Social Science Computer Review最新文献

{"title":"Comparative Digital Political Communication: Comparisons Across Countries, Platforms, and Time","authors":"Shelley Boulianne, A. O. Larsson","doi":"10.1177/08944393241258763","DOIUrl":"https://doi.org/10.1177/08944393241258763","url":null,"abstract":"Comparative communication research needs to catch up to other disciplines. In this special issue and the associated International Communication Association preconference, we focus on comparative work related to digital political communication. This introduction argues that comparative digital political communication needs to consider comparisons across various dimensions, including countries, platforms, and time, whereas existing comparative communication research focuses on country or territorial comparison. We highlight the six submissions’ approaches to comparative work. Each submission provides at least one of these three dimensions of contrast. We conclude with a discussion of enduring gaps in this field of research, such as the lack of studies using time as a dimension of comparison. Time is crucial for understanding ever-changing digital media platforms. We also conclude by discussing some ongoing challenges in political communication research.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"141 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach","authors":"Miklós Sebők, Ákos Máté, Orsolya Ring, Viktor Kovács, Richárd Lehoczki","doi":"10.1177/08944393241259434","DOIUrl":"https://doi.org/10.1177/08944393241259434","url":null,"abstract":"The article presents an open-source and freely available natural language processing system for comparative policy studies. The CAP Babel Machine allows for the automated classification of input files based on the 21 major policy topics of the codebook of the Comparative Agendas Project (CAP). By using multilingual XLM-RoBERTa large language models, the pipeline can produce state-of-the-art level outputs for selected pairs of languages and domains (such as media or parliamentary speech). For 24 cases out of 41, the weighted macro F1 of our language-domain models surpassed 0.75 (and, for 6 language-domain pairs, 0.90). Besides macro F1, for most major topic categories, the distribution of micro F1 scores is also centered around 0.75. These results show that the CAP Babel machine is a viable alternative for human coding in terms of validity at less cost and higher reliability. The proposed research design also has significant possibilities for scaling in terms of leveraging new models, covering new languages, and adding new datasets for fine-tuning. Based on our tests on manifesto data, a different policy classification scheme, we argue that model-pipeline frameworks such as the Babel Machine can, over time, potentially replace double-blind human coding for a multitude of comparative classification problems.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"56 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Platform Convergence or Divergence? Comparing Political Ad Content Across Digital and Social Media Platforms","authors":"Travis N. Ridout, Markus Neumann, Jielu Yao, Laura M. Baum, Michael M. Franz, P. Oleinikov, Erika Franklin Fowler","doi":"10.1177/08944393241258767","DOIUrl":"https://doi.org/10.1177/08944393241258767","url":null,"abstract":"When it comes to the study of the messaging of online political campaigns, theory suggests that platform divergence should be common, but much research finds considerable convergence across platforms. In this research, we examine variation across digital and social media platforms in the types of paid campaign messages that are distributed, focusing on their goals, tone, and the partisanship of political rhetoric. We use data on the content of paid election advertisements placed on YouTube, Google search, Instagram, and Facebook during the 2020 elections in the United States, examining all federal candidates who advertised on these platforms during the final 2 months of the campaign. We find that YouTube is most distinct from the other platforms, perhaps because it most resembles television, but convergence better describes the two Meta platforms, Facebook and Instagram.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"2 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Re-mediation of Legacy and New Media on Twitter: A Six-Language Comparison of the European Social Media Discourse on Migration","authors":"Mike Farjam, Anamaria Dutceac Segesten","doi":"10.1177/08944393241246101","DOIUrl":"https://doi.org/10.1177/08944393241246101","url":null,"abstract":"Scholarly literature has demonstrated that hybridity transforms both legacy and new media, but that this change is not even. We treat social media platforms as arenas of remediation, where users share and add their own context to information produced by both media subtypes and compare social media conversations about migration in six European languages that include links to either traditional or new media during 2015–2019. We use a mix of computational and statistical methods to analyze 3.5 million (re)tweets and 500,000 links shared within them. We identify the main differences in agenda setting power, function, and tone present within tweets that include links to legacy or new media. Our results show that discourses are similar across languages but clearly different when remediating legacy and new media. Trust in legacy media is correlated with higher proportion of shared links from legacy media and reversely related to the proportion of shared links from new media sources. Considering the volume and timing of the remediated content, we conclude that legacy media retains its agenda setting power. New media linked content tends to cover migration in association to subjects such as Islam or terrorism and to express strong critical opinions against migrants/refugees. The language used is more toxic than in legacy media linked content. The tweets remediating legacy media articles covered topics like domestic or European politics, causes of refugee arrivals and procedures to give them protection. Thus, legacy and new media remediated content differs in both tone and function: toxicity is low and factuality high for content linking to legacy media, with the reverse being true for new media remediations.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Recidivism and Detection of Risk Factors Under Different Time Windows Using Machine Learning Techniques","authors":"Di Mu, Simai Zhang, Ting Zhu, Yong Zhou, Wei Zhang","doi":"10.1177/08944393241226607","DOIUrl":"https://doi.org/10.1177/08944393241226607","url":null,"abstract":"Following a comprehensive analysis of the initial three generations of prisoner risk assessment tools, the field has observed a notable prominence in the integration of fourth-generation tools and machine learning techniques. However, limited efforts have been made to address the explainability of data-driven prediction models and their connection with treatment recommendations. Our primary objective was to develop predictive models for assessing the likelihood of recidivism among prisoners released from their index incarceration within 1-year, 2-year, and 5-year timeframes. We aimed to enhance interpretability using SHapley Additive exPlanations (SHAP). We collected data from 20,457 in-prison records from February 10, 2005, to August 25, 2021, sourced from a Southwestern China prison’s data management system. Recidivism records were officially determined through data mining from an official website and combined identification data from neighboring prisons. We employed five machine learning algorithms, considering sociodemographic, physical health, psychological assessments, criminological characteristics, crime history, social support, and in-prison behaviors as factors. For interpretability, SHAP was applied to reveal feature contributions. Findings indicated that young prisoners accused of larceny, previous convictions, lower fines, and limited family support faced higher reoffending risk. Conversely, middle-aged and senior prisoners with no prior convictions, lower monthly supermarket expenses, and positive psychological test results had lower reoffending risk. We also explored interactions between significant predictive features, such as prisoner age at incarceration initiation and primary accusation, and the duration of current incarceration and cumulative prior incarcerations. Notably, our models consistently exhibited high performance, as shown by AUC on the test dataset across time windows. Interpretability results provided insights into evolving risk factors over time, valuable for intervention with high-risk individuals. These insights, with additional validation, could offer dynamic prisoner information for stakeholders. Moreover, interpretability results can be seamlessly integrated into prison and court management systems as a valuable risk assessment tool.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"13 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139532757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Americanization: Coverage of American Topics in Different Wikipedias","authors":"Piotr Konieczny, Włodzimierz Lewoniewski","doi":"10.1177/08944393231220165","DOIUrl":"https://doi.org/10.1177/08944393231220165","url":null,"abstract":"As one of the most popular sources of information in the world, Wikipedia is edited by a large, global community of contributors. User-generated nature of this online encyclopedia ensures that the information reflects a wide range of topics. Hovewer, Wikipedia articles are created and edited independently in each language version. Therefore, some topics may be presented with varying degrees of completeness depending on their importance in a particular language community. In this paper, we quantified the concept of Americanization on a global scale through comparative analysis of the coverage of American topics in different language versions of Wikipedia. For this purpose, we analyzed over 90 million Wikidata items and 40 million Wikipedia articles in 58 languages. We discussed whether Americanization is more or less dominant in different languages, regions, and cultures. We showed that the interest in American topics is not universal. Western, developed countries are more Americanized (more interested in topics related to America) than the rest of the world. This is the first global, quantitative confirmation of issues often hypothesized, or assumed, in the literature on Americanization and related phenomena. This study shows that Wikipedia and Wikidata can allow quantification of social science concepts that previously were considered not realistically measurable. Finally, the presented research is also relevant to the discourses on the biases of Wikipedia.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"93 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Likes vs. Loves (and Other Emoji Reactions): Facebook, Women, and the Gender Emoji Gap in US Election Campaigns","authors":"J. Phillips","doi":"10.1177/08944393231224535","DOIUrl":"https://doi.org/10.1177/08944393231224535","url":null,"abstract":"In 2017, Facebook’s news feed algorithm began weighting emoji reactions (e.g., love and angry) as five times more valuable than the like button. Such a change is theoretically intriguing because existing research largely suggests that women tend to use emojis more than men on social media. Within the context of political campaigns, prior work has revealed a host of other “gender gaps,” from documenting men’s and women’s differing tolerance for negative campaigns, to examining variations in online political participation and—more broadly—charting gendered imbalances in party demographic support. To date, however, no study has looked to investigate this potential gender emoji gap within the online political environment. This paper explores just such a gap, combining data across three US election cycles (2016–2020), over thirty million individual observations, and thousands of (federal and state) candidates. The data shows that women exhibited a greater preference for emoji reactions than men in response to posts from the 2016 presidential election candidates. Party, and candidate negativity, also appeared to moderate this effect. Likely due to this (moderated) gender gap, Democratic candidates continued to see a much higher proportion of emoji reactions to their posts, than Republicans in 2018, and 2020. In turn, the results offer clear evidence of a persistent emoji gender gap in US political campaigns on Facebook. Such findings strengthen our theoretical understanding of political communication and behavior online, and prompt important questions going forward for future research.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"123 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A National RDD Smartphone Web Survey: Comparison With a Large-Scale CAPI Survey","authors":"Sunwoong Kim, M. Couper","doi":"10.1177/08944393231222675","DOIUrl":"https://doi.org/10.1177/08944393231222675","url":null,"abstract":"The most important national surveys of the general population for creating official statistics or public policymaking in many countries, including South Korea, are still conducted using face-to-face interviews with household members. Recently face-to-face surveys have faced threats to data quality from decreasing response rates and rising costs of in-person visits. The COVID-19 pandemic and associated lockdown exacerbated the situation for face-to-face surveys. Survey organizations suspended fieldwork or began to explore alternate means of collecting data. One alternative was a shift to telephone surveys; however, telephone interviews have encountered similar difficulties with declining response rates and increasing costs. Could a self-administered web survey be a viable alternative to interviewer-administered modes such as telephone interviews (CATI) or face-to-face interviews (CAPI)? Smartphones may offer opportunities not offered by other modes. We conducted a smartphone web survey using SMS invitations where a sample of cell phone numbers was selected by random digit dialing (RDD) and compared it with a large-scale national face-to-face survey (CAPI) where a sample of households was selected by stratified cluster sampling. The two surveys were conducted during the COVID pandemic in the second half of 2020. The coverage and sample representation of the smartphone web survey were comparable to that of the face-to-face survey. Despite the relatively small number of respondents, the quality of the smartphone web survey was sufficient to provide accurate data and compared favorably with the CAPI survey. The smartphone web survey yielded more reports of socially undesirable attitudes and behavior than the CAPI survey. The findings will guide researchers to explore new opportunities in establishing a web survey methodology that obtains data more conveniently, efficiently, accurately, and with less cost.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"302 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpersonal and Computer-Mediated Competence for Prejudice Reduction: Learning to Interact Digitally and Physically During the Pandemic","authors":"B. Bouchillon","doi":"10.1177/08944393231219192","DOIUrl":"https://doi.org/10.1177/08944393231219192","url":null,"abstract":"As racial and ethnic diversity have increased in America, prejudice too has expanded. Citizens are more wary of immigrants, with attitudes toward Asian immigrants in particular worsening during COVID-19. Yet less is known about the prejudice directed at other immigrant groups during this period, with research suggesting that feeling capable of interacting with new people could reduce misgivings about diversity. A web survey was conducted in April of 2020 to test the potential for digital and physical social competence to improve attitudes toward Mexican immigrants, as the largest immigrant group in the United States ( N = 665). Interpersonal competence was inversely associated with prejudice toward Mexican immigrants, with interpersonal skills such as attentiveness, expressiveness, and mindfulness being especially valuable for prejudice reduction. Computer-mediated communication competence was indirectly associated with feeling less prejudiced, through interpersonal competence, and social presence also moderated the conversion of CMC competence into interpersonal competence, diminishing prejudice even further. Digital social capabilities encourage admiration and sympathy for immigrants by making users feel more capable of interacting with them locally. Networked settings now have the potential to train dissimilar users to interact together in person, as a way of reducing prejudice.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139214515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the Systematic Bias in the Accessibility and Inaccessibility of Web Scraping Content From URL-Logged Web-Browsing Digital Trace Data","authors":"Ross Dahlke, Deepak Kumar, Z. Durumeric, Jeffrey T. Hancock","doi":"10.1177/08944393231218214","DOIUrl":"https://doi.org/10.1177/08944393231218214","url":null,"abstract":"Social scientists and computer scientists are increasingly using observational digital trace data and analyzing these data post hoc to understand the content people are exposed to online. However, these content collection efforts may be systematically biased when the entirety of the data cannot be captured retroactively. We call this often unstated assumption the problematic assumption of accessibility. To examine the extent to which this assumption may be problematic, we identify 107k hard news and misinformation web pages visited by a representative panel of 1,238 American adults and record the degree to which the web pages individuals visited were accessible via successful web scrapes or inaccessible via unsuccessful scrapes. While we find that the URLs collected are largely accessible and with unrestricted content, we find there are systematic biases in which URLs are restricted, return an error, or are inaccessible. For example, conservative misinformation URLs are more likely to be inaccessible than other types of misinformation. We suggest how social scientists should capture and report digital trace and web scraping data.","PeriodicalId":506768,"journal":{"name":"Social Science Computer Review","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}