Pub Date : 2024-09-10DOI: 10.1177/08944393241275641
Ali Unlu, Sophie Truong, Nitin Sawhney, Tuukka Tammi
This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was implemented to distinguish COVID-19 specific bot accounts from regular bots. Network analysis illuminated the communication patterns of COVID-19 bots within retweet and mention networks. The findings reveal that these bots exhibit distinct characteristics and tactics that enable them to influence public discourse, particularly showing an increased activity in COVID-19-related conversations. Topic modeling analysis uncovers that COVID-19 bots predominantly focused on themes such as safety, political/conspiracy theories, and personal choice. The study highlights the critical need to develop effective strategies for detecting and countering bot influence. Essential actions include using clear and concise language in health communications, establishing strategic partnerships during crises, and ensuring the authenticity of user accounts on digital platforms. The findings underscore the pivotal role of bots in propagating misinformation related to COVID-19 and vaccines, highlighting the necessity of identifying and mitigating bot activities for effective intervention.
{"title":"Unveiling the Veiled Threat: The Impact of Bots on COVID-19 Health Communication","authors":"Ali Unlu, Sophie Truong, Nitin Sawhney, Tuukka Tammi","doi":"10.1177/08944393241275641","DOIUrl":"https://doi.org/10.1177/08944393241275641","url":null,"abstract":"This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was implemented to distinguish COVID-19 specific bot accounts from regular bots. Network analysis illuminated the communication patterns of COVID-19 bots within retweet and mention networks. The findings reveal that these bots exhibit distinct characteristics and tactics that enable them to influence public discourse, particularly showing an increased activity in COVID-19-related conversations. Topic modeling analysis uncovers that COVID-19 bots predominantly focused on themes such as safety, political/conspiracy theories, and personal choice. The study highlights the critical need to develop effective strategies for detecting and countering bot influence. Essential actions include using clear and concise language in health communications, establishing strategic partnerships during crises, and ensuring the authenticity of user accounts on digital platforms. The findings underscore the pivotal role of bots in propagating misinformation related to COVID-19 and vaccines, highlighting the necessity of identifying and mitigating bot activities for effective intervention.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"82 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142166075","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}
Pub Date : 2024-09-04DOI: 10.1177/08944393241279418
Nils Brandenstein, Christian Montag, Cornelia Sindermann
Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studies using SMD to extract individuals’ political opinions are limited and differ greatly in their methodological approaches and levels of success. Recent studies highlight the benefits of analyzing individuals’ social media network structure to estimate political opinions. Nevertheless, current methodologies exhibit limitations, including the use of simplistic linear models and a predominant focus on samples from the United States. Addressing these issues, we employ an unsupervised Variational Autoencoder (VAE) machine learning model to extract individual opinion estimates from SMD of N = 276 008 German Twitter (now called ’X’) users, compare its performance to a linear model and validate model estimates on self-reported opinion measures. Our findings suggest that the VAE captures Twitter users’ network structure more precisely, leading to higher accuracy in following decision predictions and associations with self-reported political ideology and voting intentions. Our study emphasizes the need for advanced analytical approaches capable to capture complex relationships in social media networks when studying political opinion, at least in non-US contexts.
{"title":"To Follow or Not to Follow: Estimating Political Opinion From Twitter Data Using a Network-Based Machine Learning Approach","authors":"Nils Brandenstein, Christian Montag, Cornelia Sindermann","doi":"10.1177/08944393241279418","DOIUrl":"https://doi.org/10.1177/08944393241279418","url":null,"abstract":"Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studies using SMD to extract individuals’ political opinions are limited and differ greatly in their methodological approaches and levels of success. Recent studies highlight the benefits of analyzing individuals’ social media network structure to estimate political opinions. Nevertheless, current methodologies exhibit limitations, including the use of simplistic linear models and a predominant focus on samples from the United States. Addressing these issues, we employ an unsupervised Variational Autoencoder (VAE) machine learning model to extract individual opinion estimates from SMD of N = 276 008 German Twitter (now called ’X’) users, compare its performance to a linear model and validate model estimates on self-reported opinion measures. Our findings suggest that the VAE captures Twitter users’ network structure more precisely, leading to higher accuracy in following decision predictions and associations with self-reported political ideology and voting intentions. Our study emphasizes the need for advanced analytical approaches capable to capture complex relationships in social media networks when studying political opinion, at least in non-US contexts.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142553","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}
Pub Date : 2024-09-02DOI: 10.1177/08944393241268526
Mikyung Chang
This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.
{"title":"Does the Media’s Partisanship Influence News Coverage on Artificial Intelligence Issues? Media Coverage Analysis on Artificial Intelligence Issues","authors":"Mikyung Chang","doi":"10.1177/08944393241268526","DOIUrl":"https://doi.org/10.1177/08944393241268526","url":null,"abstract":"This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"18 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123492","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}
Pub Date : 2024-08-29DOI: 10.1177/08944393241279422
Meredith E. David, James A. Roberts
Phubbing (phone snubbing) has become the norm in (im)polite society. A vast majority of US adults report using their phones during a recent social interaction. Using one’s phone in the presence of others has been shown to have a negative impact on relationships among co-workers, friends, family, and romantic partners. Recent research suggests viewing short-form videos (SFVs) (e.g., TikTok) is more addictive/immersive than traditional social media (e.g., Facebook) leading to a greater likelihood of phubbing others. Across two studies, the present research investigates the relationship between SFV viewing and phubbing and the possible mediating effect of self-control. We also test whether TikTok has a stronger relationship with phubbing than Instagram Reels and YouTube Shorts, two popular SFV purveyors. Study 1 (282 college students) finds that viewing TikTok videos is positively associated with phubbing others and this relationship is mediated by self-control. Interestingly, Study 1 also finds that this relationship does not hold for Instagram Reels and YouTube shorts. Using two different measures of self-control, Study 2 (198 adults) provides additional support for the mediating effect of self-control on the SFV viewing—phubbing relationship. Again, the model is only supported for TikTok SFV viewing, not Instagram or YouTube. In sum, the viewing of carefully curated short TikTok videos, often 30–60 seconds in length, undermines self-control which is associated with increased phubbing behavior. Implications of the present study’s findings expand far beyond phubbing. Self-control plays a central role in nearly all human decision making and behavior. Suggestions for future research are offered.
{"title":"TikTok Brain: An Investigation of Short-Form Video Use, Self-Control, and Phubbing","authors":"Meredith E. David, James A. Roberts","doi":"10.1177/08944393241279422","DOIUrl":"https://doi.org/10.1177/08944393241279422","url":null,"abstract":"Phubbing (phone snubbing) has become the norm in (im)polite society. A vast majority of US adults report using their phones during a recent social interaction. Using one’s phone in the presence of others has been shown to have a negative impact on relationships among co-workers, friends, family, and romantic partners. Recent research suggests viewing short-form videos (SFVs) (e.g., TikTok) is more addictive/immersive than traditional social media (e.g., Facebook) leading to a greater likelihood of phubbing others. Across two studies, the present research investigates the relationship between SFV viewing and phubbing and the possible mediating effect of self-control. We also test whether TikTok has a stronger relationship with phubbing than Instagram Reels and YouTube Shorts, two popular SFV purveyors. Study 1 (282 college students) finds that viewing TikTok videos is positively associated with phubbing others and this relationship is mediated by self-control. Interestingly, Study 1 also finds that this relationship does not hold for Instagram Reels and YouTube shorts. Using two different measures of self-control, Study 2 (198 adults) provides additional support for the mediating effect of self-control on the SFV viewing—phubbing relationship. Again, the model is only supported for TikTok SFV viewing, not Instagram or YouTube. In sum, the viewing of carefully curated short TikTok videos, often 30–60 seconds in length, undermines self-control which is associated with increased phubbing behavior. Implications of the present study’s findings expand far beyond phubbing. Self-control plays a central role in nearly all human decision making and behavior. Suggestions for future research are offered.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"10 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100644","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}
Modern Configurational Comparative Methods (CCMs), such as Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), have gained in popularity among social scientists over the last thirty years. A new CCM called Combinational Regularity Analysis (CORA) has recently joined this family of methods. In this article, we provide a software tutorial for the open-source package CORA, which implements the eponymous method. In particular, we demonstrate how to use CORA to discover shared causes of complex effects and how to interpret corresponding solutions correctly, how to mine configurational data to identify minimum-size tuples of solution-generating inputs, and how to visualize solutions by means of logic diagrams.
{"title":"CORA: An Open-Source Software Tool for Combinational Regularity Analysis","authors":"Lusine Mkrtchyan, Alrik Thiem, Zuzana Sebechlebská","doi":"10.1177/08944393241275640","DOIUrl":"https://doi.org/10.1177/08944393241275640","url":null,"abstract":"Modern Configurational Comparative Methods (CCMs), such as Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), have gained in popularity among social scientists over the last thirty years. A new CCM called Combinational Regularity Analysis (CORA) has recently joined this family of methods. In this article, we provide a software tutorial for the open-source package CORA, which implements the eponymous method. In particular, we demonstrate how to use CORA to discover shared causes of complex effects and how to interpret corresponding solutions correctly, how to mine configurational data to identify minimum-size tuples of solution-generating inputs, and how to visualize solutions by means of logic diagrams.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"19 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100645","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}
Pub Date : 2024-08-24DOI: 10.1177/08944393241277553
Kristen Olson, Amanda Ganshert
Recruitment materials for concurrent mixed-mode self-administered web and mail surveys must communicate information about multiple modes simultaneously. Providing the link to the web survey on the cover of the paper questionnaire or including a QR code to access the web survey may increase the visibility of the web mode and thus increase the proportion of people who participate via the web, but whether including either piece of information does so has received surprisingly little empirical attention. In this paper, we examine the results of experiments embedded in two general population probability-based concurrent mixed-mode surveys of Nebraska adults. First, in the Labor Availability Survey of the Greater Omaha Area, respondents were randomly assigned to receive the web link and login information on the cover or the paper questionnaire without this information (all had web information in the cover letter). We then replicated and extended this experiment in the Labor Availability Survey of Northeast Nebraska. The questionnaire cover experiment was fully crossed with the presence or absence of a QR code to access the web survey. Neither of these design features affected response rates or speed of response, but the link on the questionnaire significantly increased the proportion of respondents who participated by web and the QR code significantly increased the proportion of respondents who participated by smartphone. Sample composition was largely unaffected on most characteristics, although the respondent pool was less similar to the population on education when the link was on the questionnaire. About 20% of respondents used a smartphone when typing in a survey link, but virtually all respondents used a smartphone when scanning the QR code. Survey researchers can include a link on the cover of the questionnaire to increase web participation rates in mixed-mode surveys. QR codes can be used when smartphone participation is desired.
同时进行的混合模式自填式网络调查和邮件调查的招募材料必须同时传达多种模式的信息。在纸质问卷的封面上提供网络调查的链接或加入二维码以访问网络调查，可能会提高网络模式的可见度，从而增加通过网络参与调查的人数比例，但令人惊讶的是，这两种信息是否都能起到这样的作用，却很少受到实证研究的关注。在本文中，我们研究了在对内布拉斯加州成年人进行的两次基于普通人群概率的并行混合模式调查中嵌入的实验结果。首先，在大奥马哈地区劳动力可用性调查中，受访者被随机分配到收到封面上的网络链接和登录信息或没有这些信息的纸质问卷中（所有问卷的封面信中都有网络信息）。随后，我们在内布拉斯加州东北部劳动力可用性调查中复制并扩展了这一实验。问卷封面实验与是否有二维码访问网络调查完全交叉进行。这些设计特征都没有影响回复率或回复速度，但问卷上的链接显著提高了通过网络参与调查的受访者比例，而二维码则显著提高了通过智能手机参与调查的受访者比例。虽然在问卷上设置链接时，受访者的教育程度与人口的相似度较低，但大多数特征对样本组成基本没有影响。约 20% 的受访者在输入调查链接时使用了智能手机，但几乎所有受访者在扫描二维码时都使用了智能手机。调查研究人员可以在问卷封面上加入链接，以提高混合模式调查中的网络参与率。当需要智能手机参与时，可以使用 QR 代码。
{"title":"Remember, You Can Complete This Survey Online! Web Survey Links and QR Codes in a Mixed-Mode Web and Mail General Population Survey","authors":"Kristen Olson, Amanda Ganshert","doi":"10.1177/08944393241277553","DOIUrl":"https://doi.org/10.1177/08944393241277553","url":null,"abstract":"Recruitment materials for concurrent mixed-mode self-administered web and mail surveys must communicate information about multiple modes simultaneously. Providing the link to the web survey on the cover of the paper questionnaire or including a QR code to access the web survey may increase the visibility of the web mode and thus increase the proportion of people who participate via the web, but whether including either piece of information does so has received surprisingly little empirical attention. In this paper, we examine the results of experiments embedded in two general population probability-based concurrent mixed-mode surveys of Nebraska adults. First, in the Labor Availability Survey of the Greater Omaha Area, respondents were randomly assigned to receive the web link and login information on the cover or the paper questionnaire without this information (all had web information in the cover letter). We then replicated and extended this experiment in the Labor Availability Survey of Northeast Nebraska. The questionnaire cover experiment was fully crossed with the presence or absence of a QR code to access the web survey. Neither of these design features affected response rates or speed of response, but the link on the questionnaire significantly increased the proportion of respondents who participated by web and the QR code significantly increased the proportion of respondents who participated by smartphone. Sample composition was largely unaffected on most characteristics, although the respondent pool was less similar to the population on education when the link was on the questionnaire. About 20% of respondents used a smartphone when typing in a survey link, but virtually all respondents used a smartphone when scanning the QR code. Survey researchers can include a link on the cover of the questionnaire to increase web participation rates in mixed-mode surveys. QR codes can be used when smartphone participation is desired.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"7 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050629","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}
Pub Date : 2024-08-24DOI: 10.1177/08944393241269406
Xiao Xu, Anne Gauthier, Gert Stulp, Antal van den Bosch
Uncertainty in fertility intentions is a major obstacle to understanding contemporary trends in fertility decision-making and its outcomes. Quantifying this uncertainty by structural factors such as income, ethnicity, and housing conditions is recognized as insufficient. A recently proposed framework on subjective narratives has opened up a new way to gauge factors behind fertility decision-making and uncertainty. Through surveys, such narratives can be elicited with open-ended questions (OEQs). However, analyzing answers to OEQs typically involves extensive human coding, imposing constraints on sample size. Natural Language Processing (NLP) techniques assist researchers in grasping aspects of the underlying reasoning behind responses with much less human effort. In this study, using automatic neural topic modeling methods, we identify and interpret topics and themes underlying the narratives on fertility intention uncertainty of women in the Netherlands. We used Contextualized Topic Models (CTMs), a neural topic model using pre-trained representations of Dutch language, to conduct our analyses. Our results show that nine topics dominate the narratives about fertility planning, with age and health-related issues as the most prominent ones. In addition, we found that uncertainty in fertility intentions is not homogeneous, as women who feel uncertain due to real-life constraints and those who have no fertility plans at all put their stress on vastly different narratives.
{"title":"Understanding Narratives of Uncertainty in Fertility Intentions of Dutch Women: A Neural Topic Modeling Approach","authors":"Xiao Xu, Anne Gauthier, Gert Stulp, Antal van den Bosch","doi":"10.1177/08944393241269406","DOIUrl":"https://doi.org/10.1177/08944393241269406","url":null,"abstract":"Uncertainty in fertility intentions is a major obstacle to understanding contemporary trends in fertility decision-making and its outcomes. Quantifying this uncertainty by structural factors such as income, ethnicity, and housing conditions is recognized as insufficient. A recently proposed framework on subjective narratives has opened up a new way to gauge factors behind fertility decision-making and uncertainty. Through surveys, such narratives can be elicited with open-ended questions (OEQs). However, analyzing answers to OEQs typically involves extensive human coding, imposing constraints on sample size. Natural Language Processing (NLP) techniques assist researchers in grasping aspects of the underlying reasoning behind responses with much less human effort. In this study, using automatic neural topic modeling methods, we identify and interpret topics and themes underlying the narratives on fertility intention uncertainty of women in the Netherlands. We used Contextualized Topic Models (CTMs), a neural topic model using pre-trained representations of Dutch language, to conduct our analyses. Our results show that nine topics dominate the narratives about fertility planning, with age and health-related issues as the most prominent ones. In addition, we found that uncertainty in fertility intentions is not homogeneous, as women who feel uncertain due to real-life constraints and those who have no fertility plans at all put their stress on vastly different narratives.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"25 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050627","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}
Pub Date : 2024-08-23DOI: 10.1177/08944393241277556
Boyu Qiu, Wei Zhang
There is a close connection between video games and social life, and researchers are interested in whether and how video games shape aggression and prosocial behaviors. However, there are great inconsistencies across studies on this topic. These mixed results may be due in part to a focus on learning models that were relevant in research on traditional media like television but are less useful in research on video games. Unlike other media, video games are characterized by frequent game-player interactions and immediate feedback, and there is evidence that in-game rewards and punishments can shape aggressive or prosocial behavior inside and outside the game. We argue that reinforcement learning may help us to understand the effects of video games on aggressive and prosocial behaviors, and propose a conceptual model based on this argument.
{"title":"Video Game Feedback Learning and Aggressive or Prosocial Effects","authors":"Boyu Qiu, Wei Zhang","doi":"10.1177/08944393241277556","DOIUrl":"https://doi.org/10.1177/08944393241277556","url":null,"abstract":"There is a close connection between video games and social life, and researchers are interested in whether and how video games shape aggression and prosocial behaviors. However, there are great inconsistencies across studies on this topic. These mixed results may be due in part to a focus on learning models that were relevant in research on traditional media like television but are less useful in research on video games. Unlike other media, video games are characterized by frequent game-player interactions and immediate feedback, and there is evidence that in-game rewards and punishments can shape aggressive or prosocial behavior inside and outside the game. We argue that reinforcement learning may help us to understand the effects of video games on aggressive and prosocial behaviors, and propose a conceptual model based on this argument.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"5 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045454","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}
Pub Date : 2024-08-16DOI: 10.1177/08944393241270382
Hsiu-Chi Lu, Hsuan-wei Lee
The pervasive presence and influence of political bots have become the subject of extensive research in recent years. Studies have revealed that a significant percentage of active accounts are bots, contributing to the polarization of public sentiment online. This study employs an agent-based model in conducting computer simulations of complex social networks, to elucidate how bots, representing diverse ideological perspectives, exacerbate societal divisions. To investigate the dynamics of opinion diffusion and shed light on the phenomenon of polarization caused by the activities of political bots, we introduced bots into a bounded-confidence opinion dynamic model for different social networks, whereby the effects of bots on other agents were studied to provide a comprehensive understanding of their influence on opinion dynamics. The simulations showed that the symmetrical deployment of bots on both sides of the opinion spectrum intensifies polarization. These effects were observed within specific tolerance and homophily ranges, with low and high user tolerances slowing down polarization. Moreover, the average path length of the network and the centrality of the bots had a significant impact on the result. Finally, polarization tends to be lower when humans exhibit reduced confidence in bots. This research not only offers valuable insights into the implications of bot activities on the polarization of public opinion and current state of digital society but also provides suggestions to mitigate bot-driven polarization.
{"title":"Agents of Discord: Modeling the Impact of Political Bots on Opinion Polarization in Social Networks","authors":"Hsiu-Chi Lu, Hsuan-wei Lee","doi":"10.1177/08944393241270382","DOIUrl":"https://doi.org/10.1177/08944393241270382","url":null,"abstract":"The pervasive presence and influence of political bots have become the subject of extensive research in recent years. Studies have revealed that a significant percentage of active accounts are bots, contributing to the polarization of public sentiment online. This study employs an agent-based model in conducting computer simulations of complex social networks, to elucidate how bots, representing diverse ideological perspectives, exacerbate societal divisions. To investigate the dynamics of opinion diffusion and shed light on the phenomenon of polarization caused by the activities of political bots, we introduced bots into a bounded-confidence opinion dynamic model for different social networks, whereby the effects of bots on other agents were studied to provide a comprehensive understanding of their influence on opinion dynamics. The simulations showed that the symmetrical deployment of bots on both sides of the opinion spectrum intensifies polarization. These effects were observed within specific tolerance and homophily ranges, with low and high user tolerances slowing down polarization. Moreover, the average path length of the network and the centrality of the bots had a significant impact on the result. Finally, polarization tends to be lower when humans exhibit reduced confidence in bots. This research not only offers valuable insights into the implications of bot activities on the polarization of public opinion and current state of digital society but also provides suggestions to mitigate bot-driven polarization.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"136 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994348","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}
Pub Date : 2024-08-10DOI: 10.1177/08944393241269420
Prathm Juneja, Luciano Floridi
In this article, we analyze whether Twitter can be used to detect relative reports of issues at polling places. We use 20,322 tweets geolocated to U.S. states that match a series of keywords on the 2010, 2012, 2014, 2016, and 2018 general election days. We fine-tune BERTweet, a pre-trained language model, using a training set of 6,365 tweets labeled as issues or non-issues. We develop a model with an accuracy of 96.9% and a recall of 72.2%, and another model with an accuracy of 90.5% and a recall of 93.5%, far exceeding the performance of baseline models. Based on these results, we argue that these BERTweet-based models are promising methods for detecting reports of polling place issues on U.S. election days. We suggest that outputs from these models can be used to supplement existing voter protection efforts and to research the impact of policies, demographics, and other variables on voting access.
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