Social Science Computer Review最新文献_第6页

From Search to Separation: Digital Behavioral Decoupling and the Predictive Power of Google Trends for Divorce Outcomes Across Four Western Nations 从搜索到分离：数字行为脱钩和谷歌趋势对四个西方国家离婚结果的预测能力

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-01-21 DOI: 10.1177/08944393261419796

Emre Can Kuran, Umut Kuran

{"title":"From Search to Separation: Digital Behavioral Decoupling and the Predictive Power of Google Trends for Divorce Outcomes Across Four Western Nations","authors":"Emre Can Kuran, Umut Kuran","doi":"10.1177/08944393261419796","DOIUrl":"https://doi.org/10.1177/08944393261419796","url":null,"abstract":"Digital search platforms enable real-time observation of relationship distress through behavioral traces. This study tests whether Google Trends predicts official divorce rates in the United States, Germany, the Netherlands, and the United Kingdom from 2009 to 2023. We introduce Digital Behavioral Decoupling, in which online distress signals diverge from legal outcomes as divorce shifts from an institutional procedure to an emotionally mediated digital phenomenon. Methods include unit root tests, cointegration analysis, Granger causality, spectral coherence, and rolling-origin nowcasting. Search queries are grouped into pre-divorce (cognitive distress), during-divorce (procedural action), and post-divorce (emotional recovery) phases. Results show 62.5% of terms lead divorce rates by 1–2 years, yet only 8.3% remain significant after False Discovery Rate correction. The Netherlands demonstrates 100% forecast improvements beyond autoregressive models (DM 2.87–3.43, p < 0.02) across all search terms, from early relationship therapy queries through procedural and post-divorce searches, indicating systematic capture of the entire divorce pathway. Germany shows intermediate results with 33% forecast success beyond autoregressive benchmarks (DM 2.40–2.81) limited to problem-recognition terms, suggesting episodic crisis-driven engagement. The United States and United Kingdom show no forecast gains beyond autoregressive models despite high search volumes, consistent with information saturation in normalized divorce cultures. Lead-lag relationships are frequency-specific, concentrated at 3–5 year periodicities. Findings link family sociology with affective computing and provide a replicable toolkit for tracking relationship dissolution in algorithmically curated information environments.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"396 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Capability, Opportunity, and Motivation in a Social Multiplayer Online Game: Player Influence Dynamics in Sky: Children of Light 社交多人在线游戏中的能力、机会和动机：《Sky: Children of Light》中的玩家影响动态

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-01-19 DOI: 10.1177/08944393251414170

Wen Zeng, Chandni Kumar, Sinong Zhou, Donggyu Kim, Aimei Yang, Dmitri Williams

{"title":"Capability, Opportunity, and Motivation in a Social Multiplayer Online Game: Player Influence Dynamics in Sky: Children of Light","authors":"Wen Zeng, Chandni Kumar, Sinong Zhou, Donggyu Kim, Aimei Yang, Dmitri Williams","doi":"10.1177/08944393251414170","DOIUrl":"https://doi.org/10.1177/08944393251414170","url":null,"abstract":"This study investigates networked social influence in Sky: Children of the Light , a social multiplayer online game. Drawing on survey responses ( n = 9,254) and in-game data from over 660,000 players, we use an innovative graph-based machine learning approach to quantify how individuals influence others’ playtime, and regression analyses to test predictors from the COM-B model. Results show that Capability enhances influence, although excessive task focus correlates negatively with social impact; Opportunity emerges as the strongest predictor, with active social interactions significantly boosting influence; and Motivation varies by playstyle, with socializers and competitors demonstrating greater influence than narrative-focused players. By applying the COM-B model in a digital gaming context, this research highlights behavioral dimensions of player influence and employs a novel metric for quantifying interpersonal influence. These findings suggest practical implications for game design, particularly by highlighting how social interaction opportunities and different player motivations shape influence within communities.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"48 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Dawn of Generative AI-Enabled Political Activism: How Kenyan Gen Z Used ChatGPT and Protest GPTs to Mobilize 生成人工智能支持的政治激进主义的曙光：肯尼亚Z世代如何使用聊天技术和抗议技术进行动员

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-01-14 DOI: 10.1177/08944393261416781

John Maina Karanja, Macrina Mbaika Musili

{"title":"The Dawn of Generative AI-Enabled Political Activism: How Kenyan Gen Z Used ChatGPT and Protest GPTs to Mobilize","authors":"John Maina Karanja, Macrina Mbaika Musili","doi":"10.1177/08944393261416781","DOIUrl":"https://doi.org/10.1177/08944393261416781","url":null,"abstract":"In June 2024, youth-led protests in Kenya against a controversial Finance Bill demonstrated the connection between digital technologies and political activism in the Global South. This study examines how generative artificial intelligence (GAI) shapes political participation by focusing on Kenyan Gen Z activists who used ChatGPT to create custom models: Finance_Bill_GPT, Corrupt_Politicians_GPT, and MPs_Contribution_GPT (collectively called Protest_GPT_KE). These tools simplified complex laws, exposed corruption, and mobilized young people online, allowing them to bypass traditional sources such as media and elites. However, using GAI for activism raises ethical and political concerns, including surveillance, data rights, and state repression. The study surveyed 374 Kenyan Gen Z participants, primarily in Nairobi, and used Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the connections among AI use, tool appropriation, and political participation. Results show that ChatGPT use alone did not directly increase offline activism; its effect appeared when combined with Protest_GPT_KE and online participation. This study is one of the first to document how youth in the Global South are creatively using GAI for grassroots mobilization, demonstrating that GAI’s political influence depends on user innovation and context.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"83 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Bots Through LLM-Generated Text in Open Narrative Responses: A Proof-of-Concept Study 通过法学硕士生成的文本在开放的叙事反应中识别机器人：概念验证研究

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-01-14 DOI: 10.1177/08944393251408022

Joshua Claassen, Jan Karem Höhne, Ruben Bach, Anna-Carolina Haensch

{"title":"Identifying Bots Through LLM-Generated Text in Open Narrative Responses: A Proof-of-Concept Study","authors":"Joshua Claassen, Jan Karem Höhne, Ruben Bach, Anna-Carolina Haensch","doi":"10.1177/08944393251408022","DOIUrl":"https://doi.org/10.1177/08944393251408022","url":null,"abstract":"Online survey participants are frequently recruited through social media platforms, opt-in online access panels, and river sampling approaches. Such online surveys are threatened by bots that shift survey outcomes and exploit incentives. In this proof-of-concept study, we advance the identification of bots driven by Large Language Models (LLMs) through the prediction of LLM-generated text in open narrative responses. We conducted an online survey on same-gender partnerships, including three open narrative questions, and recruited 1512 participants through Facebook. In addition, we utilized two LLM-driven bots, each of which responded to the open narrative questions 400 times. Open narrative responses synthesized by our bots were labeled as containing LLM-generated text (“yes”). Facebook responses were assigned a proxy label (“unclear”) as they may contain bots themselves. Using this binary label as ground truth, we fine-tuned prediction models relying on the “Bidirectional Encoder Representations from Transformers” (BERT) model, resulting in an impressive prediction performance: The models accurately identified between 97% and 100% of bot responses. However, prediction performance decreases if the models make predictions about questions they were not fine-tuned with. Our study contributes to the ongoing discussion on bots and extends the methodological toolkit for protecting the quality and integrity of online survey data.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

International Differences in Windows Remote Desktop Hacking: An Analysis of Honeypot Data Windows远程桌面黑客的国际差异：蜜罐数据分析

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-01-10 DOI: 10.1177/08944393261416786

Thomas E. Dearden, Andréanne Bergeron

引用次数: 0

Who Are the Online Commenters? A Large-Scale Representative Survey to Explore the Identity and Motivation of Online News Commenters in Comparison to Non-Commenters 谁是网上评论者？一项大规模的代表性调查，探讨网络新闻评论者与非评论者的身份和动机

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2025-12-30 DOI: 10.1177/08944393251410596

Liesje C. A. van der Linden, Cedric Waterschoot, Ernst van den Hemel, Florian A. Kunneman, Antal P. J. van den Bosch, Emiel J. Krahmer

{"title":"Who Are the Online Commenters? A Large-Scale Representative Survey to Explore the Identity and Motivation of Online News Commenters in Comparison to Non-Commenters","authors":"Liesje C. A. van der Linden, Cedric Waterschoot, Ernst van den Hemel, Florian A. Kunneman, Antal P. J. van den Bosch, Emiel J. Krahmer","doi":"10.1177/08944393251410596","DOIUrl":"https://doi.org/10.1177/08944393251410596","url":null,"abstract":"To better understand the demographic composition of people participating in commenting sections beneath online news articles, we conducted a large-scale survey ( n = 5,490) with a panel that is representative of the Dutch population – the LISS panel. We combined these data with demographic background variables and previously collected data on political views and values, to provide a detailed description of the identity of online news commenters in comparison to non-commenters. Our results show that the group of commenters contain more men (55%), and the age group of 45–54 years old has the largest share of commenters (18% for men, 13% for women). Furthermore, we found little to no differences for education levels, income, location, political preferences, and cultural background, suggesting that there is no striking overrepresentation of specific groups among online commenters in general. However, when looking at the profiles of online commenters as a function of the topic and platform of discussion, differences start to emerge for gender, age, and education levels. We found no differences related to age and gender distributions for those with a higher commenting frequency, but a higher frequency does go hand in hand with more support for national populist and far-right political parties and a lower confidence in political parties.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"13 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generational Divide in AI Adoption for Academic Writing: Evidence From Serbian Social Scientists 人工智能在学术写作中的代际差异：来自塞尔维亚社会科学家的证据

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2025-12-30 DOI: 10.1177/08944393251413796

Marko Galjak, Marina Budić

{"title":"Generational Divide in AI Adoption for Academic Writing: Evidence From Serbian Social Scientists","authors":"Marko Galjak, Marina Budić","doi":"10.1177/08944393251413796","DOIUrl":"https://doi.org/10.1177/08944393251413796","url":null,"abstract":"This cross-sectional study examines a generational divide in the adoption of AI for academic writing among academic researchers in Serbia. A survey of 823 social scientists analyzed usage patterns and measured age-related adoption rates through logistic regression analysis. The findings indicate that 27.2% of researchers employ AI for academic writing, with adoption rates varying significantly by age: 42.9% of researchers in their twenties use these tools, compared to 14.3% of those in their sixties. Researchers aged 23–34 were twice as likely to adopt AI writing tools as those aged 49–80. Each additional year of age reduced the odds of AI adoption by 3.8%, even when controlled for academic title, sex, and workplace type. This age effect persisted while gender and institutional context showed no significant association with adoption. The significant variation in AI adoption across age groups suggests potential shifts in academia. Senior faculty who avoid AI writing tools cannot effectively mentor graduate students who rely on them. Manuscripts now face inconsistent peer review standards; reviewers familiar with AI-assisted writing apply different criteria than those who reject it entirely. Universities face competing demands: junior researchers insist AI tools help them publish enough to secure tenure, yet senior faculty argue that students who depend on these tools never learn to construct arguments or evaluate evidence independently.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"39 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating the Past: How Artificial Intelligence Summaries of Historical Events Affect Knowledge 生成过去：人工智能对历史事件的总结如何影响知识

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2025-12-24 DOI: 10.1177/08944393251409744

Daniel Karell, Matthew Shu, Thomas Davidson, Keitaro Okura

{"title":"Generating the Past: How Artificial Intelligence Summaries of Historical Events Affect Knowledge","authors":"Daniel Karell, Matthew Shu, Thomas Davidson, Keitaro Okura","doi":"10.1177/08944393251409744","DOIUrl":"https://doi.org/10.1177/08944393251409744","url":null,"abstract":"Many people now use AI chatbots to obtain summaries of complex topics, yet we know little about how this affects knowledge acquisition, including how the effects might vary across different groups of people. We conducted two experiments comparing how well people recalled factual information after reading AI-generated or human-written historical summaries. Participants who read AI-generated summaries scored significantly higher on knowledge tests than those who read expert-written blog posts (Study 1) or Wikipedia articles (Study 2). These improvements were present regardless of whether readers knew the content was AI-generated or if the AI summaries were politically biased. Moreover, AI summaries improved recall across various demographic groups, including gender, race, income, education, and digital literacy levels. This suggets that using AI tools for everyday factual queries does not create new knowledge inequalities but could still amplify existing ones through differential access. Our findings indicate that the increasingly routine use of AI for information-seeking could enhance factual learning, with implications for education policy and addressing inequality.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"93 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Embracing Dialectic Intersubjectivity: Coordination of Differential Perspectives in Content Analysis With LLM Persona Simulation 拥抱辩证法主体间性：法学硕士角色模拟内容分析中不同视角的协调

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2025-12-23 DOI: 10.1177/08944393251410155

Taewoo Kang, Kjerstin Thorson, Tai-Quan Peng, Dan Hiaeshutter-Rice, Sanguk Lee, Stuart Soroka

{"title":"Embracing Dialectic Intersubjectivity: Coordination of Differential Perspectives in Content Analysis With LLM Persona Simulation","authors":"Taewoo Kang, Kjerstin Thorson, Tai-Quan Peng, Dan Hiaeshutter-Rice, Sanguk Lee, Stuart Soroka","doi":"10.1177/08944393251410155","DOIUrl":"https://doi.org/10.1177/08944393251410155","url":null,"abstract":"This study attempts to advance automated content analysis from consensus-oriented to coordination-oriented practices, thereby embracing diverse coding outputs and exploring the dynamics among differential perspectives. As an exploratory investigation, we evaluate six GPT-4o configurations to analyze sentiment in Fox News and MSNBC transcripts on Biden and Trump during the 2020 U.S. presidential campaign. By assessing each model’s alignment with partisan perspectives, we explore how partisan selective processing can be identified in LLM-Assisted Content Analysis (LACA). The findings indicate that LLM-based partisan perspective simulations reflect politically polarized standpoints across partisan groups, revealing a pronounced divergence in sentiment analysis between Democrat-aligned and Republican-aligned persona models. This pattern is evident in intercoder-reliability metrics, which are higher among same-partisan than cross-partisan persona model pairs. Results also suggest that LLM partisan simulations exhibit stronger ideological biases when analyzing politically congruent content. This approach enhances the nuanced understanding of LLM outputs and advances the integrity of AI-driven social science research and may also enable simulations of real-world implications.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the AI Tool “Elicit” as a Semi-Automated Second Reviewer for Data Extraction in Systematic Reviews: A Proof-of-Concept 评估人工智能工具“引出”作为系统评价中数据提取的半自动第二审稿人：概念验证

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2025-12-03 DOI: 10.1177/08944393251404052

Frederic Hilkenmeier, Marie Pelzer, Christian Stierle, Jakob Fink-Lamotte

{"title":"Evaluating the AI Tool “Elicit” as a Semi-Automated Second Reviewer for Data Extraction in Systematic Reviews: A Proof-of-Concept","authors":"Frederic Hilkenmeier, Marie Pelzer, Christian Stierle, Jakob Fink-Lamotte","doi":"10.1177/08944393251404052","DOIUrl":"https://doi.org/10.1177/08944393251404052","url":null,"abstract":"Systematic reviews are essential for evidence synthesis but often require extensive time and resources, especially during data extraction. This proof-of-concept study evaluates the performance of Elicit , an AI tool specifically developed to support systematic reviews, in the context of a systematic review on psychological factors in dermatological conditions. We compared Elicit’s automated data extraction with manually extracted data across 43 studies and 602 data points. Both were assessed against a consensus-based ground truth. Elicit achieved an overall accuracy of 81.4%, compared to 86.7% for human reviewers—a difference that was not statistically significant. In cases where Elicit and the human reviewer extracted the same information, this information was correct in 100% of instances, suggesting that agreement between human and machine may serve as a reliable proxy for validity. Based on these results, we propose a semi-automated workflow in which Elicit functions as a second reviewer, reducing workload while maintaining high data quality. Our results demonstrate that domain-specific AI tools can effectively augment data extraction in systematic reviews, especially in settings with limited time or personnel.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0