Pub Date : 2024-03-14DOI: 10.1177/08944393241235175
Jakob Mökander, Ralph Schroeder
In this paper, we first frame the use of artificial intelligence (AI) systems in the public sector as a continuation and intensification of long-standing rationalization and bureaucratization processes. Drawing on Weber, we understand the core of these processes to be the replacement of traditions with instrumental rationality, that is, the most calculable and efficient way of achieving any given policy objective. Second, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well-known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end: reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, our analysis also highlights that AI-driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens’ sense of their (non-instrumental) obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Third, we observe that contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic: science can only provide the means – it cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI-driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny, deliberation, and debate.
{"title":"Artificial Intelligence, Rationalization, and the Limits of Control in the Public Sector: The Case of Tax Policy Optimization","authors":"Jakob Mökander, Ralph Schroeder","doi":"10.1177/08944393241235175","DOIUrl":"https://doi.org/10.1177/08944393241235175","url":null,"abstract":"In this paper, we first frame the use of artificial intelligence (AI) systems in the public sector as a continuation and intensification of long-standing rationalization and bureaucratization processes. Drawing on Weber, we understand the core of these processes to be the replacement of traditions with instrumental rationality, that is, the most calculable and efficient way of achieving any given policy objective. Second, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well-known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end: reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, our analysis also highlights that AI-driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens’ sense of their (non-instrumental) obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Third, we observe that contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic: science can only provide the means – it cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI-driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny, deliberation, and debate.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"19 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140142191","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 : 2024-02-23DOI: 10.1177/08944393241235182
Anne Reinhardt, Sophie Mayen, Claudia Wilhelm
Mobile Experience Sampling (MES) is a promising tool for understanding youth digital media use and its effects. Unfortunately, the method suffers from high levels of missing data. Depending on whether the data is randomly or non-randomly missing, it can have severe effects on the validity of findings. For this reason, we investigated predictors of non-response in an MES study on displacement effects of digital media use on adolescents’ well-being and academic performance ( N = 347). Multilevel binary logistic regression identified significant influencing factors of response odds, such as afternoon beeps and being outside. Importantly, adolescents with poorer school grades were more likely to miss beeps. Because this missingness was related to the outcome variable, modern missing data methods such as multiple imputation should be applied before analyzing the data. Understanding the reasons for non-response can be seen as the first step to preventing, minimizing, and handling missing data in MES studies, ultimately ensuring that the collected data is fully utilized to draw accurate conclusions.
移动体验取样（MES）是了解青少年数字媒体使用情况及其影响的一种很有前途的工具。遗憾的是，这种方法存在大量数据缺失的问题。根据数据是随机缺失还是非随机缺失，缺失数据会严重影响研究结果的有效性。因此，我们在一项关于数字媒体的使用对青少年幸福感和学习成绩的影响的多层次调查研究（N = 347）中调查了未回应的预测因素。多层次二元逻辑回归确定了影响响应几率的重要因素，如下午的哔哔声和在户外。重要的是，学习成绩较差的青少年更有可能错过提示音。由于这种缺失与结果变量有关，因此在分析数据前应采用多重估算等现代缺失数据方法。了解无响应的原因可被视为预防、尽量减少和处理 MES 研究中数据缺失的第一步，最终确保收集到的数据得到充分利用，从而得出准确的结论。
{"title":"Uncovering the Missing Pieces: Predictors of Nonresponse in a Mobile Experience Sampling Study on Media Effects Among Youth","authors":"Anne Reinhardt, Sophie Mayen, Claudia Wilhelm","doi":"10.1177/08944393241235182","DOIUrl":"https://doi.org/10.1177/08944393241235182","url":null,"abstract":"Mobile Experience Sampling (MES) is a promising tool for understanding youth digital media use and its effects. Unfortunately, the method suffers from high levels of missing data. Depending on whether the data is randomly or non-randomly missing, it can have severe effects on the validity of findings. For this reason, we investigated predictors of non-response in an MES study on displacement effects of digital media use on adolescents’ well-being and academic performance ( N = 347). Multilevel binary logistic regression identified significant influencing factors of response odds, such as afternoon beeps and being outside. Importantly, adolescents with poorer school grades were more likely to miss beeps. Because this missingness was related to the outcome variable, modern missing data methods such as multiple imputation should be applied before analyzing the data. Understanding the reasons for non-response can be seen as the first step to preventing, minimizing, and handling missing data in MES studies, ultimately ensuring that the collected data is fully utilized to draw accurate conclusions.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139939049","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 : 2024-01-11DOI: 10.1177/08944393231224540
Alexander Wenz, Florian Keusch, Ruben L. Bach
While digital technology use and skills have typically been measured with surveys, digital behavioral data that are passively collected from individuals’ digital devices have recently emerged as an alternative method of measuring technology usage patterns in a more unobtrusive and detailed way. In this paper, we evaluate how passively collected smartphone usage data compare to self-reported measures of smartphone use, considering the three usage dimensions amount of use, variety of use, and activities of use. Based on a sample of smartphone users in Germany who completed a survey and had a tracking app installed on their smartphone, we find that the alignment between the survey and digital behavioral data varies by dimension of smartphone use. Whereas amount of use is considerably overreported in the survey data, variety of use aligns more closely across the two data sources. For activities of use, the alignment differs by type of activity. The results also show that the alignment between survey and digital behavioral data is systematically related to individuals’ sociodemographic characteristics, including age, gender, and educational attainment. Finally, latent class analyses conducted separately for the survey and digital behavioral data suggest similar typologies of smartphone use, although the overlap between the typologies on the individual level is rather small.
{"title":"Measuring Smartphone Use: Survey Versus Digital Behavioral Data","authors":"Alexander Wenz, Florian Keusch, Ruben L. Bach","doi":"10.1177/08944393231224540","DOIUrl":"https://doi.org/10.1177/08944393231224540","url":null,"abstract":"While digital technology use and skills have typically been measured with surveys, digital behavioral data that are passively collected from individuals’ digital devices have recently emerged as an alternative method of measuring technology usage patterns in a more unobtrusive and detailed way. In this paper, we evaluate how passively collected smartphone usage data compare to self-reported measures of smartphone use, considering the three usage dimensions amount of use, variety of use, and activities of use. Based on a sample of smartphone users in Germany who completed a survey and had a tracking app installed on their smartphone, we find that the alignment between the survey and digital behavioral data varies by dimension of smartphone use. Whereas amount of use is considerably overreported in the survey data, variety of use aligns more closely across the two data sources. For activities of use, the alignment differs by type of activity. The results also show that the alignment between survey and digital behavioral data is systematically related to individuals’ sociodemographic characteristics, including age, gender, and educational attainment. Finally, latent class analyses conducted separately for the survey and digital behavioral data suggest similar typologies of smartphone use, although the overlap between the typologies on the individual level is rather small.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"9 4","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437763","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 : 2024-01-02DOI: 10.1177/08944393231224543
Grace H. Wolff, Cuihua Shen
This study examines how active participation, financial commitment, and passive participation in the leading social live-streaming service, Twitch.tv, relate to individuals’ psychological well-being. The three dimensions of social capital—structural, relational, and cognitive—as well as parasocial relationship are explored as mediators. Cross-sectional survey data from 396 respondents was analyzed by comparing two fully saturated structural equation models. Findings indicate actively participating in a favorite streamers’ Chat is positively associated with increased well-being. Structural social capital, or having more social interaction ties, positively mediates the relationship between active participation and well-being, as well as financial commitment and well-being. Greater cognitive social capital, or shared values and goals with a favorite streamer, is related to decreased well-being. Parasocial relationship does not significantly mediate the relationship between use and well-being. Our results demonstrate the importance of tangible social ties over the perceived relationships or identification with a favorite streamer.
{"title":"Social Live-Streaming Use and Well-Being: Examining Participation, Financial Commitment, Social Capital, and Psychological Well-Being on Twitch.tv","authors":"Grace H. Wolff, Cuihua Shen","doi":"10.1177/08944393231224543","DOIUrl":"https://doi.org/10.1177/08944393231224543","url":null,"abstract":"This study examines how active participation, financial commitment, and passive participation in the leading social live-streaming service, Twitch.tv, relate to individuals’ psychological well-being. The three dimensions of social capital—structural, relational, and cognitive—as well as parasocial relationship are explored as mediators. Cross-sectional survey data from 396 respondents was analyzed by comparing two fully saturated structural equation models. Findings indicate actively participating in a favorite streamers’ Chat is positively associated with increased well-being. Structural social capital, or having more social interaction ties, positively mediates the relationship between active participation and well-being, as well as financial commitment and well-being. Greater cognitive social capital, or shared values and goals with a favorite streamer, is related to decreased well-being. Parasocial relationship does not significantly mediate the relationship between use and well-being. Our results demonstrate the importance of tangible social ties over the perceived relationships or identification with a favorite streamer.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"131 31","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453405","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 : 2023-12-19DOI: 10.1177/08944393231222680
Xin‑Yi Wei, Han-Yu Liang, Ting Gao, Ling-Feng Gao, Guo-Hua Zhang, Xiao-Yuan Chu, Hong-Xia Wang, Jing-Yu Geng, Ke Liu, Jia Nie, Pan Zeng, Lei Ren, Chang Liu, Huai‑Bin Jiang, Li Lei
Young adults are a high-risk population for developing smartphone addiction (SA), which bring about social issues. One theoretically and empirically supported proximal risk factor of SA is preference for smartphone-based internet applications (PSIA). However, most previous studies ignore gender difference and symptomatic heterogeneity of SA. Besides, many previous data analyses contain non-addicts, and the results derived might not be applicable to smartphone addicts. To bridging the gap, we used a symptom-level network analysis to assess gender differences in the links between preferences for 8 smartphone-based internet applications and 4 SA symptoms among young adults with high-level phone addiction (619 women and 415 men). The results showed that: (1) The relationship between the preference for video and the “loss of control” symptom was more pronounced in female addicts compared to their male counterparts. (2) Shopping app had stronger bridge centrality in women’s smartphone applications-SA network, which was positively linked with more SA symptoms. (3) Our research identified marginal gender differences in smartphone addicts' psychological networks, with female addicts showing stronger links between social media/eBook preferences and withdrawal symptoms, and male addicts displaying a stronger connection between gaming/eBook and other smartphone activities. The study provides a visualized network association and network metrics for understanding the relationship between PSIA and SA. We propose adopting a selective processing hypothesis and an evolutionary psychology perspective to aid in understanding these gender differences.
年轻人是智能手机成瘾（SA）的高风险人群，而智能手机成瘾会带来社会问题。对智能手机网络应用程序的偏好（PSIA）是一个得到理论和实证支持的 SA 近端风险因素。然而，以往的研究大多忽视了 SA 的性别差异和症状异质性。此外，以前的许多数据分析都包含非成瘾者，得出的结果可能不适用于智能手机成瘾者。为了弥补这一缺陷，我们采用症状层面的网络分析方法，评估了高度手机成瘾的年轻人（619 名女性和 415 名男性）对 8 种基于智能手机的互联网应用软件的偏好与 4 种 SA 症状之间的性别差异。结果显示(1) 与男性成瘾者相比，女性成瘾者对视频的偏好与 "失控 "症状之间的关系更为明显。(2）在女性的智能手机应用-SA 网络中，购物应用具有更强的桥中心性，这与更多的 SA 症状呈正相关。(3）我们的研究发现了智能手机成瘾者心理网络中的边缘性别差异，女性成瘾者在社交媒体/电子书偏好和戒断症状之间表现出更强的联系，而男性成瘾者在游戏/电子书和其他智能手机活动之间表现出更强的联系。该研究提供了可视化的网络关联和网络指标，用于理解 PSIA 与戒断症状之间的关系。我们建议采用选择性加工假说和进化心理学视角来帮助理解这些性别差异。
{"title":"Preference for Smartphone-Based Internet Applications and Smartphone Addiction Among Young Adult Addicts: Gender Difference in Psychological Network","authors":"Xin‑Yi Wei, Han-Yu Liang, Ting Gao, Ling-Feng Gao, Guo-Hua Zhang, Xiao-Yuan Chu, Hong-Xia Wang, Jing-Yu Geng, Ke Liu, Jia Nie, Pan Zeng, Lei Ren, Chang Liu, Huai‑Bin Jiang, Li Lei","doi":"10.1177/08944393231222680","DOIUrl":"https://doi.org/10.1177/08944393231222680","url":null,"abstract":"Young adults are a high-risk population for developing smartphone addiction (SA), which bring about social issues. One theoretically and empirically supported proximal risk factor of SA is preference for smartphone-based internet applications (PSIA). However, most previous studies ignore gender difference and symptomatic heterogeneity of SA. Besides, many previous data analyses contain non-addicts, and the results derived might not be applicable to smartphone addicts. To bridging the gap, we used a symptom-level network analysis to assess gender differences in the links between preferences for 8 smartphone-based internet applications and 4 SA symptoms among young adults with high-level phone addiction (619 women and 415 men). The results showed that: (1) The relationship between the preference for video and the “loss of control” symptom was more pronounced in female addicts compared to their male counterparts. (2) Shopping app had stronger bridge centrality in women’s smartphone applications-SA network, which was positively linked with more SA symptoms. (3) Our research identified marginal gender differences in smartphone addicts' psychological networks, with female addicts showing stronger links between social media/eBook preferences and withdrawal symptoms, and male addicts displaying a stronger connection between gaming/eBook and other smartphone activities. The study provides a visualized network association and network metrics for understanding the relationship between PSIA and SA. We propose adopting a selective processing hypothesis and an evolutionary psychology perspective to aid in understanding these gender differences.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" 556","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960569","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 : 2023-12-18DOI: 10.1177/08944393231220488
Sofie L Astrupgaard, August Lohse, E. M. Gregersen, Jonathan H. Salka, Kristoffer Albris, Morten A. Pedersen
Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomena compared to other data sources. Their open-ended and flexible character makes them especially useful in explorative research. However, fieldnotes are typically highly unstructured and personalized by individual researchers, which make them harder to use as a method for data collection in collaborative and mixed methods research. More precisely, the unstructured nature of ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficult to search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult to meaningfully integrate fieldnotes with other more quantitative data types such as more such as surveys or geospatial data, and 3) Computational Processability—it is hard to process and analyze fieldnotes with computational methods such as topic models and network analysis. To solve these three challenges, we present a new digital tool, for the systematic collection, processing, and analysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinary mixed methods pilot study on attention dynamics at a political festival in Denmark. Through case examples from this study, we show how adopting this new digital tool allowed our team to overcome the three aforementioned challenges of fieldnotes, while retaining the flexible and explorative character of ethnographic research, which is a key strength of ethnographic fieldwork.
{"title":"Fixing Fieldnotes: Developing and Testing a Digital Tool for the Collection, Processing, and Analysis of Ethnographic Data","authors":"Sofie L Astrupgaard, August Lohse, E. M. Gregersen, Jonathan H. Salka, Kristoffer Albris, Morten A. Pedersen","doi":"10.1177/08944393231220488","DOIUrl":"https://doi.org/10.1177/08944393231220488","url":null,"abstract":"Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomena compared to other data sources. Their open-ended and flexible character makes them especially useful in explorative research. However, fieldnotes are typically highly unstructured and personalized by individual researchers, which make them harder to use as a method for data collection in collaborative and mixed methods research. More precisely, the unstructured nature of ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficult to search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult to meaningfully integrate fieldnotes with other more quantitative data types such as more such as surveys or geospatial data, and 3) Computational Processability—it is hard to process and analyze fieldnotes with computational methods such as topic models and network analysis. To solve these three challenges, we present a new digital tool, for the systematic collection, processing, and analysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinary mixed methods pilot study on attention dynamics at a political festival in Denmark. Through case examples from this study, we show how adopting this new digital tool allowed our team to overcome the three aforementioned challenges of fieldnotes, while retaining the flexible and explorative character of ethnographic research, which is a key strength of ethnographic fieldwork.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":" 18","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138963565","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 : 2023-12-18DOI: 10.1177/08944393231220757
Suha AlAwadhi, Husain Alansari, Ahmad R. Alsaber
This study investigates the users’ perception of trust-building factors influencing the use of e-government services and information, by integrating constructs identified in the technology acceptance model (TAM) with information systems (IS) success and trust models. Data was collected using a questionnaire targeted towards users of e-government services in Kuwait. The partial least squares structural equation modeling method was used to analyze 717 valid questionnaire responses. The results indicate that information quality and design (IQD) and perceived ease of use (PEU) influence individuals’ trust in e-government (TEG), thereby affecting their behavioral intentions (BI). Furthermore, the results indicate an average level of the users’ satisfaction and significant differences in how gender and nationality are associated with the overall satisfaction of e-government services users. The proposed framework contributes to extending models by integrating IQD (a modified construct of the IS model) and PEU (a construct of the TAM) as trust-related factors that provide better insights into the driving forces of BI and should be considered when designing and developing e-government services. Additionally, the study provides a deeper understanding of the challenges that could hinder the use of e-government systems.
本研究通过将技术接受模型（TAM）中确定的构造与信息系统（IS）成功和信任模型相结合，调查了用户对影响电子政务服务和信息使用的信任建立因素的看法。数据是通过针对科威特电子政务服务用户的调查问卷收集的。采用偏最小二乘法结构方程模型法对 717 份有效问卷进行了分析。结果表明，信息质量和设计（IQD）以及感知易用性（PEU）会影响个人对电子政务的信任（TEG），从而影响其行为意向（BI）。此外，研究结果表明，用户的满意度处于平均水平，性别和国籍与电子政务服务用户的总体满意度存在显著差异。所提出的框架通过整合 IQD（IS 模型的一个修正构造）和 PEU（TAM 的一个构造）作为与信任相关的因素，为扩展模型做出了贡献，这些因素为 BI 的驱动力提供了更好的见解，在设计和开发电子政务服务时应加以考虑。此外，本研究还有助于深入了解可能阻碍电子政务系统使用的挑战。
{"title":"Explicating Trust-building Factors Impacting the Use of e-government Services","authors":"Suha AlAwadhi, Husain Alansari, Ahmad R. Alsaber","doi":"10.1177/08944393231220757","DOIUrl":"https://doi.org/10.1177/08944393231220757","url":null,"abstract":"This study investigates the users’ perception of trust-building factors influencing the use of e-government services and information, by integrating constructs identified in the technology acceptance model (TAM) with information systems (IS) success and trust models. Data was collected using a questionnaire targeted towards users of e-government services in Kuwait. The partial least squares structural equation modeling method was used to analyze 717 valid questionnaire responses. The results indicate that information quality and design (IQD) and perceived ease of use (PEU) influence individuals’ trust in e-government (TEG), thereby affecting their behavioral intentions (BI). Furthermore, the results indicate an average level of the users’ satisfaction and significant differences in how gender and nationality are associated with the overall satisfaction of e-government services users. The proposed framework contributes to extending models by integrating IQD (a modified construct of the IS model) and PEU (a construct of the TAM) as trust-related factors that provide better insights into the driving forces of BI and should be considered when designing and developing e-government services. Additionally, the study provides a deeper understanding of the challenges that could hinder the use of e-government systems.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"12 2","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995380","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 : 2023-12-07DOI: 10.1177/08944393231220490
Brian C. Britt
The past several years have seen rising hate crimes, terrorist attacks, and broader extremist movements, with news reports often noting that these movements can be traced back to fringe online communities. Yet the question remains why such online groups appear more likely to foster radicalization than those in other contexts. This netnographic case study demonstrates how sexual appeals in fringe online communities facilitate the development of extremist ideologies. Specifically, the cognitive effects of sexual arousal combined with the social norms of such communities contribute to the acceptance of hate speech and fringe ideologies while reducing the extent to which audiences evaluate rational arguments and competing points of view. Thus, sexual appeals paired with messaging or imagery that promotes fringe points of view, which can be more freely expressed in small online groups than in other contexts, are more likely to result in intended attitudinal and behavioral changes—in other words, extremism.
{"title":"Sex Sells Terrorism: How Sexual Appeals in Fringe Online Communities Contribute to Self-Radicalization","authors":"Brian C. Britt","doi":"10.1177/08944393231220490","DOIUrl":"https://doi.org/10.1177/08944393231220490","url":null,"abstract":"The past several years have seen rising hate crimes, terrorist attacks, and broader extremist movements, with news reports often noting that these movements can be traced back to fringe online communities. Yet the question remains why such online groups appear more likely to foster radicalization than those in other contexts. This netnographic case study demonstrates how sexual appeals in fringe online communities facilitate the development of extremist ideologies. Specifically, the cognitive effects of sexual arousal combined with the social norms of such communities contribute to the acceptance of hate speech and fringe ideologies while reducing the extent to which audiences evaluate rational arguments and competing points of view. Thus, sexual appeals paired with messaging or imagery that promotes fringe points of view, which can be more freely expressed in small online groups than in other contexts, are more likely to result in intended attitudinal and behavioral changes—in other words, extremism.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"8 8","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138592821","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 : 2023-12-06DOI: 10.1177/08944393231220487
Piper Liping Liu, T. E. D. Yeo
Despite the growing prevalence of social media usage among older adults, the impact for their well-being remains unclear. This study investigates the impact of social grooming on social media (SGSM) on the life satisfaction of a representative sample ( N = 591) of older adults (aged 55 and above) in Taiwan. Using an indirect effects paradigm, the study examines the mediation mechanisms of bridging social capital and perceived social support in the relationship between SGSM and life satisfaction. Additionally, the moderating effect of social network size (SNS) is assessed. The results indicate that bridging social capital and social support fully and sequentially mediate the influence of SGSM on older adults’ life satisfaction. Furthermore, SNS is identified as a significant moderator in this sequential mediating effect. These findings contribute to the existing literature on social media use and highlight the importance of understanding the impact of SGSM on life satisfaction and other psychological outcomes for older adults. The results also emphasize the need to consider the unique characteristics and specific needs of older adults, and to promote and assist them in effectively using social media to expand their social networks and acquire social support, which are crucial for their life satisfaction.
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Pub Date : 2023-12-06DOI: 10.1177/08944393231219685
Lingshu Hu
Political partisanship constitutes a pivotal group identity that significantly influences individuals’ voting behaviors and shapes their ideological and cultural perspectives. While traditional surveys and experimental studies can directly capture political identity by asking the participants, this task has become intricate when employing digital trace data sourced from social media. Previous classification methods, attempting to infer political identity from users’ networks or textual content, suffered from limited efficiency or generalizability. In response, this study introduces a two-step method that utilizes deep learning models to enhance classification efficiency, generalizability, and interpretability. In the first step, two deep learning models, trained on 2.5 million tweets from 825 Congressional politicians in the U.S., achieved accuracy rates of 87.71% and 89.54%, respectively, in detecting politicians’ partisanships based on their individual tweets. Subsequently, in the second step, by employing a simple machine learning model that leverages the aggregated predicted values derived from the first-step models, accuracy rates of 94.92% and 96.61% were attained for identifying non-politician users’ political identities based off their 50 and 200 tweets, respectively. In addition, an attention mechanism was integrated into the deep learning model to assess the contribution of each word in the classification process.
{"title":"A Two-Step Method for Classifying Political Partisanship Using Deep Learning Models","authors":"Lingshu Hu","doi":"10.1177/08944393231219685","DOIUrl":"https://doi.org/10.1177/08944393231219685","url":null,"abstract":"Political partisanship constitutes a pivotal group identity that significantly influences individuals’ voting behaviors and shapes their ideological and cultural perspectives. While traditional surveys and experimental studies can directly capture political identity by asking the participants, this task has become intricate when employing digital trace data sourced from social media. Previous classification methods, attempting to infer political identity from users’ networks or textual content, suffered from limited efficiency or generalizability. In response, this study introduces a two-step method that utilizes deep learning models to enhance classification efficiency, generalizability, and interpretability. In the first step, two deep learning models, trained on 2.5 million tweets from 825 Congressional politicians in the U.S., achieved accuracy rates of 87.71% and 89.54%, respectively, in detecting politicians’ partisanships based on their individual tweets. Subsequently, in the second step, by employing a simple machine learning model that leverages the aggregated predicted values derived from the first-step models, accuracy rates of 94.92% and 96.61% were attained for identifying non-politician users’ political identities based off their 50 and 200 tweets, respectively. In addition, an attention mechanism was integrated into the deep learning model to assess the contribution of each word in the classification process.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"32 2","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596600","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}
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