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.
{"title":"Social Grooming on Social Media and Older Adults’ Life Satisfaction: Testing a Moderated Mediation Model","authors":"Piper Liping Liu, T. E. D. Yeo","doi":"10.1177/08944393231220487","DOIUrl":"https://doi.org/10.1177/08944393231220487","url":null,"abstract":"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.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"56 3","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597710","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: 10.1177/08944393231219685
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}
For qualitative data analysis (QDA), researchers assign codes to text segments to arrange the information into topics or concepts. These annotations facilitate information retrieval and the identification of emerging patterns in unstructured data. However, this metadata is typically not published or reused after the research. Subsequent studies with similar research questions require a new definition of codes and do not benefit from other analysts’ experience. Machine learning (ML) based classification seeded with such data remains a challenging task due to the ambiguity of code definitions and the inherent subjectivity of the exercise. Previous attempts to support QDA using ML rely on linear models and only examined individual datasets that were either smaller or coded specifically for this purpose. However, we show that modern approaches effectively capture at least part of the codes’ semantics and may generalize to multiple studies. We analyze the performance of multiple classifiers across three large real-world datasets. Furthermore, we propose an ML-based approach to identify semantic relations of codes in different studies to show thematic faceting, enhance retrieval of related content, or bootstrap the coding process. These are encouraging results that suggest how analysts might benefit from prior interpretation efforts, potentially yielding new insights into qualitative data.
{"title":"Bridging Qualitative Data Silos: The Potential of Reusing Codings Through Machine Learning Based Cross-Study Code Linking","authors":"Sergej Wildemann, Claudia Niederée, Erick Elejalde","doi":"10.1177/08944393231215459","DOIUrl":"https://doi.org/10.1177/08944393231215459","url":null,"abstract":"For qualitative data analysis (QDA), researchers assign codes to text segments to arrange the information into topics or concepts. These annotations facilitate information retrieval and the identification of emerging patterns in unstructured data. However, this metadata is typically not published or reused after the research. Subsequent studies with similar research questions require a new definition of codes and do not benefit from other analysts’ experience. Machine learning (ML) based classification seeded with such data remains a challenging task due to the ambiguity of code definitions and the inherent subjectivity of the exercise. Previous attempts to support QDA using ML rely on linear models and only examined individual datasets that were either smaller or coded specifically for this purpose. However, we show that modern approaches effectively capture at least part of the codes’ semantics and may generalize to multiple studies. We analyze the performance of multiple classifiers across three large real-world datasets. Furthermore, we propose an ML-based approach to identify semantic relations of codes in different studies to show thematic faceting, enhance retrieval of related content, or bootstrap the coding process. These are encouraging results that suggest how analysts might benefit from prior interpretation efforts, potentially yielding new insights into qualitative data.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"11 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346256","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 : 2023-11-10DOI: 10.1177/08944393231214026
Jinqi Men, Xiabing Zheng, Feifei Shan, Xiao Shi, Feng Yang
Attention on customer citizenship behavior (CCB) has increased markedly in recent years for both researchers and practitioners. However, existing research lacks deep understanding of the antecedents of CCB in social commerce, especially from the perspective of seller-consumer interaction relationships. Drawing on complexity theory, this study investigated the impacts of the causal configurations of three types of seller-consumer interaction relationships (experience of parasocial interaction (PSI), social interaction, and parasocial relationship (PSR)) on CCB in social commerce. To test this proposition, this study adopted fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 380 experienced social commerce consumers. Our findings indicate that the combination of social interaction and PSR leads to a high CCB among social commerce consumers. Moreover, borrowing from social exchange theory, we further employed partial least squares structural equation modeling (PLS-SEM) to reanalyze the research data. The PLS-SEM results are consistent with the fsQCA results.
{"title":"Exploring Customer Citizenship Behavior in Social Commerce From the Parasocial Perspective","authors":"Jinqi Men, Xiabing Zheng, Feifei Shan, Xiao Shi, Feng Yang","doi":"10.1177/08944393231214026","DOIUrl":"https://doi.org/10.1177/08944393231214026","url":null,"abstract":"Attention on customer citizenship behavior (CCB) has increased markedly in recent years for both researchers and practitioners. However, existing research lacks deep understanding of the antecedents of CCB in social commerce, especially from the perspective of seller-consumer interaction relationships. Drawing on complexity theory, this study investigated the impacts of the causal configurations of three types of seller-consumer interaction relationships (experience of parasocial interaction (PSI), social interaction, and parasocial relationship (PSR)) on CCB in social commerce. To test this proposition, this study adopted fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 380 experienced social commerce consumers. Our findings indicate that the combination of social interaction and PSR leads to a high CCB among social commerce consumers. Moreover, borrowing from social exchange theory, we further employed partial least squares structural equation modeling (PLS-SEM) to reanalyze the research data. The PLS-SEM results are consistent with the fsQCA results.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"8 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137620","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 : 2023-11-08DOI: 10.1177/08944393231212252
Kathryn Haglin, Soren Jordan, Grant Ferguson
Media stories on the economy tout automation as one of the biggest contemporary technological changes in America and argue that many Americans may lose their jobs because of it. Politicians and financial elites often promote a policy of Universal Basic Income (UBI) as a solution to the potential unemployment caused by automation, suggesting Americans should support UBI to protect them from this technological disruption. This linkage and basic descriptive findings are largely untested: we don’t know much about whether Americans support UBI, see automation as a threat to their job, or connect the two in any meaningful way. Using a Mechanical Turk survey of 3600 respondents, we examine the relationship between Americans’ perception of how much automation threatens their jobs, how much automation actually threatens their jobs, and their support for UBI. Our results indicate that while the public does not view automation as the same threat that elites do, Americans who believe their jobs will be automated are more likely to support UBI. These relationships, however, vary considerably by political party.
{"title":"They’re Coming for You! How Perceptions of Automation Affect Public Support for Universal Basic Income","authors":"Kathryn Haglin, Soren Jordan, Grant Ferguson","doi":"10.1177/08944393231212252","DOIUrl":"https://doi.org/10.1177/08944393231212252","url":null,"abstract":"Media stories on the economy tout automation as one of the biggest contemporary technological changes in America and argue that many Americans may lose their jobs because of it. Politicians and financial elites often promote a policy of Universal Basic Income (UBI) as a solution to the potential unemployment caused by automation, suggesting Americans should support UBI to protect them from this technological disruption. This linkage and basic descriptive findings are largely untested: we don’t know much about whether Americans support UBI, see automation as a threat to their job, or connect the two in any meaningful way. Using a Mechanical Turk survey of 3600 respondents, we examine the relationship between Americans’ perception of how much automation threatens their jobs, how much automation actually threatens their jobs, and their support for UBI. Our results indicate that while the public does not view automation as the same threat that elites do, Americans who believe their jobs will be automated are more likely to support UBI. These relationships, however, vary considerably by political party.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"23 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390654","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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