Pub Date : 2023-06-01DOI: 10.1177/08944393211067687
Shelley Boulianne
Nonprofit organizations and groups depend on donations and volunteers for their survival. Digital media can help by offering a platform for making online donations and facilitating online volunteering, but also by identifying and connecting with people who are sympathetic to an organization's mission. This article employs four-country (USA, UK, France, and Canada) representative survey data (n = 6291) to examine the use of social media for establishing connections between citizens and organizations as well as the relationship of these connections to online and offline volunteering and donating. Across all social media platforms considered (Facebook, Instagram, and Twitter), I find significant positive correlations of following nonprofits with online and offline volunteering and donating. However, Facebook has a slightly larger role, which may be attributed to its overall popularity, which can incentivize organizations' more intense use of this platform.
{"title":"Standby Ties that Mobilize: Social Media Platforms and Civic Engagement.","authors":"Shelley Boulianne","doi":"10.1177/08944393211067687","DOIUrl":"https://doi.org/10.1177/08944393211067687","url":null,"abstract":"<p><p>Nonprofit organizations and groups depend on donations and volunteers for their survival. Digital media can help by offering a platform for making online donations and facilitating online volunteering, but also by identifying and connecting with people who are sympathetic to an organization's mission. This article employs four-country (USA, UK, France, and Canada) representative survey data (<i>n</i> = 6291) to examine the use of social media for establishing connections between citizens and organizations as well as the relationship of these connections to online and offline volunteering and donating. Across all social media platforms considered (Facebook, Instagram, and Twitter), I find significant positive correlations of following nonprofits with online and offline volunteering and donating. However, Facebook has a slightly larger role, which may be attributed to its overall popularity, which can incentivize organizations' more intense use of this platform.</p>","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"41 3","pages":"1001-1016"},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10297966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-30DOI: 10.1177/08944393231176596
Arne Weigold, Ingrid K. Weigold, Xiangling Zhang, Ning Tang, Yun Kai Chong
Computer self-efficacy (CSE) continues to be an important construct in research and application. Two measures of CSE, the Brief Inventory of Technology Self-Efficacy (BITS) and the Brief Inventory of Technology Self-Efficacy – Short Form (BITS-SF) were recently developed to correct for issues in other available measures. The BITS and BITS-SF were originally written in English, and their psychometric properties assessed in samples from the United States. The current two studies translated the BITS and BITS-SF into simplified Chinese (Mainland China) and traditional Chinese (Taiwan) and assessed their psychometric properties. In Study 1, 207 adults in Mainland China completed the simplified Chinese BITS and BITS-SF, as well as measures given to assess convergent, discriminant, and concurrent validity. In Study 2, 273 adults in Taiwan did the same, except that they completed the traditional Chinese BITS and BITS-SF. In both studies, the translated BITS showed evidence of a three-factor correlated structure, and the translated BITS-SF yielded several underlying classes consistent with theory and scoring interpretation. Additionally, the translated measures’ scores showed solid evidence of convergent, discriminant, and concurrent validity. The results replicate the findings using the original BITS and BITS-SF and extend them to simplified Chinese and traditional Chinese translated versions. These versions are recommended for use in research and applied settings to assess CSE and are available for use. Both the original and translated measures are available for download at www.bitssurvey.com .
{"title":"Translation and Validation of the Brief Inventory of Technology Self-Efficacy (BITS): Simplified and Traditional Chinese Versions","authors":"Arne Weigold, Ingrid K. Weigold, Xiangling Zhang, Ning Tang, Yun Kai Chong","doi":"10.1177/08944393231176596","DOIUrl":"https://doi.org/10.1177/08944393231176596","url":null,"abstract":"Computer self-efficacy (CSE) continues to be an important construct in research and application. Two measures of CSE, the Brief Inventory of Technology Self-Efficacy (BITS) and the Brief Inventory of Technology Self-Efficacy – Short Form (BITS-SF) were recently developed to correct for issues in other available measures. The BITS and BITS-SF were originally written in English, and their psychometric properties assessed in samples from the United States. The current two studies translated the BITS and BITS-SF into simplified Chinese (Mainland China) and traditional Chinese (Taiwan) and assessed their psychometric properties. In Study 1, 207 adults in Mainland China completed the simplified Chinese BITS and BITS-SF, as well as measures given to assess convergent, discriminant, and concurrent validity. In Study 2, 273 adults in Taiwan did the same, except that they completed the traditional Chinese BITS and BITS-SF. In both studies, the translated BITS showed evidence of a three-factor correlated structure, and the translated BITS-SF yielded several underlying classes consistent with theory and scoring interpretation. Additionally, the translated measures’ scores showed solid evidence of convergent, discriminant, and concurrent validity. The results replicate the findings using the original BITS and BITS-SF and extend them to simplified Chinese and traditional Chinese translated versions. These versions are recommended for use in research and applied settings to assess CSE and are available for use. Both the original and translated measures are available for download at www.bitssurvey.com .","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135642771","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 : 2023-05-29DOI: 10.1177/08944393231178605
Kohei Watanabe, A. Baturo
Topic models have been widely used by researchers across disciplines to automatically analyze large textual data. However, they often fail to automate content analysis, because the algorithms cannot accurately classify individual sentences into pre-defined topics. Aiming to make topic classification more theoretically grounded and content analysis in general more topic-specific, we have developed Seeded Sequential Latent Dirichlet allocation (LDA), extending the existing LDA algorithm, and implementing it in a widely accessible open-source package. Taking a large corpus of speeches delivered by delegates at the United Nations General Assembly as an example, we explain how our algorithm differs from the original algorithm; why it can classify sentences more accurately; how it accepts pre-defined topics in deductive or semi-deductive analysis; how such ex-ante topic mapping differs from ex-post topic mapping; how it enables topic-specific framing analysis in applied research. We also offer practical guidance on how to determine the optimal number of topics and select seed words for the algorithm.
{"title":"Seeded Sequential LDA: A Semi-Supervised Algorithm for Topic-Specific Analysis of Sentences","authors":"Kohei Watanabe, A. Baturo","doi":"10.1177/08944393231178605","DOIUrl":"https://doi.org/10.1177/08944393231178605","url":null,"abstract":"Topic models have been widely used by researchers across disciplines to automatically analyze large textual data. However, they often fail to automate content analysis, because the algorithms cannot accurately classify individual sentences into pre-defined topics. Aiming to make topic classification more theoretically grounded and content analysis in general more topic-specific, we have developed Seeded Sequential Latent Dirichlet allocation (LDA), extending the existing LDA algorithm, and implementing it in a widely accessible open-source package. Taking a large corpus of speeches delivered by delegates at the United Nations General Assembly as an example, we explain how our algorithm differs from the original algorithm; why it can classify sentences more accurately; how it accepts pre-defined topics in deductive or semi-deductive analysis; how such ex-ante topic mapping differs from ex-post topic mapping; how it enables topic-specific framing analysis in applied research. We also offer practical guidance on how to determine the optimal number of topics and select seed words for the algorithm.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49102713","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 : 2023-05-26DOI: 10.1177/08944393231173887
Peter Lynn, A. Bianchi, A. Gaia
The day of the week on which sample members are invited to participate in a web survey might influence propensity to respond, or to respond promptly (within two days from the invitation). This effect could differ between sample members with different characteristics. We explore such effects using a large-scale experiment implemented on the Understanding Society Innovation Panel, in which some people received an invitation on a Monday and some on a Friday. Specifically, we test whether any effect of the invitation day is moderated by economic activity status (which may result in a different organisation of time by day of the week), previous participation in the panel, or whether the invitation was sent only by post or by post and email simultaneously. Overall, we do not find any effect of day of invitation in survey participation or in prompt participation. However, sample members who provided an email address, and, thus, were contacted by email in addition to postal letter, are less likely to participate if invited on Friday (email reminders: Sunday and Tuesday) as opposed to Monday (email reminders: Wednesday and Friday). Given that no difference between the two protocols is found for prompt response, the effect seems to be due to the day of mailing of reminders. With respect to sample members' economic activity status, those not having a job and the retired are less likely to participate when invited on a Friday; this result holds also for prompt participation, but only for retired respondents. Also, sample members who work long hours are less likely to participate when invited on a Friday; however, no effect is found for prompt response.
样本成员被邀请参加网络调查的日期可能会影响他们的回复倾向，或者是迅速回复的倾向(收到邀请后两天内)。这种效应在具有不同特征的样品成员之间可能有所不同。我们通过在“理解社会创新小组”(Understanding Society Innovation Panel)上实施的大规模实验来探索这种影响，在实验中，一些人在周一收到邀请，一些人在周五收到邀请。具体而言，我们测试了邀请日的任何影响是否受到经济活动状况(可能导致一周中按天组织不同的时间)，以前参加小组讨论的情况，或者邀请是仅通过邮寄发送还是同时通过邮寄和电子邮件发送。总的来说，我们没有发现邀请日期对参与调查或及时参与有任何影响。然而，提供电子邮件地址的样本成员，因此，除了邮寄信件之外，还通过电子邮件联系，如果在周五(电子邮件提醒:周日和周二)被邀请，与周一(电子邮件提醒:周三和周五)相比，他们不太可能参加。鉴于两种协议在迅速反应方面没有差别，这种影响似乎是由于邮寄提醒的日期。就样本成员的经济活动状况而言，那些没有工作和退休的人在周五被邀请时不太可能参加;这一结果也适用于即时参与，但仅适用于退休受访者。此外，工作时间较长的样本成员在周五被邀请时不太可能参加;然而，没有发现及时反应的效果。
{"title":"The Impact of day of Mailing on Web Survey Response Rate and Response Speed","authors":"Peter Lynn, A. Bianchi, A. Gaia","doi":"10.1177/08944393231173887","DOIUrl":"https://doi.org/10.1177/08944393231173887","url":null,"abstract":"The day of the week on which sample members are invited to participate in a web survey might influence propensity to respond, or to respond promptly (within two days from the invitation). This effect could differ between sample members with different characteristics. We explore such effects using a large-scale experiment implemented on the Understanding Society Innovation Panel, in which some people received an invitation on a Monday and some on a Friday. Specifically, we test whether any effect of the invitation day is moderated by economic activity status (which may result in a different organisation of time by day of the week), previous participation in the panel, or whether the invitation was sent only by post or by post and email simultaneously. Overall, we do not find any effect of day of invitation in survey participation or in prompt participation. However, sample members who provided an email address, and, thus, were contacted by email in addition to postal letter, are less likely to participate if invited on Friday (email reminders: Sunday and Tuesday) as opposed to Monday (email reminders: Wednesday and Friday). Given that no difference between the two protocols is found for prompt response, the effect seems to be due to the day of mailing of reminders. With respect to sample members' economic activity status, those not having a job and the retired are less likely to participate when invited on a Friday; this result holds also for prompt participation, but only for retired respondents. Also, sample members who work long hours are less likely to participate when invited on a Friday; however, no effect is found for prompt response.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46306503","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 : 2023-05-26DOI: 10.1177/08944393231178604
Guan Wang
Street views, satellite imageries and remote sensing data have been integrated into a wide spectrum of topics in the social sciences. Computer vision methods not only help analysts and policymakers make better decisions and produce more effective solutions but they also enable models to achieve more precise predictions and greater interpretability. In this paper, we review the growing literature applying such methods to economic issues and the social sciences, in which social scientists employ deep learning approaches to utilise image data to retrieve additional information. Typically, image data produce better results than traditional approaches and can provide detailed results and helpful insights to improve society and people’s well-being.
{"title":"Integrating Street Views, Satellite Imageries and Remote Sensing Data Into Economics and the Social Sciences","authors":"Guan Wang","doi":"10.1177/08944393231178604","DOIUrl":"https://doi.org/10.1177/08944393231178604","url":null,"abstract":"Street views, satellite imageries and remote sensing data have been integrated into a wide spectrum of topics in the social sciences. Computer vision methods not only help analysts and policymakers make better decisions and produce more effective solutions but they also enable models to achieve more precise predictions and greater interpretability. In this paper, we review the growing literature applying such methods to economic issues and the social sciences, in which social scientists employ deep learning approaches to utilise image data to retrieve additional information. Typically, image data produce better results than traditional approaches and can provide detailed results and helpful insights to improve society and people’s well-being.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42886068","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 : 2023-05-25DOI: 10.1177/08944393231178603
Zicheng Cheng, Yanlin Li
TikTok—the world’s most downloaded app since 2020, has become a place for more than silly dancing and lip-syncing. TikTok users are increasingly turning to TikTok for news content. Meanwhile, news publishers are embracing TikTok to reach a younger audience. We aim to examine the content strategy adopted by the most-followed news publishers on TikTok and how effective their TikTok strategy is in spurring audience engagement in terms of liking, commenting, and sharing. This study retrieved 101,292 TikTok news videos as of November 22, 2022. With the help of computer vision, natural language processing, and sentiment analysis, we found that TikTok news videos containing negative sentiment and more second-person view shots are associated with significantly higher audience engagement. In addition, this study demonstrated that the TikTok video features and engagement levels differ between the news publishers and other TikTok creators. Moderator analysis shows that both the effect of negative sentiment on engagement and the effect of the second-person view on engagement are moderated by the TikTok account type. The impact of negative sentiment and second-person view on engagement behaviors becomes smaller or even insignificant for news publisher TikTok videos. Theoretical and practical implications are discussed in this study.
{"title":"Like, Comment, and Share on TikTok: Exploring the Effect of Sentiment and Second-Person View on the User Engagement with TikTok News Videos","authors":"Zicheng Cheng, Yanlin Li","doi":"10.1177/08944393231178603","DOIUrl":"https://doi.org/10.1177/08944393231178603","url":null,"abstract":"TikTok—the world’s most downloaded app since 2020, has become a place for more than silly dancing and lip-syncing. TikTok users are increasingly turning to TikTok for news content. Meanwhile, news publishers are embracing TikTok to reach a younger audience. We aim to examine the content strategy adopted by the most-followed news publishers on TikTok and how effective their TikTok strategy is in spurring audience engagement in terms of liking, commenting, and sharing. This study retrieved 101,292 TikTok news videos as of November 22, 2022. With the help of computer vision, natural language processing, and sentiment analysis, we found that TikTok news videos containing negative sentiment and more second-person view shots are associated with significantly higher audience engagement. In addition, this study demonstrated that the TikTok video features and engagement levels differ between the news publishers and other TikTok creators. Moderator analysis shows that both the effect of negative sentiment on engagement and the effect of the second-person view on engagement are moderated by the TikTok account type. The impact of negative sentiment and second-person view on engagement behaviors becomes smaller or even insignificant for news publisher TikTok videos. Theoretical and practical implications are discussed in this study.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48856380","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 : 2023-05-24DOI: 10.1177/08944393231178602
Zehui Dai, Cory Higgs
On June 24, 2022, the Supreme Court released its decision in Dobbs v. Jackson Women’s Health, which officially repealed Roe v. Wade and its subsequent rulings. Employing social network analysis and semantic analysis methods, the current project reviews the public reaction among Twitter users shared from the May 2 draft leak to the June 24 official repeal, using a series of Twitter hashtags related to Roe v. Wade. The project identified the main influencers within the network, namely, journalist/news organizations, Internet celebrities, activists/activist groups, professional/non-profit organizations, and politicians/political organizations through social network analysis. Through semantic analysis, the authors found prominent themes such as legal concerns, discourse on reproductive rights, distrusting of Supreme Court’s authority, and political nepotism. The results offer policy implications and communication message strategies to healthcare providers and policymakers. The authors believe that the polarizing nature of Roe v. Wade-related issues will be a crucial factor in shaping voters’ decisions during the upcoming 2024 presidential election.
{"title":"Social Network and Semantic Analysis of Roe v. Wade’s Reversal on Twitter","authors":"Zehui Dai, Cory Higgs","doi":"10.1177/08944393231178602","DOIUrl":"https://doi.org/10.1177/08944393231178602","url":null,"abstract":"On June 24, 2022, the Supreme Court released its decision in Dobbs v. Jackson Women’s Health, which officially repealed Roe v. Wade and its subsequent rulings. Employing social network analysis and semantic analysis methods, the current project reviews the public reaction among Twitter users shared from the May 2 draft leak to the June 24 official repeal, using a series of Twitter hashtags related to Roe v. Wade. The project identified the main influencers within the network, namely, journalist/news organizations, Internet celebrities, activists/activist groups, professional/non-profit organizations, and politicians/political organizations through social network analysis. Through semantic analysis, the authors found prominent themes such as legal concerns, discourse on reproductive rights, distrusting of Supreme Court’s authority, and political nepotism. The results offer policy implications and communication message strategies to healthcare providers and policymakers. The authors believe that the polarizing nature of Roe v. Wade-related issues will be a crucial factor in shaping voters’ decisions during the upcoming 2024 presidential election.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41946609","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 : 2023-05-16DOI: 10.1177/08944393231173895
A. M. Möller, Susan A. M. Vermeer, Susanne E. Baumgartner
Social scientists often study comments on YouTube to learn about people’s attitudes towards and experiences of online videos. However, not all YouTube comments are relevant in the sense that they reflect individuals’ thoughts about, or experiences of the content of a video or its artist/maker. Therefore, the present paper employs Supervised Machine Learning to automatically assess comments written in response to music videos in terms of their relevance. For those comments that are relevant, we also assess why they are relevant. Our results indicate that most YouTube comments are relevant (approx. 78%). Among those, most are relevant because they include a positive evaluation of the video, describe a viewer’s personal experience related to the video, or express a sense of community among the video viewers. We conclude that Supervised Machine Learning is a suitable method to find those YouTube comments that are relevant to scholars studying viewers’ reactions to online videos, and we present suggestions for scholars wanting to apply the same technique in their own projects.
{"title":"Cutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments","authors":"A. M. Möller, Susan A. M. Vermeer, Susanne E. Baumgartner","doi":"10.1177/08944393231173895","DOIUrl":"https://doi.org/10.1177/08944393231173895","url":null,"abstract":"Social scientists often study comments on YouTube to learn about people’s attitudes towards and experiences of online videos. However, not all YouTube comments are relevant in the sense that they reflect individuals’ thoughts about, or experiences of the content of a video or its artist/maker. Therefore, the present paper employs Supervised Machine Learning to automatically assess comments written in response to music videos in terms of their relevance. For those comments that are relevant, we also assess why they are relevant. Our results indicate that most YouTube comments are relevant (approx. 78%). Among those, most are relevant because they include a positive evaluation of the video, describe a viewer’s personal experience related to the video, or express a sense of community among the video viewers. We conclude that Supervised Machine Learning is a suitable method to find those YouTube comments that are relevant to scholars studying viewers’ reactions to online videos, and we present suggestions for scholars wanting to apply the same technique in their own projects.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45253757","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 : 2023-05-15DOI: 10.1177/08944393231176326
Jinzhe Zhao, Zhen Guo, Liying Jiao, M. Yu, Huiyue Shi, Yan Xu
Shyness has been shown to be linked to aggression. However, whether this relationship occurs in cyberspace and the mechanisms that might affect it are largely unexplored. Based on the social fitness model, the current study examined the relationship between shyness and cyber aggression, as well as the mediating roles of passive use and relative deprivation. Moreover, according to the integration of the social information processing model and moral domain theory, moral sensitivity serves as a moderator in the direct and indirect links between shyness and cyber aggression. A total of 700 Chinese college students ( M age = 18.68, 53.57% women) participated in the current study and completed multiple questionnaires, namely, the Shyness Scale, Cyber-Aggression Scale, Passive Use of Social Network Site Scale, Relative Deprivation Scale, and Ethical Sensitivity Scale. The results showed that shyness was positively associated with cyber aggression through the multiple mediating effects of passive use and relative deprivation. Additionally, moderated mediation analysis indicated that moral sensitivity moderated the direct and indirect relationship between shyness and cyber aggression. A high level of moral sensitivity weakened the association of shyness with cyber aggression and the association of relative deprivation with cyber aggression, supporting the moderated mediation model. This study implicates the underlying mechanisms of the relationship between shyness and cyber aggression and preventative interventions to reduce the risk of cyber aggression.
{"title":"Do Shy Individuals Engage in Cyber Aggression? The Multiple Mediation of Passive Use and Relative Deprivation and the Moderation of Moral Sensitivity","authors":"Jinzhe Zhao, Zhen Guo, Liying Jiao, M. Yu, Huiyue Shi, Yan Xu","doi":"10.1177/08944393231176326","DOIUrl":"https://doi.org/10.1177/08944393231176326","url":null,"abstract":"Shyness has been shown to be linked to aggression. However, whether this relationship occurs in cyberspace and the mechanisms that might affect it are largely unexplored. Based on the social fitness model, the current study examined the relationship between shyness and cyber aggression, as well as the mediating roles of passive use and relative deprivation. Moreover, according to the integration of the social information processing model and moral domain theory, moral sensitivity serves as a moderator in the direct and indirect links between shyness and cyber aggression. A total of 700 Chinese college students ( M age = 18.68, 53.57% women) participated in the current study and completed multiple questionnaires, namely, the Shyness Scale, Cyber-Aggression Scale, Passive Use of Social Network Site Scale, Relative Deprivation Scale, and Ethical Sensitivity Scale. The results showed that shyness was positively associated with cyber aggression through the multiple mediating effects of passive use and relative deprivation. Additionally, moderated mediation analysis indicated that moral sensitivity moderated the direct and indirect relationship between shyness and cyber aggression. A high level of moral sensitivity weakened the association of shyness with cyber aggression and the association of relative deprivation with cyber aggression, supporting the moderated mediation model. This study implicates the underlying mechanisms of the relationship between shyness and cyber aggression and preventative interventions to reduce the risk of cyber aggression.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49454667","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 : 2023-05-14DOI: 10.1177/08944393231176595
Matías Dodel
Mobile devices were key drivers for recent Internet expansion in lower-income countries, democratizing access. Nonetheless, concerns arose regarding their role in the creation of new digital underclass related to the capital-enhancing consequences of Internet use. Among these, e-government engagement allows individuals to reduce the administrative burdens of governmental interactions. Nonetheless, its uptake has been proven to be highly stratified in Latin American countries where most services are not digital-by-default. The article argues that disparities in digital access play a role in this e-government divides. It examines the antecedents and determinants of household computer access and mobile-only Internet use, and e-government engagement in Brazil. Based on “TIC Domicilios 2019” survey, using logistic regressions to predict household access to computers, mobile-only Internet access, and e-government engagement. Mediation analyses of the latter models are conducted, testing the sequential nature of socio-digital inequalities based on the DiSTO framework. Findings show that living in a household with computers reduces the chances of being a mobile-only user and increases the odds of e-government engagement. Mobile-only access reduces e-government engagement. The effects of socioeconomic status and digital inequalities are mediated by household access to computers and mobile-only use. Implications for digital inclusion policies are discussed.
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