Social Science Computer Review最新文献_第5页

Combining Natural Language Processing and Statistical Methods to Assess Gender Gaps in the Mediated Personalization of Politics 结合自然语言处理和统计方法，评估政治个性化中介中的性别差距

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-31 DOI: 10.1177/08944393241269097

Emanuele Brugnoli, Rosaria Simone, Marco Delmastro

{"title":"Combining Natural Language Processing and Statistical Methods to Assess Gender Gaps in the Mediated Personalization of Politics","authors":"Emanuele Brugnoli, Rosaria Simone, Marco Delmastro","doi":"10.1177/08944393241269097","DOIUrl":"https://doi.org/10.1177/08944393241269097","url":null,"abstract":"The media attention to the personal sphere of famous and important individuals has become a key element of the gender narrative. In this setting, we aim at assessing gender gaps in the mediated personalization of a wide range of political office holders in Italy during the period 2017–2020 by means of a combination of NLP and statistical methods. The proposed analysis hinges on the definition of a new score for each word in the corpus that adjusts the incidence rate for the under representation of women in politics. On this basis, evidence is found that political personalization in Italy is more detrimental for women than it is for men, with the persistence of entrenched stereotypes including a masculine connotation of leadership, the resulting women’s unsuitability to hold political functions, and a greater deal of focus on their attractiveness and body parts. In addition, women politicians are covered with a more negative tone than their men counterpart when personal details are reported. By distinguishing between different types of media, we also show that the observed gender differences are primarily found in online news rather than print news. This suggests that the expression of certain stereotypes may be favored when click baiting and personal targeting have a major impact.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"178 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141877344","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

How Algorithms Promote Self-Radicalization: Audit of TikTok’s Algorithm Using a Reverse Engineering Method 算法如何促进自我激进化？使用逆向工程方法审计 TikTok 算法

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-30 DOI: 10.1177/08944393231225547

Donghee Shin, Kulsawasd Jitkajornwanich

{"title":"How Algorithms Promote Self-Radicalization: Audit of TikTok’s Algorithm Using a Reverse Engineering Method","authors":"Donghee Shin, Kulsawasd Jitkajornwanich","doi":"10.1177/08944393231225547","DOIUrl":"https://doi.org/10.1177/08944393231225547","url":null,"abstract":"Algorithmic radicalization is the idea that algorithms used by social media platforms push people down digital “rabbit holes” by framing personal online activity. Algorithms control what people see and when they see it and learn from their past activities. As such, people gradually and subconsciously adopt the ideas presented to them by the rabbit hole down which they have been pushed. In this study, TikTok’s role in fostering radicalized ideology is examined to offer a critical analysis of the state of radicalism and extremism on platforms. This study conducted an algorithm audit of the role of radicalizing information in social media by examining how TikTok’s algorithms are being used to radicalize, polarize, and spread extremism and societal instability. The results revealed that the pathways through which users access far-right content are manifold and that a large portion of the content can be ascribed to platform recommendations through radicalization pipelines. Algorithms are not simple tools that offer personalized services but rather contributors to radicalism, societal violence, and polarization. Such personalization processes have been instrumental in how artificial intelligence (AI) has been deployed, designed, and used to the detrimental outcomes that it has generated. Thus, the generation and adoption of extreme content on TikTok are, by and large, not only a reflection of user inputs and interactions with the platform but also the platform’s ability to slot users into specific categories and reinforce their ideas.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"26 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857972","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

Tracking Census Online Self-Completion Using Twitter Posts 利用 Twitter 帖子跟踪人口普查在线自我填写情况

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-30 DOI: 10.1177/08944393241268461

Mao Li, Frederick Conrad

{"title":"Tracking Census Online Self-Completion Using Twitter Posts","authors":"Mao Li, Frederick Conrad","doi":"10.1177/08944393241268461","DOIUrl":"https://doi.org/10.1177/08944393241268461","url":null,"abstract":"From the start of data collection for the 2020 US Census, official and celebrity users tweeted about the importance of everyone being counted in the Census and urged followers to complete the questionnaire (so-called social media campaign.) At the same time, social media posts expressing skepticism about the Census became increasingly common. This study distinguishes between different prototypical Twitter user groups and investigates their possible impact on (online) self-completion rate for the 2020 Census, according to Census Bureau data. Using a network analysis method, Community Detection, and a clustering algorithm, Latent Dirichlet Allocation (LDA), three prototypical user groups were identified: “Official Government Agency,” “Census Advocate,” and “Census Skeptic.” The prototypical Census Skeptic user was motivated by events about which an influential person had tweeted (e.g., “Republicans in Congress signal Census cannot take extra time to count”). This group became the largest one over the study period. The prototypical Census Advocate was motivated more by official tweets and was more active than the prototypical Census Skeptic. The Official Government Agency user group was the smallest of the three, but their messages—primarily promoting completion of the Census—seemed to have been amplified by Census Advocate, especially celebrities and politicians. We found that the daily size of the Census Advocate user group—but not the other two—predicted the 2020 Census online self-completion rate within five days after a tweet was posted. This finding suggests that the Census social media campaign was successful in promoting completion, apparently due to the help of Census Advocate users who encouraged people to fill out the Census and amplified official tweets. This finding demonstrates that a social media campaign can positively affect public behavior regarding an essential national project like the Decennial Census.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"81 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857928","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

A Transformer Model for Manifesto Classification Using Cross-Context Training: An Ecuadorian Case Study 利用跨语境训练进行宣言分类的转换器模型：厄瓜多尔案例研究

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-24 DOI: 10.1177/08944393241266220

Fernanda Barzallo, Maria Baldeon-Calisto, Margorie Pérez, Maria Emilia Moscoso, Danny Navarrete, Daniel Riofrío, Pablo Medina-Peréz, Susana K Lai-Yuen, Diego Benítez, Noel Peréz, Ricardo Flores Moyano, Mateo Fierro

{"title":"A Transformer Model for Manifesto Classification Using Cross-Context Training: An Ecuadorian Case Study","authors":"Fernanda Barzallo, Maria Baldeon-Calisto, Margorie Pérez, Maria Emilia Moscoso, Danny Navarrete, Daniel Riofrío, Pablo Medina-Peréz, Susana K Lai-Yuen, Diego Benítez, Noel Peréz, Ricardo Flores Moyano, Mateo Fierro","doi":"10.1177/08944393241266220","DOIUrl":"https://doi.org/10.1177/08944393241266220","url":null,"abstract":"Content analysis of political manifestos is necessary to understand the policies and proposed actions of a party. However, manually labeling political texts is time-consuming and labor-intensive. Transformer networks have become essential tools for automating this task. Nevertheless, these models require extensive datasets to achieve good performance. This can be a limitation in manifesto classification, where the availability of publicly labeled datasets can be scarce. To address this challenge, in this work, we developed a Transformer network for the classification of manifestos using a cross-domain training strategy. Using the database of the Comparative Manifesto Project, we implemented a fractional factorial experimental design to determine which Spanish-written manifestos form the best training set for Ecuadorian manifesto labeling. Furthermore, we statistically analyzed which Transformer architecture and preprocessing operations improve the model accuracy. The results indicate that creating a training set with manifestos from Spain and Uruguay, along with implementing stemming and lemmatization preprocessing operations, produces the highest classification accuracy. In addition, we found that the DistilBERT and RoBERTa transformer networks perform statistically similarly and consistently well in manifesto classification. Using the cross-context training strategy, DistilBERT and RoBERTa achieve 60.05% and 57.64% accuracy, respectively, in the classification of the Ecuadorian manifesto. Finally, we investigated the effect of the composition of the training set on performance. The experiments demonstrate that training DistilBERT solely with Ecuadorian manifestos achieves the highest accuracy and F1-score. Furthermore, in the absence of the Ecuadorian dataset, competitive performance is achieved by training the model with datasets from Spain and Uruguay.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"53 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755367","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

Online Harassment: The Mediating and Moderating Role of Thoughtfully Reflective Decision-Making 网络骚扰：深思熟虑的反思性决策的中介和调节作用

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-20 DOI: 10.1177/08944393241261983

C. Jordan Howell, Saeed Kabiri, Fangzhou Wang, Caitlyn N. Muniz, Eden Kamar, Mahmoud Sharepour, John Cochran, Seyyedeh Masoomeh (Shamila) Shadmanfaat

{"title":"Online Harassment: The Mediating and Moderating Role of Thoughtfully Reflective Decision-Making","authors":"C. Jordan Howell, Saeed Kabiri, Fangzhou Wang, Caitlyn N. Muniz, Eden Kamar, Mahmoud Sharepour, John Cochran, Seyyedeh Masoomeh (Shamila) Shadmanfaat","doi":"10.1177/08944393241261983","DOIUrl":"https://doi.org/10.1177/08944393241261983","url":null,"abstract":"The current study employs a construct from the criminological literature, thoughtfully reflective decision-making (TRDM), to understand cyber offenders’ decision-making and offer relevant insights to prevent online harassment. Using a sample of Iranian high school students ( N = 366), we employ OLS and SEM to test whether and how TRDM, perceived deterrence, and prior victimization influence the most common forms of online harassment: cyberbullying and cyberstalking. Findings demonstrate cyberbullying and cyberstalking victimization increase engagement in offending behavior while participants’ fear of sanction reduces engagement in both cyberbullying and cyberstalking perpetration. Notably, results demonstrate that TRDM has a direct, mediating, and moderating effect on both forms of offending. TRDM also has an indirect effect on cyberbullying and cyberstalking perpetration through victimization and participants’ perceptions of sanction. Unlike contemporary, pre-dispositional theories of crime, TRDM is dynamic and can be improved via educational programming. We posit that current cyber hygiene campaigns should include elements aimed to improve individuals’ cognitive decision-making capabilities. Guided by theory, and based on the results of the current study, this translational approach could prevent victimization while simultaneously improving other elements of the participants’ life.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"136 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730514","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

Typing or Speaking? Comparing Text and Voice Answers to Open Questions on Sensitive Topics in Smartphone Surveys 打字还是说话？比较智能手机调查中对敏感话题开放式问题的文字和语音回答

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-05-28 DOI: 10.1177/08944393231160961

Jan Karem Höhne, Konstantin Gavras, Joshua Claassen

{"title":"Typing or Speaking? Comparing Text and Voice Answers to Open Questions on Sensitive Topics in Smartphone Surveys","authors":"Jan Karem Höhne, Konstantin Gavras, Joshua Claassen","doi":"10.1177/08944393231160961","DOIUrl":"https://doi.org/10.1177/08944393231160961","url":null,"abstract":"The smartphone increase in web surveys, coupled with technological developments, provides novel opportunities for measuring attitudes. For example, smartphones allow the collection of voice instead of text answers by using the built-in microphone. This may facilitate answering questions with open answer formats resulting in richer information and higher data quality. So far, there is only a little body of research investigating voice and text answers to open questions. In this study, we therefore compare the linguistic and content characteristics of voice and text answers to open questions on sensitive topics. For this purpose, we ran an experiment in a smartphone survey ( N = 1001) and randomly assigned respondents to an answer format condition (text or voice). The findings indicate that voice answers have a higher number of words and a higher number of topics than their text counterparts. We find no differences regarding sentiments (or extremity of answers). Our study provides new insights into the linguistic and content characteristics of voice and text answers. Furthermore, it helps to evaluate the usefulness and usability of voice answers for future smartphone surveys.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"40 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165132","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

Estimating Measurement Quality in Digital Trace Data and Surveys Using the MultiTrait MultiMethod Model 使用多特征多方法模型估算数字痕迹数据和调查的测量质量

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-05-22 DOI: 10.1177/08944393241254464

Alexandru Cernat, Florian Keusch, Ruben L. Bach, Paulina K. Pankowska

引用次数: 0

Adaptive Self-Reflection as a Social Media Self-Effect: Insights from Computational Text Analyses of Self-Disclosures of Unreported Sexual Victimization in a Hashtag Campaign 作为社交媒体自我效应的适应性自我反思：对标签活动中未报告性侵害的自我披露进行计算文本分析的启示

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-05-21 DOI: 10.1177/08944393241252640

Tien Ee Dominic Yeo, Tsz Hang Chu

{"title":"Adaptive Self-Reflection as a Social Media Self-Effect: Insights from Computational Text Analyses of Self-Disclosures of Unreported Sexual Victimization in a Hashtag Campaign","authors":"Tien Ee Dominic Yeo, Tsz Hang Chu","doi":"10.1177/08944393241252640","DOIUrl":"https://doi.org/10.1177/08944393241252640","url":null,"abstract":"Hashtag campaigns calling out sexual violence and rape myths offer a unique context for disclosing sexual victimization on social media. This study investigates the applicability of adaptive self-reflection as a potential self-effect from such public disclosures of unreported sexual victimization experiences by analyzing 92,583 tweets that invoked #WhyIDidntReport. A supervised machine learning classifier determined that 61.8% of the tweets were self-disclosures of sexual victimization. Linguistic Inquiry and Word Count (LIWC) analysis showed statistically significant differences in four psycholinguistic dimensions (greater use of past focus, cognitive processes, insight, and causation words) connected with reflective processing in tweets with self-disclosed sexual victimization compared to those without. Additionally, topic modeling and thematic analysis identified nine salient topics within the self-disclosing tweets, comprising three self-distanced representations (i.e., relatively abstract and insightful construals) of the unwanted experiences: (a) acknowledging one’s previously unacknowledged victimization, (b) reaffirming one’s rationale for not reporting, and (c) decrying invalidating response to one’s disclosure. Moving beyond reception effects and social support in extant research about social media as a coping tool, this study provides new empirical insights into the potential of social media to promote expressive meaning-making of upsetting and traumatic experiences in ways that support recovery and resilience.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"40 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079280","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

Personal, Private, Emotional? How Political Parties Use Personalization Strategies on Facebook in the 2014 and 2019 EP Election Campaigns 个人、隐私、情感？政党如何在 2014 年和 2019 年欧洲议会选举活动中使用 Facebook 上的个性化策略

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-05-17 DOI: 10.1177/08944393241254807

Uta Russmann, Ulrike Klinger, Karolina Koc-Michalska

引用次数: 0

Incivility in Comparison: How Context, Content, and Personal Characteristics Predict Exposure to Uncivil Content 比较中的不文明行为：情境、内容和个人特征如何预测不文明内容的暴露程度

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-05-14 DOI: 10.1177/08944393241252638

Felix Schmidt, Sebastian Stier, Lukas Otto

{"title":"Incivility in Comparison: How Context, Content, and Personal Characteristics Predict Exposure to Uncivil Content","authors":"Felix Schmidt, Sebastian Stier, Lukas Otto","doi":"10.1177/08944393241252638","DOIUrl":"https://doi.org/10.1177/08944393241252638","url":null,"abstract":"Incivility, that is, the breaking of social norms of conversation, is evidently prevalent in online political communication. While a growing literature provides evidence on the prevalence of incivility in different online venues, it is still unclear where and to what extent Internet users are exposed to incivility. This paper takes a comparative approach to assess the levels of incivility across contexts, content and personal characteristics. The pre-registered analysis uses detailed web browsing histories, including public Facebook posts and tweets seen by study participants, in combination with surveys collected during the German federal election 2021 ( N = 739). The level of incivility is predicted using Google’s Perspective API and compared across contexts (platforms and campaign periods), content features, and individual-level variables. The findings show that incivility is particularly strong on Twitter and more prevalent in comments than original posts/tweets on Facebook and Twitter. Content featuring political content and actors is more uncivil, whereas personal characteristics are less relevant predictors. The finding that user-generated political content is the most likely source of individuals’ exposure to incivility adds to the understanding of social media’s impact on public discourse.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"124 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942985","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