Computers in Human Behavior最新文献

英文中文

Participant behavior and community response in online mental health communities: Insights from Reddit

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-24 DOI: 10.1016/j.chb.2024.108544

Virginia Morini , Maria Sansoni , Giulio Rossetti , Dino Pedreschi , Carlos Castillo

The growing presence of online mutual-help communities has significantly changed how people access and provide mental health (MH) support. While extensive research has explored self-disclosure and social support dynamics within these communities, less is known about users’ distinctive behavioral patterns, posting intents, and community response. This study analyzed a large-scale, five-year Reddit dataset of 67 MH-related subreddits, comprising over 3.4 million posts and 24 million comments from approximately 2.4 million users. We categorized subreddits based on the Diagnostic and Statistical Manual of Mental Disorders and compared the behavioral patterns in these communities with Reddit non-MH ones. Leveraging Reddit’s post flair feature, we defined a ground truth for post intents and applied an automated classification method to infer intents across the dataset. We then used causal inference analysis to assess the effect of community responses on subsequent user behavior. Our analysis revealed that MH-related subreddits featured unique characteristics in content length, throwaway account usage, user actions, persistence, and community response. These online behaviors mirrored those in other mutual-help Reddit communities and resonated with offline patterns while diverging from non-support-oriented subreddits. We also found that seeking support and venting are the predominant posting intents, with users tending to maintain consistent intents over time. Furthermore, we observed that receiving comments and reactions significantly influenced users’ follow-up engagement, fostering increased participation. These findings highlight the supportive role of online MH communities and emphasize the need for tailored design to optimize user experience and support for individuals facing MH challenges.

{"title":"Participant behavior and community response in online mental health communities: Insights from Reddit","authors":"Virginia Morini , Maria Sansoni , Giulio Rossetti , Dino Pedreschi , Carlos Castillo","doi":"10.1016/j.chb.2024.108544","DOIUrl":"10.1016/j.chb.2024.108544","url":null,"abstract":"<div><div>The growing presence of online mutual-help communities has significantly changed how people access and provide mental health (MH) support. While extensive research has explored self-disclosure and social support dynamics within these communities, less is known about users’ distinctive behavioral patterns, posting intents, and community response. This study analyzed a large-scale, five-year Reddit dataset of 67 MH-related subreddits, comprising over 3.4 million posts and 24 million comments from approximately 2.4 million users. We categorized subreddits based on the Diagnostic and Statistical Manual of Mental Disorders and compared the behavioral patterns in these communities with Reddit non-MH ones. Leveraging Reddit’s post flair feature, we defined a ground truth for post intents and applied an automated classification method to infer intents across the dataset. We then used causal inference analysis to assess the effect of community responses on subsequent user behavior. Our analysis revealed that MH-related subreddits featured unique characteristics in content length, throwaway account usage, user actions, persistence, and community response. These online behaviors mirrored those in other mutual-help Reddit communities and resonated with offline patterns while diverging from non-support-oriented subreddits. We also found that seeking support and venting are the predominant posting intents, with users tending to maintain consistent intents over time. Furthermore, we observed that receiving comments and reactions significantly influenced users’ follow-up engagement, fostering increased participation. These findings highlight the supportive role of online MH communities and emphasize the need for tailored design to optimize user experience and support for individuals facing MH challenges.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108544"},"PeriodicalIF":9.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Worldwide connections of influencers who promote e-cigarettes on Instagram and TikTok: A social network analysis

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-23 DOI: 10.1016/j.chb.2024.108545

Julia Vassey , Ho-Chun Herbert Chang , Tom Valente , Jennifer B. Unger

Exposure to e-cigarette marketing on social media is a risk factor for e-cigarette use among youth. Tobacco brands use influencers to promote e-cigarettes on social media; however, influencer marketing has not been sufficiently studied. This study explored network connections and interactions through comments on social media posts between global nano- and micro-influencers (influencers with approximately 1,000 to 100,000 followers) and their audiences on Instagram and TikTok. We constructed directed unipartite networks among Instagram (N = 104) and TikTok (N = 100) influencers and users on Instagram (N = 55,622) and TikTok (N = 68,673) who commented on these influencers' posts in 2021–2022 (including influencers who commented on each other's posts). Comments to posts of users who were not classified as influencers were not collected. The Instagram network was denser (more interconnected) and more active compared to the TikTok network (1.48 times as high density, 281 times as high transitivity, and 86 times as high reciprocity). Both Instagram and TikTok networks contained heterophilic ties (i.e., influencers from different geographic regions such as Asia, North America and Europe connected to each other), indicating that influencers from different geographic regions engage with (comment on) each other's content, potentially exposing audiences to a wide variety of e-cigarette content. Influencers who promote e-cigarettes and post about lifestyle topics (e.g., fitness, fashion, gaming) occupy more central positions in the Instagram and TikTok networks than influencers who focus primarily on e-cigarette promotion, potentially exposing users who are not interested in tobacco-related content to harmful imagery of e-cigarettes. The findings emphasize the need for strengthening influencer marketing regulation on social media platforms popular among youth.

{"title":"Worldwide connections of influencers who promote e-cigarettes on Instagram and TikTok: A social network analysis","authors":"Julia Vassey , Ho-Chun Herbert Chang , Tom Valente , Jennifer B. Unger","doi":"10.1016/j.chb.2024.108545","DOIUrl":"10.1016/j.chb.2024.108545","url":null,"abstract":"<div><div>Exposure to e-cigarette marketing on social media is a risk factor for e-cigarette use among youth. Tobacco brands use influencers to promote e-cigarettes on social media; however, influencer marketing has not been sufficiently studied. This study explored network connections and interactions through comments on social media posts between global nano- and micro-influencers (influencers with approximately 1,000 to 100,000 followers) and their audiences on Instagram and TikTok. We constructed directed unipartite networks among Instagram (N = 104) and TikTok (N = 100) influencers and users on Instagram (N = 55,622) and TikTok (N = 68,673) who commented on these influencers' posts in 2021–2022 (including influencers who commented on each other's posts). Comments to posts of users who were not classified as influencers were not collected. The Instagram network was denser (more interconnected) and more active compared to the TikTok network (1.48 times as high density, 281 times as high transitivity, and 86 times as high reciprocity). Both Instagram and TikTok networks contained heterophilic ties (i.e., influencers from different geographic regions such as Asia, North America and Europe connected to each other), indicating that influencers from different geographic regions engage with (comment on) each other's content, potentially exposing audiences to a wide variety of e-cigarette content. Influencers who promote e-cigarettes and post about lifestyle topics (e.g., fitness, fashion, gaming) occupy more central positions in the Instagram and TikTok networks than influencers who focus primarily on e-cigarette promotion, potentially exposing users who are not interested in tobacco-related content to harmful imagery of e-cigarettes. The findings emphasize the need for strengthening influencer marketing regulation on social media platforms popular among youth.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108545"},"PeriodicalIF":9.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scaffolding source evaluation during document-based scientific inquiry: The contributions of document mapping and shared criteria scaffolds

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-20 DOI: 10.1016/j.chb.2024.108547

Sarit Barzilai , Danna Tal-Savir , Fayez Abed , Shiri Mor-Hagani , Clark A. Chinn

In times of widespread misinformation, students must learn to evaluate source trustworthiness so that they can determine the reliability of scientific information. The aim of our study was to advance the understanding of how epistemic scaffolds contribute to the development of students' source evaluation as they engage in scientific inquiry learning. In a quasi-experimental study with 137 9th-grade students, we examined the additive contribution of two types of epistemic scaffolds: (1) a document mapping scaffold designed to support cognitive engagement with sourcing processes and criteria by prompting learners to evaluate sources and to link sources and contents; and (2) a shared criteria scaffold designed to foster metacognitive understanding of source evaluation criteria by engaging learners in developing and discussing class criteria lists. Learning with the document mapping scaffold increased the use of source trustworthiness criteria to evaluate documents as well as critical source evaluations in argumentative essays. Adding the shared criteria scaffold led to a greater increase in the uses of sourcing criteria and critical source evaluation in the essays. The shared criteria scaffold also decreased selections of documents with unreliable sources and increased metacognitive understanding of sourcing criteria. The scaffolds did not impact source citations and selections of documents with reliable sources. These results demonstrate that learning with a document mapping scaffold, which encourages students to evaluate sources and to track who said what, can improve critical source evaluation to some extent. Yet, engaging students in developing and discussing shared criteria can enhance metacognitive growth and thus support greater improvement in critical source evaluation.

{"title":"Scaffolding source evaluation during document-based scientific inquiry: The contributions of document mapping and shared criteria scaffolds","authors":"Sarit Barzilai , Danna Tal-Savir , Fayez Abed , Shiri Mor-Hagani , Clark A. Chinn","doi":"10.1016/j.chb.2024.108547","DOIUrl":"10.1016/j.chb.2024.108547","url":null,"abstract":"<div><div>In times of widespread misinformation, students must learn to evaluate source trustworthiness so that they can determine the reliability of scientific information. The aim of our study was to advance the understanding of how epistemic scaffolds contribute to the development of students' source evaluation as they engage in scientific inquiry learning. In a quasi-experimental study with 137 9th-grade students, we examined the additive contribution of two types of epistemic scaffolds: (1) a document mapping scaffold designed to support cognitive engagement with sourcing processes and criteria by prompting learners to evaluate sources and to link sources and contents; and (2) a shared criteria scaffold designed to foster metacognitive understanding of source evaluation criteria by engaging learners in developing and discussing class criteria lists. Learning with the document mapping scaffold increased the use of source trustworthiness criteria to evaluate documents as well as critical source evaluations in argumentative essays. Adding the shared criteria scaffold led to a greater increase in the uses of sourcing criteria and critical source evaluation in the essays. The shared criteria scaffold also decreased selections of documents with unreliable sources and increased metacognitive understanding of sourcing criteria. The scaffolds did not impact source citations and selections of documents with reliable sources. These results demonstrate that learning with a document mapping scaffold, which encourages students to evaluate sources and to track who said what, can improve critical source evaluation to some extent. Yet, engaging students in developing and discussing shared criteria can enhance metacognitive growth and thus support greater improvement in critical source evaluation.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108547"},"PeriodicalIF":9.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the impact of social network structures on toxicity in online mental health communities

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-18 DOI: 10.1016/j.chb.2024.108542

Ezgi Akar

This study examines how structural social capital influences online toxicity within mental health communities. Using social network analysis and regression models, we analyze both direct and interaction effects of network centralities—degree, closeness, eigenvector, and betweenness—on toxicity in the r/MentalHealth subreddit. From a dataset of 90,626 posts, we constructed a network of 7562 users interconnected through 12,699,868 relationships. Our findings highlight the nuanced relationship between network positioning and toxic behavior. Users with a higher degree centrality, reflecting broad connectivity, exhibit lower toxicity levels, indicating that well-connected individuals contribute positively to community dynamics. Conversely, higher eigenvector, closeness, and betweenness centralities are associated with increased toxicity, suggesting that influential users, those centrally located, and those acting as bridges between network segments are more likely to engage in toxic behavior. Interaction effects further reveal complexities: for instance, well-connected and influential users tend to mitigate toxicity, while those who combine influence with proximity amplify it. These insights underscore the dual role of network structures in moderating or exacerbating harmful interactions. The study offers actionable strategies for fostering healthier online environments by leveraging network centralities to design targeted interventions and reduce toxicity in online mental health communities.

{"title":"Exploring the impact of social network structures on toxicity in online mental health communities","authors":"Ezgi Akar","doi":"10.1016/j.chb.2024.108542","DOIUrl":"10.1016/j.chb.2024.108542","url":null,"abstract":"<div><div>This study examines how structural social capital influences online toxicity within mental health communities. Using social network analysis and regression models, we analyze both direct and interaction effects of network centralities—degree, closeness, eigenvector, and betweenness—on toxicity in the r/MentalHealth subreddit. From a dataset of 90,626 posts, we constructed a network of 7562 users interconnected through 12,699,868 relationships. Our findings highlight the nuanced relationship between network positioning and toxic behavior. Users with a higher degree centrality, reflecting broad connectivity, exhibit lower toxicity levels, indicating that well-connected individuals contribute positively to community dynamics. Conversely, higher eigenvector, closeness, and betweenness centralities are associated with increased toxicity, suggesting that influential users, those centrally located, and those acting as bridges between network segments are more likely to engage in toxic behavior. Interaction effects further reveal complexities: for instance, well-connected and influential users tend to mitigate toxicity, while those who combine influence with proximity amplify it. These insights underscore the dual role of network structures in moderating or exacerbating harmful interactions. The study offers actionable strategies for fostering healthier online environments by leveraging network centralities to design targeted interventions and reduce toxicity in online mental health communities.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108542"},"PeriodicalIF":9.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deprivation's role in adolescent social media use and its links to life satisfaction

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-15 DOI: 10.1016/j.chb.2024.108541

Sebastian Kurten , Sakshi Ghai , Candice Odgers , Rogier A. Kievit , Amy Orben

Adolescents spend more time on social media than ever, making it necessary to understand the impact of social media use on their well-being. A largely unexplored, but potentially important, risk factor which may moderate effects of social media on well-being is material deprivation. Using 10-wave longitudinal data from 23,155 adolescents collected between 2009 and 2019, we test whether adolescents who spend more time on social media report lower levels of well-being, and whether differences in deprivation are associated with heightened sensitivity to positive or negative effects of their social media use. We find that deprived adolescents have less access to social media. However, those adolescents from deprived households who do have social media access spend slightly more time using it. Although we find that deprived adolescents are less satisfied with their lives, deprivation does not seem to affect the longitudinal link from time spent on social media to life satisfaction.

引用次数: 0

SBoCF: A deep learning-based sequential bag of convolutional features for human behavior quantification

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-15 DOI: 10.1016/j.chb.2024.108534

Baoli Lu , Dinghuang Zhang , Dalin Zhou , Achyut Shankar , Fahad Alasim , Mustufa Haider Abidi

The current methods for behavioral quantification heavily rely on manual annotation, which poses a significant challenge due to its labor-intensive and time-consuming nature. This reliance has become a bottleneck, particularly in the context of diagnosing Autism Spectrum Disorder (ASD), where early diagnosis and intervention are crucial for improving patient outcomes. One key area in ASD research is the assessment of atypical hand movements, which are frequently observed in individuals with ASD. To address the limitations of manual annotation, this paper proposes a deep learning-based method for automatically quantifying human behavior, focusing on hand motion evaluation. Specifically, we introduce a Sequential Bag of Convolutional Features (SBoCF) framework that combines the Bag of Words (BoW) approach with a customized skeleton-based CNN gesture classification model. This method allows for the automatic conversion of high-dimensional motion features into discrete behavior sequences, facilitating quantitative hand motor assessment based on established psychological research methods for hand behavior evaluation. Experiments using the DHG-14 dataset have shown promising results, demonstrating the potential of this method to replace traditional time-consuming manual video encoding processes.

{"title":"SBoCF: A deep learning-based sequential bag of convolutional features for human behavior quantification","authors":"Baoli Lu , Dinghuang Zhang , Dalin Zhou , Achyut Shankar , Fahad Alasim , Mustufa Haider Abidi","doi":"10.1016/j.chb.2024.108534","DOIUrl":"10.1016/j.chb.2024.108534","url":null,"abstract":"<div><div>The current methods for behavioral quantification heavily rely on manual annotation, which poses a significant challenge due to its labor-intensive and time-consuming nature. This reliance has become a bottleneck, particularly in the context of diagnosing Autism Spectrum Disorder (ASD), where early diagnosis and intervention are crucial for improving patient outcomes. One key area in ASD research is the assessment of atypical hand movements, which are frequently observed in individuals with ASD. To address the limitations of manual annotation, this paper proposes a deep learning-based method for automatically quantifying human behavior, focusing on hand motion evaluation. Specifically, we introduce a Sequential Bag of Convolutional Features (SBoCF) framework that combines the Bag of Words (BoW) approach with a customized skeleton-based CNN gesture classification model. This method allows for the automatic conversion of high-dimensional motion features into discrete behavior sequences, facilitating quantitative hand motor assessment based on established psychological research methods for hand behavior evaluation. Experiments using the DHG-14 dataset have shown promising results, demonstrating the potential of this method to replace traditional time-consuming manual video encoding processes.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108534"},"PeriodicalIF":9.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tell me more: Longitudinal relationships between online self-disclosure, co-rumination, and psychological well-being

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-14 DOI: 10.1016/j.chb.2024.108540

Anja Stevic , Kevin Koban , Jörg Matthes

Online self-disclosure is a key ingredient of social media. Although disclosure practices may strengthen close relationships, revealing emotional problems might also intensify co-rumination. Co-rumination refers to excessive interpersonal dwelling about negative feelings that might bear harmful consequences on psychological well-being. To disentangle the relationships between these constructs, emerging adults (16–21 years) completed a two-wave panel survey that included measures of online self-disclosure, co-rumination, loneliness, and self-esteem. Based on a measurement invariant structural equation model, findings suggest that only informational self-disclosure, but not emotional self-disclosure, positively predicts co-rumination over time. However, co-rumination positively predicts both informational and emotional self-disclosure suggesting that social encouragement matters for disclosing online. Unexpectedly, co-rumination has no association with loneliness or self-esteem over time. Thus, we find no longitudinal evidence for psychologically negative consequences of co-ruminative interactions, suggesting that online self-disclosure and co-rumination may be less harmful than previously thought.

{"title":"Tell me more: Longitudinal relationships between online self-disclosure, co-rumination, and psychological well-being","authors":"Anja Stevic , Kevin Koban , Jörg Matthes","doi":"10.1016/j.chb.2024.108540","DOIUrl":"10.1016/j.chb.2024.108540","url":null,"abstract":"<div><div>Online self-disclosure is a key ingredient of social media. Although disclosure practices may strengthen close relationships, revealing emotional problems might also intensify co-rumination. Co-rumination refers to excessive interpersonal dwelling about negative feelings that might bear harmful consequences on psychological well-being. To disentangle the relationships between these constructs, emerging adults (16–21 years) completed a two-wave panel survey that included measures of online self-disclosure, co-rumination, loneliness, and self-esteem. Based on a measurement invariant structural equation model, findings suggest that only informational self-disclosure, but not emotional self-disclosure, positively predicts co-rumination over time. However, co-rumination positively predicts both informational and emotional self-disclosure suggesting that social encouragement matters for disclosing online. Unexpectedly, co-rumination has no association with loneliness or self-esteem over time. Thus, we find no longitudinal evidence for psychologically negative consequences of co-ruminative interactions, suggesting that online self-disclosure and co-rumination may be less harmful than previously thought.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108540"},"PeriodicalIF":9.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The curvilinear effects of relative positions in smartphone app leaderboards on physical activity

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-13 DOI: 10.1016/j.chb.2024.108532

Yanxiang Yang , Joerg Koenigstorfer

To date, the nuanced effects of relative positions in leaderboards on health behaviors remain unclear. This study partly fills this void and aims to examine the effects of relative positions in smartphone app-based leaderboards on users’ physical activity. We propose and test a curvilinear effect in the form of a U shape. In Study 1 (n = 1,585), we use a survey-experimental design to manipulate 16 relative positions and a generalized additive model to test for the curvilinear effects on physical activity intentions. We find that intentions are higher at the top (ranks 1, 2, 3, and 4) and the bottom (85, 86, 87, and 88; 88 is the last rank) compared to upper to upper-mid (10, 19, 25, and 41) and lower to lower-mid (47, 63, 69, and 79) positions in the leaderboard. In Study 2 (n = 126), we use a behavior-experimental design to study changes in actual physical activity after individuals have been informed about their relative position in the leaderboard. Change in hand grip strength is used as an outcome. Again, we find evidence for U-shaped effects: physical activity increases among top-ranked persons (ranks 1 and 2) and bottom-ranked persons (87 and 88; 88 is the last rank) but there is a marginally significant decrease among persons ranked 25 and 69. The findings reveal that both top and bottom (vs. upper or lower mid) ranks in leaderboards may motivate individuals to become more active.

{"title":"The curvilinear effects of relative positions in smartphone app leaderboards on physical activity","authors":"Yanxiang Yang , Joerg Koenigstorfer","doi":"10.1016/j.chb.2024.108532","DOIUrl":"10.1016/j.chb.2024.108532","url":null,"abstract":"<div><div>To date, the nuanced effects of relative positions in leaderboards on health behaviors remain unclear. This study partly fills this void and aims to examine the effects of relative positions in smartphone app-based leaderboards on users’ physical activity. We propose and test a curvilinear effect in the form of a U shape. In Study 1 (<em>n</em> = 1,585), we use a survey-experimental design to manipulate 16 relative positions and a generalized additive model to test for the curvilinear effects on physical activity intentions. We find that intentions are higher at the top (ranks 1, 2, 3, and 4) and the bottom (85, 86, 87, and 88; 88 is the last rank) compared to upper to upper-mid (10, 19, 25, and 41) and lower to lower-mid (47, 63, 69, and 79) positions in the leaderboard. In Study 2 (<em>n</em> = 126), we use a behavior-experimental design to study changes in actual physical activity after individuals have been informed about their relative position in the leaderboard. Change in hand grip strength is used as an outcome. Again, we find evidence for U-shaped effects: physical activity increases among top-ranked persons (ranks 1 and 2) and bottom-ranked persons (87 and 88; 88 is the last rank) but there is a marginally significant decrease among persons ranked 25 and 69. The findings reveal that both top and bottom (vs. upper or lower mid) ranks in leaderboards may motivate individuals to become more active.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108532"},"PeriodicalIF":9.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The AI interface: Designing for the ideal machine-human experience

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-12 DOI: 10.1016/j.chb.2024.108539

Aparna Sundar, Tony Russell-Rose, Udo Kruschwitz, Karen Machleit

As artificial intelligence (AI) becomes increasingly embedded in daily life, designing intuitive, trustworthy, and emotionally resonant AI-human interfaces has emerged as a critical challenge. This editorial introduces a Special Issue that explores the psychology of AI experience design, focusing on how interfaces can foster seamless collaboration between humans and machines. Drawing on insights from diverse fields—healthcare, consumer technology, workplace dynamics, and cultural sectors—the papers in this collection highlight the complexities of trust, transparency, and emotional sensitivity in human-AI interaction. Key themes include designing AI systems that align with user perceptions and expectations, overcoming resistance through transparency and trust, and framing AI capabilities to reduce user anxiety. By synthesizing findings from eight diverse studies, this editorial underscores the need for AI interfaces to balance efficiency with empathy, addressing both functional and emotional dimensions of user experience. Ultimately, it calls for actionable frameworks to bridge research and practice, ensuring that AI systems enhance human lives through thoughtful, human-centered design.

{"title":"The AI interface: Designing for the ideal machine-human experience","authors":"Aparna Sundar, Tony Russell-Rose, Udo Kruschwitz, Karen Machleit","doi":"10.1016/j.chb.2024.108539","DOIUrl":"10.1016/j.chb.2024.108539","url":null,"abstract":"<div><div>As artificial intelligence (AI) becomes increasingly embedded in daily life, designing intuitive, trustworthy, and emotionally resonant AI-human interfaces has emerged as a critical challenge. This editorial introduces a Special Issue that explores the psychology of AI experience design, focusing on how interfaces can foster seamless collaboration between humans and machines. Drawing on insights from diverse fields—healthcare, consumer technology, workplace dynamics, and cultural sectors—the papers in this collection highlight the complexities of trust, transparency, and emotional sensitivity in human-AI interaction. Key themes include designing AI systems that align with user perceptions and expectations, overcoming resistance through transparency and trust, and framing AI capabilities to reduce user anxiety. By synthesizing findings from eight diverse studies, this editorial underscores the need for AI interfaces to balance efficiency with empathy, addressing both functional and emotional dimensions of user experience. Ultimately, it calls for actionable frameworks to bridge research and practice, ensuring that AI systems enhance human lives through thoughtful, human-centered design.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108539"},"PeriodicalIF":9.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling AI-assisted writing: How self-regulated learning influences writing outcomes

IF 9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2024-12-12 DOI: 10.1016/j.chb.2024.108538

Fangzhou Jin, Chin-Hsi Lin, Chun Lai

Academic writing is essential to academic and professional success, yet remains a challenge for many students. Artificial intelligence (AI) offers a potential solution, but most research on that possibility has focused on final written products rather than on the writing process. This study helps to fill that gap by modeling how key variables interact in generative AI-assisted writing processes, based on survey data from 1073 postgraduate students from 21 countries studying in the UK. We used structural equation modeling to categorize AI use into three levels, from basic to advanced: 1) for technical support, 2) for text development, and 3) for transformation. Self-regulated learning (SRL) strategies positively predicted all three types of AI use. Notably, while the most advanced use of AI (i.e., for writing transformation) significantly enhanced outcomes including critical thinking, motivation, and writing quality, whereas the most basic use (for technical support) did not predict such outcomes. This study further revealed that AI self-efficacy and writing self-efficacy were significant antecedents of self-regulation, suggesting the importance of supporting students’ self-efficacy in boosting self-regulation in AI use. This suggests that the key to writing-outcome improvement may not be to teach students different uses of AI, but to develop their self-regulation to the point that they can independently explore and apply advanced uses of this technology.

{"title":"Modeling AI-assisted writing: How self-regulated learning influences writing outcomes","authors":"Fangzhou Jin, Chin-Hsi Lin, Chun Lai","doi":"10.1016/j.chb.2024.108538","DOIUrl":"10.1016/j.chb.2024.108538","url":null,"abstract":"<div><div>Academic writing is essential to academic and professional success, yet remains a challenge for many students. Artificial intelligence (AI) offers a potential solution, but most research on that possibility has focused on final written products rather than on the writing process. This study helps to fill that gap by modeling how key variables interact in generative AI-assisted writing processes, based on survey data from 1073 postgraduate students from 21 countries studying in the UK. We used structural equation modeling to categorize AI use into three levels, from basic to advanced: 1) for technical support, 2) for text development, and 3) for transformation. Self-regulated learning (SRL) strategies positively predicted all three types of AI use. Notably, while the most advanced use of AI (i.e., for writing transformation) significantly enhanced outcomes including critical thinking, motivation, and writing quality, whereas the most basic use (for technical support) did not predict such outcomes. This study further revealed that AI self-efficacy and writing self-efficacy were significant antecedents of self-regulation, suggesting the importance of supporting students’ self-efficacy in boosting self-regulation in AI use. This suggests that the key to writing-outcome improvement may not be to teach students different uses of AI, but to develop their self-regulation to the point that they can independently explore and apply advanced uses of this technology.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"165 ","pages":"Article 108538"},"PeriodicalIF":9.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Computers in Human Behavior

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀