity and formality of the language used, and we apply different classifiers to extract information about the affective tone in these datasets. We first measure agreement (reliability test) and then compare their classifications with the benchmark of human coding (validity test). Our analyses show that validity and reliability vary with the formality and diversity of the text; we also show that ready-to-use methods leave much space for improvement when analyzing domain-specific content and that a machine-learning approach offers more accurate predictions across communication domains.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569192
      Issue No: Vol. 659, No. 1 (2015)
  • Bad News or Mad News? Sentiment Scoring of Negativity, Fear, and Anger
           in News Content
    • Authors: Soroka, S; Young, L, Balmas, M.
      Pages: 108 - 121
      Abstract: This article examines the prevalence and nature of negativity in news content. Using dictionary-based sentiment analysis, we examine roughly fifty-five thousand front-page news stories, comparing four different affect lexicons, one for general negativity, and three capturing different measures of fear and anger. We show that fear and anger are distinct measures that capture different sentiments. It may therefore be possible to separate out fear and anger in media content, as in psychology. We also find that negativity is more strongly related to anger than to fear for each measure. This result appears to be driven by a small number of foreign policy words in the anger dictionaries, rather than an indication that negativity in U.S. coverage reflects "anger." We highlight the importance of tailoring lexicons to domains to improve construct validity when conducting dictionary-based automation. Finally, we connect these results to existing work on the impact of emotion on political preferences and reasoning.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569217
      Issue No: Vol. 659, No. 1 (2015)
  • Using Supervised Machine Learning to Code Policy Issues: Can Classifiers
           Generalize across Contexts?
    • Authors: Burscher, B; Vliegenthart, R, De Vreese, C. H.
      Pages: 122 - 131
      Abstract: Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coders. Furthermore, we investigate the capability of an automatic coding tool, which is based on supervised machine learning, to generalize across contexts. We conclude by highlighting implications for methodological advances and empirical theory testing.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569441
      Issue No: Vol. 659, No. 1 (2015)
  • Searching and Clustering Methodologies: Connecting Political Communication
           Content across Platforms
    • Authors: Driscoll, K; Thorson, K.
      Pages: 134 - 148
      Abstract: People create, consume, and share content online in increasingly complex ways, often including multiple news, entertainment, and social media platforms. This article explores methods for tracing political media content across overlapping communication infrastructures. Using the 2011 Occupy Movement protests and 2013 consumer boycotts as cases, we illustrate methods for creating integrated datasets of political event-related social media content by (1) using fixed URLs to link posts across platforms (URL-based integration) and (2) using semiautomated text clustering to identify similar posts across social networking services (thematic integration). These approaches help to reveal biases in the way that we characterize political communication practices that may occur when we focus on a single platform in isolation.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570570
      Issue No: Vol. 659, No. 1 (2015)
  • Candidate Networks, Citizen Clusters, and Political Expression: Strategic
           Hashtag Use in the 2010 Midterms
    • Authors: Bode, L; Hanna, A, Yang, J, Shah, D. V.
      Pages: 149 - 165
      Abstract: Twitter provides a direct method for political actors to connect with citizens, and for those citizens to organize into online clusters through their use of hashtags (i.e., a word or phrase marked with # to identify an idea or topic and facilitate a search for it). We examine the political alignments and networking of Twitter users, analyzing 9 million tweets produced by more than 23,000 randomly selected followers of candidates for the U.S. House and Senate and governorships in 2010. We find that Twitter users in that election cycle did not align in a simple Right-Left division; rather, five unique clusters emerged within Twitter networks, three of them representing different conservative groupings. Going beyond discourses of fragmentation and polarization, certain clusters engaged in strategic expression such as "retweeting" (i.e., sharing someone else’s tweet with one’s followers) and "hashjacking" (i.e., co-opting the hashtags preferred by political adversaries). We find the Twitter alignments in the political Right were more nuanced than those on the political Left and discuss implications of this behavior in relation to the rise of the Tea Party during the 2010 elections.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716214563923
      Issue No: Vol. 659, No. 1 (2015)
  • Online Fragmentation in Wartime: A Longitudinal Analysis of Tweets about
           Syria, 2011-2013
    • Authors: Freelon, D; Lynch, M, Aday, S.
      Pages: 166 - 179
      Abstract: Theorists have long predicted that like-minded individuals will tend to use social media to self-segregate into enclaves and that this tendency toward homophily will increase over time. Many studies have found moment-in-time evidence of network homophily, but very few have been able to directly measure longitudinal changes in the diversity of social media users’ habits. This is due in part to a lack of appropriate tools and methods for such investigations. This study takes a step toward developing those methods. Drawing on the complete historical record of public retweets posted between January 2011 and August 2013, we propose and justify a partial method of measuring increases or decreases in network homophily. We demonstrate that Twitter network communities that focused on Syria are in general highly fragmented and homophilous; however, only one of the nine detected network communities that persisted over time exhibited a clear increase in homophily.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716214563921
      Issue No: Vol. 659, No. 1 (2015)
  • Individual Motivations and Network Effects: A Multilevel Analysis of the
           Structure of Online Social Relationships
    • Authors: Welles, B. F; Contractor, N.
      Pages: 180 - 190
      Abstract: This article explores the relative influence of individual and network-level effects on the emergence of online social relationships. Using network modeling and data drawn from logs of social behavior inside the virtual world Second Life, we combine individual- and network-level theories into an integrated model of online social relationship formation. Results reveal that time spent online and the network pressure toward balance (individuals tending to form relationships with others who have relationships in common) predict the emergence of online relationship ties, while gender, age, proximity, homophily (the tendency of individuals to form relationships among people with similar traits), and preferential attachment are not significant predictors within the observed networks. We discuss these results in light of existing research on online social relationships and describe how digital data and network analytics enable novel insights about the emergence of online social relationships.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716214565755
      Issue No: Vol. 659, No. 1 (2015)
  • What Social Media Data We Are Missing and How to Get It
    • Authors: Resnick, P; Adar, E, Lampe, C.
      Pages: 192 - 206
      Abstract: Most electronic behavior traces available to social scientists offer a site-centric view of behavior. We argue that to understand patterns of interpersonal communication and media consumption, a more person-centric view is needed. The ideal research platform would capture reading as well as writing and friending, behavior across multiple sites, and demographic and psychographic variables. It would also offer opportunities for researchers to make interventions that make changes and additions to the information presented to people in social media interfaces. Any attempt to create such an ideal platform will have to make compromises because of engineering and privacy constraints. We describe one attempt to navigate those tensions: the MTogether project will recruit a panel of participants who will install a browser extension and mobile app that enable limited data collection and interventions.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570006
      Issue No: Vol. 659, No. 1 (2015)
  • The Dynamics of Issue Frame Competition in Traditional and Social Media
    • Authors: Guggenheim, L; Jang, S. M, Bae, S. Y, Neuman, W. R.
      Pages: 207 - 224
      Abstract: This study examines the dynamics of the framing of mass shooting incidences in the U.S. occurring in the traditional commercial online news media and Twitter. We demonstrate that there is a dynamic, reciprocal relationship between the attention paid to different aspects of mass shootings in online news and in Twitter: tweets tend to be responsive to traditional media reporting, but traditional media framing of these incidents also seems to resonate from public framing in the Twitterverse. We also explore how different frames become prominent as they compete among media as time passes after shooting events. Finally, we find that key differences emerge between norms of journalistic routine and how users rely on Twitter to express their reactions to these tragic shooting incidents.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570549
      Issue No: Vol. 659, No. 1 (2015)
  • The Power of Television Images in a Social Media Age: Linking
           Biobehavioral and Computational Approaches via the Second Screen
    • Authors: Shah, D. V; Hanna, A, Bucy, E. P, Wells, C, Quevedo, V.
      Pages: 225 - 245
      Abstract: There is considerable controversy surrounding the study of presidential debates, particularly efforts to connect their content and impact. Research has long debated whether the citizenry reacts to what candidates say, how they say it, or simply how they appear. This study uses detailed coding of the first 2012 debate between Barack Obama and Mitt Romney to test the relative influence of the candidates’ verbal persuasiveness and nonverbal features on viewers’ "second screen" behavior—their use of computers, tablets, and mobile phones to enhance or extend the televised viewing experience. To examine these relationships, we merged two datasets: (1) a shot-by-shot content analysis coded for functional, tonal, and visual elements of both candidates’ communication behavior during the debate; and (2) corresponding real-time measures, synched and lagged, of the volume and sentiment of Twitter expression about Obama and Romney. We find the candidates’ facial expressions and physical gestures to be more consistent and robust predictors of the volume and valence of Twitter expression than candidates’ persuasive strategies, verbal utterances, and voice tone during the debate.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569220
      Issue No: Vol. 659, No. 1 (2015)
  • The Network of Celebrity Politics: Political Implications of Celebrity
           Following on Twitter
    • Authors: Park, S; Lee, J, Ryu, S, Hahn, K. S.
      Pages: 246 - 258
      Abstract: With the rise of networked media such as Twitter, celebrities’ ability to speak on policy matters directly to the public has become amplified. We investigate the political implications of celebrity activism on Twitter by estimating the political ideology of thirty-four South Korean news outlets and fourteen political celebrities based on the co-following pattern among 1,868,587 Twitter users. We also had a rare opportunity to match their following behavior with individual-level attributes by relying on supplementary survey data on 11,953 members of an online survey panel. Our results reveal that celebrity following on Twitter is ideologically skewed; a vast majority of Korean Twitter users following politically influential celebrities are liberal. Additionally, survey results show that political celebrities are more likely to attract those lacking the ability to process one-sided information in a balanced manner.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569226
      Issue No: Vol. 659, No. 1 (2015)
  • Automating Open Science for Big Data
    • Authors: Crosas, M; King, G, Honaker, J, Sweeney, L.
      Pages: 260 - 273
      Abstract: The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570847
      Issue No: Vol. 659, No. 1 (2015)
  • Big Data under the Microscope and Brains in Social Context: Integrating
           Methods from Computational Social Science and Neuroscience
    • Authors: O'Donnell, M. B; Falk, E. B.
      Pages: 274 - 289
      Abstract: Methods for analyzing neural and computational social science data are usually used by different types of scientists and generally seen as distinct, but they strongly complement one another. Computational social science methodologies can strengthen and contextualize individual-level analysis, specifically our understanding of the brain. Neuroscience can help to unpack the mechanisms that lead from micro- through meso- to macro-level observations. Integrating levels of analysis is essential to unified progress in social research. We present two example areas that illustrate this integration. First, combining egocentric social network data with neural variables from the "egos" provides insight about why and for whom certain types of antismoking messages may be more or less effective. Second, combining tools from natural language processing with neuroimaging reveals mechanisms involved in successful message propagation, and suggests links from microscopic to macroscopic scales.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215569446
      Issue No: Vol. 659, No. 1 (2015)
  • Constructing Recommendation Systems for Effective Health Messages Using
           Content, Collaborative, and Hybrid Algorithms
    • Authors: Cappella, J. N; Yang, S, Lee, S.
      Pages: 290 - 306
      Abstract: Theoretical and empirical approaches to the design of effective messages to increase healthy and reduce risky behavior have shown only incremental progress. This article explores approaches to the development of a "recommendation system" for archives of public health messages. Recommendation systems are algorithms operating on dense data involving both individual preferences and objective message features. Their goal is to predict ratings for items (i.e., messages) not previously seen by the user on content similarity, prior preference patterns, or their combination. Standard approaches to message testing and research, while making progress, suffer from very slow accumulation of knowledge. This article seeks to leapfrog conventional models of message research, taking advantage of modeling developments in recommendation systems from the commercial arena. After sketching key components in developing recommendation algorithms, this article concludes with reflections on the implications of these approaches in both theory development and application.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570573
      Issue No: Vol. 659, No. 1 (2015)
  • Content Analysis and the Algorithmic Coder: What Computational Social
           Science Means for Traditional Modes of Media Analysis
    • Authors: Zamith, R; Lewis, S. C.
      Pages: 307 - 318
      Abstract: To deal with ever-larger datasets, media scholars are increasingly using computational analytic methods. This article focuses on how the traditional (manual) approach to conducting a content analysis—a primary method in the study of media messages—is being reconfigured, assesses what is gained and lost in turning to computational solutions, and builds on a "hybrid" approach to content analysis. We argue that computational methods are most fruitful when variables are readily identifiable in texts and when source material is easily parsed. Manual methods, though, are most appropriate for complex variables and when source material is not well digitized. These modes can be effectively combined throughout the process of content analysis to facilitate expansive and powerful analyses that are reliable and meaningful.
      PubDate: 2015-04-09T21:00:27-07:00
      DOI: 10.1177/0002716215570576
      Issue No: Vol. 659, No. 1 (2015)
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