Pub Date : 2024-11-12DOI: 10.1016/j.chb.2024.108498
Madison Meston, David E. Williams
This paper explores an authentic way for brands to connect with the non-binary community, an understudied and underserved audience. With a call for better representation, this study is the first to investigate what role non-binary social media influencers (SMIs) may play in filling this gap. Using Interpretative Phenomenological Analysis, non-binary social media users were interviewed on their perceptions, thoughts, and feelings of non-binary SMIs. Three superordinate themes were discovered: (1) Motivations for following non-binary SMIs, (2) Popularity Factors of non-binary SMIs, and (3) Representation of the community through non-binary SMIs. The findings may be paired with existing literature to provide a basis for future research on influencer marketing to the non-binary community.
{"title":"Perception of non-binary social media users towards authentic non-binary social media influencers","authors":"Madison Meston, David E. Williams","doi":"10.1016/j.chb.2024.108498","DOIUrl":"10.1016/j.chb.2024.108498","url":null,"abstract":"<div><div>This paper explores an authentic way for brands to connect with the non-binary community, an understudied and underserved audience. With a call for better representation, this study is the first to investigate what role non-binary social media influencers (SMIs) may play in filling this gap. Using Interpretative Phenomenological Analysis, non-binary social media users were interviewed on their perceptions, thoughts, and feelings of non-binary SMIs. Three superordinate themes were discovered: (1) Motivations for following non-binary SMIs, (2) Popularity Factors of non-binary SMIs, and (3) Representation of the community through non-binary SMIs. The findings may be paired with existing literature to provide a basis for future research on influencer marketing to the non-binary community.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"163 ","pages":"Article 108498"},"PeriodicalIF":9.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656851","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}
Pub Date : 2024-11-12DOI: 10.1016/j.chb.2024.108494
Guo-Li Chiou , Meng-Jung Tsai , Chung-Yuan Hsu
The purpose of this study was to examine the effect of self-explanation prompts on game performance using eye-tracking techniques. We adopted an experimental design for the study. A total of 40 4th graders participated and were randomly assigned to either the experimental or control group. While the experimental group played a computer game with self-explanation prompts, the control group played the same game without any prompts. A Tobii 4C eye tracker with a sampling rate of 90 Hz was used to record the eye movements of the participants while they were playing the game. A pre-test and post-test were used to assess the participants’ understanding of the target concepts of the game before and after their game-playing, respectively. The results indicated an interaction effect between self-explanation and prior knowledge on the learning outcomes. Regarding the participants with lower-level prior knowledge, the experimental group gained significantly better scores on the posttest than the control group. In contrast, with respect to the participants with higher-level prior knowledge, the self-explanation prompts did not demonstrate a positive effect on the experimental group. Moreover, the results of the lag sequential analysis provide solid evidence of the differences in visual transitional patterns between the participants with different levels of prior knowledge while they were reacting to the self-explanation prompts. These results could account for the different learning outcomes achieved by the participants with different levels of prior knowledge in the experimental group.
{"title":"The effects of self-explanation on game-based learning: Evidence from eye-tracking analyses","authors":"Guo-Li Chiou , Meng-Jung Tsai , Chung-Yuan Hsu","doi":"10.1016/j.chb.2024.108494","DOIUrl":"10.1016/j.chb.2024.108494","url":null,"abstract":"<div><div>The purpose of this study was to examine the effect of self-explanation prompts on game performance using eye-tracking techniques. We adopted an experimental design for the study. A total of 40 4th graders participated and were randomly assigned to either the experimental or control group. While the experimental group played a computer game with self-explanation prompts, the control group played the same game without any prompts. A Tobii 4C eye tracker with a sampling rate of 90 Hz was used to record the eye movements of the participants while they were playing the game. A pre-test and post-test were used to assess the participants’ understanding of the target concepts of the game before and after their game-playing, respectively. The results indicated an interaction effect between self-explanation and prior knowledge on the learning outcomes. Regarding the participants with lower-level prior knowledge, the experimental group gained significantly better scores on the posttest than the control group. In contrast, with respect to the participants with higher-level prior knowledge, the self-explanation prompts did not demonstrate a positive effect on the experimental group. Moreover, the results of the lag sequential analysis provide solid evidence of the differences in visual transitional patterns between the participants with different levels of prior knowledge while they were reacting to the self-explanation prompts. These results could account for the different learning outcomes achieved by the participants with different levels of prior knowledge in the experimental group.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"163 ","pages":"Article 108494"},"PeriodicalIF":9.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656364","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}
Pub Date : 2024-11-12DOI: 10.1016/j.chb.2024.108500
Navneet Kumar Singh , Rajeev Kumar Ray , Nikee Silayach , Devi Prasad Dash , Amit Singh
As the metaverse emerges as a new frontier of human interaction, understanding public perceptions of crime in virtual spaces becomes crucial. This study delves into public perceptions of such crimes, focusing on reported incidents of sexual assault in virtual reality environments. We uncover complex dynamics, shaping threat perception in immersive digital realms by analysing YouTube comments through an innovative mixed-methods approach combining machine learning and empirical analysis. Our findings reveal that social norms and expectations are pivotal in influencing perceptions of threats, while technology-mediated interactions correlate with reduced perceived risks. Surprisingly, the oft-discussed blurring of virtual and physical realities shows no significant impact on threat perception. This research contributes to the expanding literature on the social construction of reality and public perception of emerging technologies. The results have implications for the development and governance of metaverse platforms, highlighting the necessity for comprehensive user education initiatives and culturally sensitive approaches to community guidelines and safety features.
{"title":"Avatars at risk: Exploring public response to sexual violence in immersive digital spaces","authors":"Navneet Kumar Singh , Rajeev Kumar Ray , Nikee Silayach , Devi Prasad Dash , Amit Singh","doi":"10.1016/j.chb.2024.108500","DOIUrl":"10.1016/j.chb.2024.108500","url":null,"abstract":"<div><div>As the metaverse emerges as a new frontier of human interaction, understanding public perceptions of crime in virtual spaces becomes crucial. This study delves into public perceptions of such crimes, focusing on reported incidents of sexual assault in virtual reality environments. We uncover complex dynamics, shaping threat perception in immersive digital realms by analysing YouTube comments through an innovative mixed-methods approach combining machine learning and empirical analysis. Our findings reveal that social norms and expectations are pivotal in influencing perceptions of threats, while technology-mediated interactions correlate with reduced perceived risks. Surprisingly, the oft-discussed blurring of virtual and physical realities shows no significant impact on threat perception. This research contributes to the expanding literature on the social construction of reality and public perception of emerging technologies. The results have implications for the development and governance of metaverse platforms, highlighting the necessity for comprehensive user education initiatives and culturally sensitive approaches to community guidelines and safety features.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"163 ","pages":"Article 108500"},"PeriodicalIF":9.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656365","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}
Pub Date : 2024-11-12DOI: 10.1016/j.chb.2024.108499
Silke Van Dijck , Silke Van den Eynde , Paul Enzlin
Sexting behaviour is mostly viewed from a negative perspective. In response to this negative perspective, and in line with calls for a shift towards a more sex-positive approach, this paper provides a scoping review of what is known about the benefits of sexting. Based on a thematic analysis of the content of 70 scientific papers on the benefits of sexting, three main themes were identified: 1) individual benefits resulting from the sexting act itself (i.e., sexual exploration and experimentation, sexual pleasure and non-sexual pleasure), 2) individual benefits resulting from the reaction of the sexting partner (i.e., body image and self-image, sexual agency and validation), and, 3) relational benefits of sexting (i.e., sexual and emotional connectedness, sexting as a way to build bridges). The benefits of sexting are linked to theoretical frameworks related to both the online and offline (sexual) worlds. This paper illustrates how sexting is an online extension of offline sexuality that includes both positive and negative aspects. It is important that also the benefits of sexting receive more attention in research and society, as this will contribute to healthier, more nuanced and more accurate discourses on sexting.
{"title":"The bright side of sexting: A scoping review on its benefits","authors":"Silke Van Dijck , Silke Van den Eynde , Paul Enzlin","doi":"10.1016/j.chb.2024.108499","DOIUrl":"10.1016/j.chb.2024.108499","url":null,"abstract":"<div><div>Sexting behaviour is mostly viewed from a negative perspective. In response to this negative perspective, and in line with calls for a shift towards a more sex-positive approach, this paper provides a scoping review of what is known about the benefits of sexting. Based on a thematic analysis of the content of 70 scientific papers on the benefits of sexting, three main themes were identified: 1) individual benefits resulting from the sexting act itself (i.e., sexual exploration and experimentation, sexual pleasure and non-sexual pleasure), 2) individual benefits resulting from the reaction of the sexting partner (i.e., body image and self-image, sexual agency and validation), and, 3) relational benefits of sexting (i.e., sexual and emotional connectedness, sexting as a way to build bridges). The benefits of sexting are linked to theoretical frameworks related to both the online and offline (sexual) worlds. This paper illustrates how sexting is an online extension of offline sexuality that includes both positive and negative aspects. It is important that also the benefits of sexting receive more attention in research and society, as this will contribute to healthier, more nuanced and more accurate discourses on sexting.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"164 ","pages":"Article 108499"},"PeriodicalIF":9.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745953","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}
Pub Date : 2024-11-08DOI: 10.1016/j.chb.2024.108492
Aiping Shao , Zhi Lu , Bu Zhong , Stephanie Q. Liu , Wei Lu
AI educational tool companies are fiercely competing to capture a larger share of the highly competitive AI-in-education market. A key strategy for success is to enhance user favorability and adoption. However, whether users prefer a human or an AI in the role of educator remains an important yet underexplored question. This research conducted three behavioral studies to examine user behavior toward AI-powered educational tools versus human educators, focusing on users' intentions to use these tools and their word-of-mouth (WOM) recommendations. Our findings show that users are more inclined to prefer human educators for their children and are more likely to share this preference with others (Studies 1 and 2). The underlying reason is that users perceive human educators as more capable than AI tools (Study 2). Study 3 further reveals that the type of problem-solving moderates the effect of the educational approach on both intentions to use and WOM. Specifically, users believe that AI tools are better suited for conventional problem-solving, while human educators are preferred for creative problem-solving. Our research is the first to investigate users' adoption of AI educational tools compared to human educators and the underlying mechanism. AI educational tool providers are advised to address users' negative perceptions and alleviate their concerns by demonstrating the competence of AI tools or by actively engaging with users to foster their competence in AI's capabilities. These novel insights contribute to the rapidly growing field of AI research and offer valuable implications for AI educational tools seeking success in the marketplace.
{"title":"Human touch vs. AI tech: Understanding user preferences in the future of education","authors":"Aiping Shao , Zhi Lu , Bu Zhong , Stephanie Q. Liu , Wei Lu","doi":"10.1016/j.chb.2024.108492","DOIUrl":"10.1016/j.chb.2024.108492","url":null,"abstract":"<div><div>AI educational tool companies are fiercely competing to capture a larger share of the highly competitive AI-in-education market. A key strategy for success is to enhance user favorability and adoption. However, whether users prefer a human or an AI in the role of educator remains an important yet underexplored question. This research conducted three behavioral studies to examine user behavior toward AI-powered educational tools versus human educators, focusing on users' intentions to use these tools and their word-of-mouth (WOM) recommendations. Our findings show that users are more inclined to prefer human educators for their children and are more likely to share this preference with others (Studies 1 and 2). The underlying reason is that users perceive human educators as more capable than AI tools (Study 2). Study 3 further reveals that the type of problem-solving moderates the effect of the educational approach on both intentions to use and WOM. Specifically, users believe that AI tools are better suited for conventional problem-solving, while human educators are preferred for creative problem-solving. Our research is the first to investigate users' adoption of AI educational tools compared to human educators and the underlying mechanism. AI educational tool providers are advised to address users' negative perceptions and alleviate their concerns by demonstrating the competence of AI tools or by actively engaging with users to foster their competence in AI's capabilities. These novel insights contribute to the rapidly growing field of AI research and offer valuable implications for AI educational tools seeking success in the marketplace.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"164 ","pages":"Article 108492"},"PeriodicalIF":9.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745950","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}
Pub Date : 2024-11-06DOI: 10.1016/j.chb.2024.108493
Muhammad Rashid Naeem , Muhammad Asghar Khan , Mansoor Khan , Omar Alruwaili , Ibrahim Alrashdi , Saad Alanazi
Ubiquitous learning enables software analysts to acquire knowledge and design test oracles from online resources including developer documentations, forums and collaborative platforms. Test oracles are primarily designed by humans and may contain quality defects. Mutation testing can facilitate the adaptability of ubiquitous learning to enhance software quality assurance. Unfortunately, mutation testing generates a large number of faulty versions known as mutants to analyze the quality of test oracles which is computationally expensive. In this paper, we proposed minimal path selection strategy to select fewer and non-trivial mutants to analyze diverse test oracles realizing ubiquitous learning environments. The proposed selection strategy explores the relationship between faulty conditions and output statements to identify feasible paths. To establish a ubiquitous learning setup, diverse test oracles are selected by incorporating user input, automated scripts and developer insights. This approach aims to enhance learner engagement and effectively design test oracles. The empirical evaluation and state-of-the-art comparison of various Java-built software programs demonstrated the potential of ubiquitous learning in software quality assurance by reducing 75% of the generated mutants. Furthermore, only 64%–85% of adequate test oracles were required to meet the desired quality assurance criteria. The study highlights the significance of personalized and adaptive approaches to assure software quality in ubiquitous learning environments.
{"title":"Enhancing software quality assurance in ubiquitous learning environments through mutation testing and diverse test oracles","authors":"Muhammad Rashid Naeem , Muhammad Asghar Khan , Mansoor Khan , Omar Alruwaili , Ibrahim Alrashdi , Saad Alanazi","doi":"10.1016/j.chb.2024.108493","DOIUrl":"10.1016/j.chb.2024.108493","url":null,"abstract":"<div><div>Ubiquitous learning enables software analysts to acquire knowledge and design test oracles from online resources including developer documentations, forums and collaborative platforms. Test oracles are primarily designed by humans and may contain quality defects. Mutation testing can facilitate the adaptability of ubiquitous learning to enhance software quality assurance. Unfortunately, mutation testing generates a large number of faulty versions known as mutants to analyze the quality of test oracles which is computationally expensive. In this paper, we proposed minimal path selection strategy to select fewer and non-trivial mutants to analyze diverse test oracles realizing ubiquitous learning environments. The proposed selection strategy explores the relationship between faulty conditions and output statements to identify feasible paths. To establish a ubiquitous learning setup, diverse test oracles are selected by incorporating user input, automated scripts and developer insights. This approach aims to enhance learner engagement and effectively design test oracles. The empirical evaluation and state-of-the-art comparison of various Java-built software programs demonstrated the potential of ubiquitous learning in software quality assurance by reducing 75% of the generated mutants. Furthermore, only 64%–85% of adequate test oracles were required to meet the desired quality assurance criteria. The study highlights the significance of personalized and adaptive approaches to assure software quality in ubiquitous learning environments.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"163 ","pages":"Article 108493"},"PeriodicalIF":9.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656361","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}
Pub Date : 2024-10-30DOI: 10.1016/j.chb.2024.108483
Jiayi Lu , Xinhai Tang , Xingyue Jin , Xuerong Luo , Tianqing Fan , Yanmei Shen
Objective
Internet addiction (IA), insomnia, and suicide are serious public health issues with profound implications that deserve further attention. Yet few studies have used network analysis to explore their interactions and relationship with physical activity (PA) from the symptom dimension level. This study aims to construct the IA-PA-Insomnia network and to explore the symptoms that exhibit the strongest correlation with suicidal ideation.
Methods
This research enrolled 6573 students aged 12–17 years old from two schools. Internet addiction (Revised Chinese Internet Addiction Scale), insomnia (Athens Insomnia Scale), physical activity, suicide ideation, and demographics were employed in the collection of data. The ggmModSelect algorithm was applied to establish the network structure and identify central and bridge symptoms.
Results
The prevalence of Internet addiction, insomnia, and suicide ideation is 8.7%, 17.5%, and 15. 8%, respectively. “Tolerance,” “sleep quality,” and “time management” were the core symptoms in the network. The strongest bridge nodes were “tolerance,” “daytime sleepiness,” and “time management”. “Daytime mood” shared the most direct associations with suicide ideation. Physical activity was inversely associated with all symptom nodes, especially daytime mood and interpersonal and health-related problems.
Conclusions
Tolerance and time management are crucial factors in maintaining the entire network and triggering the co-occurrence of different psychological conditions. Daytime mood was most associated with suicidal ideation and regular exercise can help with IA, insomnia, and suicidal ideation. There is a need to target these symptoms to protect the physical and mental health of adolescents.
{"title":"A network analysis-based study of the correlations between internet addiction, insomnia, physical activity, and suicide ideation in adolescents","authors":"Jiayi Lu , Xinhai Tang , Xingyue Jin , Xuerong Luo , Tianqing Fan , Yanmei Shen","doi":"10.1016/j.chb.2024.108483","DOIUrl":"10.1016/j.chb.2024.108483","url":null,"abstract":"<div><h3>Objective</h3><div>Internet addiction (IA), insomnia, and suicide are serious public health issues with profound implications that deserve further attention. Yet few studies have used network analysis to explore their interactions and relationship with physical activity (PA) from the symptom dimension level. This study aims to construct the IA-PA-Insomnia network and to explore the symptoms that exhibit the strongest correlation with suicidal ideation.</div></div><div><h3>Methods</h3><div>This research enrolled 6573 students aged 12–17 years old from two schools. Internet addiction (Revised Chinese Internet Addiction Scale), insomnia (Athens Insomnia Scale), physical activity, suicide ideation, and demographics were employed in the collection of data. The ggmModSelect algorithm was applied to establish the network structure and identify central and bridge symptoms.</div></div><div><h3>Results</h3><div>The prevalence of Internet addiction, insomnia, and suicide ideation is 8.7%, 17.5%, and 15. 8%, respectively. “Tolerance,” “sleep quality,” and “time management” were the core symptoms in the network. The strongest bridge nodes were “tolerance,” “daytime sleepiness,” and “time management”. “Daytime mood” shared the most direct associations with suicide ideation. Physical activity was inversely associated with all symptom nodes, especially daytime mood and interpersonal and health-related problems.</div></div><div><h3>Conclusions</h3><div>Tolerance and time management are crucial factors in maintaining the entire network and triggering the co-occurrence of different psychological conditions. Daytime mood was most associated with suicidal ideation and regular exercise can help with IA, insomnia, and suicidal ideation. There is a need to target these symptoms to protect the physical and mental health of adolescents.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"163 ","pages":"Article 108483"},"PeriodicalIF":9.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560674","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}
Pub Date : 2024-10-30DOI: 10.1016/j.chb.2024.108484
Hongyi Lin , Guohao He , Hong Zheng , Jiaoyang Ai
Research has identified a robust connection between adverse childhood experiences and smartphone addiction. However, few studies have explored the close links between various smartphone application addictions and their co-occurrence. To bridge this gap, the current study used one pilot study and two formal studies to explore the co-occurrence of different smartphone application addictions, as well as their potential risk factors and internal mechanisms. In the pilot study (N = 118), adolescents with smartphone addictions were interviewed to determine their regularly-used applications and content type. In Study 1 (N = 1962), network analysis was used to assess six application preferences within the broader umbrella of smartphone addiction, and the potential relationship effect between adverse childhood experiences and smartphone addiction. In Study 2 (N = 835), the causal relationship was further verified through a longitudinal study which also found that alexithymia and social sensitivity played mediating roles in smartphone application addition. The application addiction with the highest centrality was for short videos, followed by games and then social media applications. In addition, a range of social experiences associated with violence (e.g., neglect and bullying) were found to be key determinants of smartphone addiction, in that these experiences hinder adolescents' socialization by increasing their alexithymia and social sensitivity, leading them to become glued to their smartphones. The findings of this study contribute to a better understanding of how adverse childhood experiences increase adolescents’ risk of smartphone addiction, while also providing new insights into the co-occurrence of addictions to various common smartphone applications.
{"title":"What makes Chinese adolescents glued to their smartphones? Using network analysis and three-wave longitudinal analysis to assess how adverse childhood experiences influence smartphone addiction","authors":"Hongyi Lin , Guohao He , Hong Zheng , Jiaoyang Ai","doi":"10.1016/j.chb.2024.108484","DOIUrl":"10.1016/j.chb.2024.108484","url":null,"abstract":"<div><div>Research has identified a robust connection between adverse childhood experiences and smartphone addiction. However, few studies have explored the close links between various smartphone application addictions and their co-occurrence. To bridge this gap, the current study used one pilot study and two formal studies to explore the co-occurrence of different smartphone application addictions, as well as their potential risk factors and internal mechanisms. In the pilot study (<em>N</em> = 118), adolescents with smartphone addictions were interviewed to determine their regularly-used applications and content type. In Study 1 (<em>N</em> = 1962), network analysis was used to assess six application preferences within the broader umbrella of smartphone addiction, and the potential relationship effect between adverse childhood experiences and smartphone addiction. In Study 2 (<em>N</em> = 835), the causal relationship was further verified through a longitudinal study which also found that alexithymia and social sensitivity played mediating roles in smartphone application addition. The application addiction with the highest centrality was for short videos, followed by games and then social media applications. In addition, a range of social experiences associated with violence (e.g., neglect and bullying) were found to be key determinants of smartphone addiction, in that these experiences hinder adolescents' socialization by increasing their alexithymia and social sensitivity, leading them to become glued to their smartphones. The findings of this study contribute to a better understanding of how adverse childhood experiences increase adolescents’ risk of smartphone addiction, while also providing new insights into the co-occurrence of addictions to various common smartphone applications.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"163 ","pages":"Article 108484"},"PeriodicalIF":9.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573177","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}
Pub Date : 2024-10-29DOI: 10.1016/j.chb.2024.108482
Faisal Mehmood , Xin Guo , Enqing Chen , Muhammad Azeem Akbar , Arif Ali Khan , Sami Ullah
Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. GCN model's hierarchical structure and human action recognition input diversity make this a problematic approach; (II) Bone length and orientation are understudied due to their significance and variance in HAR. For this purpose, we introduce an Extended Multi-stream Temporal-attention Adaptive GCN (EMS-TAGCN). By training the network topology of the proposed model either consistently or independently according to the input data, this data-based technique makes graphs more flexible and faster to adapt to a new dataset. A spatial, temporal, and channel attention module helps the adaptive graph convolutional layer focus on joints, frames, and features. Hence, a multi-stream framework representing bones, joints, and their motion enhances recognition accuracy. Our proposed model outperforms the NTU RGBD for CS and CV by 0.6% and 1.4%, respectively, while Kinetics-skeleton Top-1 and Top-5 are 1.4% improved, UCF-101 has improved 2.34% accuracy and HMDB-51 dataset has significantly improved 1.8% accuracy. According to the results, our model has performed better than the other models. Our model consistently outperformed other models, and the results were statistically significant that demonstrating the superiority of our model for the task of HAR and its ability to provide the most reliable and accurate results.
{"title":"Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)","authors":"Faisal Mehmood , Xin Guo , Enqing Chen , Muhammad Azeem Akbar , Arif Ali Khan , Sami Ullah","doi":"10.1016/j.chb.2024.108482","DOIUrl":"10.1016/j.chb.2024.108482","url":null,"abstract":"<div><div>Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. GCN model's hierarchical structure and human action recognition input diversity make this a problematic approach; (II) Bone length and orientation are understudied due to their significance and variance in HAR. For this purpose, we introduce an Extended Multi-stream Temporal-attention Adaptive GCN (EMS-TAGCN). By training the network topology of the proposed model either consistently or independently according to the input data, this data-based technique makes graphs more flexible and faster to adapt to a new dataset. A spatial, temporal, and channel attention module helps the adaptive graph convolutional layer focus on joints, frames, and features. Hence, a multi-stream framework representing bones, joints, and their motion enhances recognition accuracy. Our proposed model outperforms the NTU RGBD for CS and CV by 0.6% and 1.4%, respectively, while Kinetics-skeleton Top-1 and Top-5 are 1.4% improved, UCF-101 has improved 2.34% accuracy and HMDB-51 dataset has significantly improved 1.8% accuracy. According to the results, our model has performed better than the other models. Our model consistently outperformed other models, and the results were statistically significant that demonstrating the superiority of our model for the task of HAR and its ability to provide the most reliable and accurate results.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"163 ","pages":"Article 108482"},"PeriodicalIF":9.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593199","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}
Pub Date : 2024-10-28DOI: 10.1016/j.chb.2024.108481
M. Claudia tom Dieck, Dai-In Danny Han, Timothy Jung
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