IEEE Transactions on Computational Social Systems最新文献_第3页

Leveraging Cooperative Learning Algorithms for Early Detection of Mental Health Issues Using Intelligence of Social Things Data 利用社会事物数据的智能，利用合作学习算法早期发现心理健康问题

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-10-17 DOI: 10.1109/TCSS.2025.3609251

Fan Gao;Himanshu Dhumras;Garima Thakur;Xingsi Xue;Ya-Juan Yang

{"title":"Leveraging Cooperative Learning Algorithms for Early Detection of Mental Health Issues Using Intelligence of Social Things Data","authors":"Fan Gao;Himanshu Dhumras;Garima Thakur;Xingsi Xue;Ya-Juan Yang","doi":"10.1109/TCSS.2025.3609251","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3609251","url":null,"abstract":"The exponential proliferation of social media and Internet of Things (IoT) technologies has paved the way for transformative applications in public health, particularly for the early detection of mental health concerns. This study introduces an innovative framework leveraging cooperative learning algorithms combined with intelligence of social things (IoST) data to enhance mental health issue detection. By integrating multimodal user data from social platforms, wearable devices, and IoT sensors, the proposed approach achieves superior predictive accuracy, with the random forest-based model outperforming benchmarks at 88% accuracy and a 0.90 receiver operating characteristic area under the curve (ROC-AUC). The incorporation of key features, including social homophily and real-time behavioral metrics, significantly bolsters detection rates. Ethical considerations, including data privacy and bias reduction, are meticulously addressed, ensuring a scalable and user-centered solution. The findings underscore the potential of IoST-driven cooperative algorithms to revolutionize mental health interventions by enabling timely, precise, and ethical detection systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1091-1099"},"PeriodicalIF":4.5,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

fNIRS-SpikeNet: A Spiking Neural Network Framework for Cognitive Load Classification in Cooperative Learning Environments 合作学习环境下认知负荷分类的尖峰神经网络框架

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-10-14 DOI: 10.1109/TCSS.2025.3598044

Peijiang Zhang;Tao Cheng;Yuande Jiang;Xiaochuan Zou;Xiaoming Chen

引用次数: 0

IEEE Transactions on Computational Social Systems Information for Authors IEEE计算社会系统信息汇刊

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-10-06 DOI: 10.1109/TCSS.2025.3608423

引用次数: 0

Flexible Electrodes: Catalyzing Commercial Revolution of Brain–Computer Interfaces 柔性电极：催化脑机接口的商业革命

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-10-06 DOI: 10.1109/TCSS.2025.3606586

Ran Cai;Donglei Chen;Lixin Dong;Bin Hu

引用次数: 0

IEEE Transactions on Computational Social Systems Publication Information IEEE计算社会系统汇刊信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-10-06 DOI: 10.1109/TCSS.2025.3608419

引用次数: 0

Guest Editorial: Special Issue on Trends in Social Multimedia Computing: Models, Methodologies, and Applications 特刊：社会多媒体计算趋势：模型、方法和应用

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-10-06 DOI: 10.1109/TCSS.2025.3606570

Amit Kumar Singh;Jungong Han;Stefano Berretti

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-10-06 DOI: 10.1109/TCSS.2025.3608421

引用次数: 0

CDC: Enhancing Scene Graph Generation for IoST-Driven Social Behavioral Modeling With Cooperative Dual Classifier 用协作双分类器增强iost驱动的社会行为建模的场景图生成

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-09-22 DOI: 10.1109/TCSS.2025.3600391

Zhaodi Wang;Yangyan Zeng;Biao Leng;Xiaokang Zhou

{"title":"CDC: Enhancing Scene Graph Generation for IoST-Driven Social Behavioral Modeling With Cooperative Dual Classifier","authors":"Zhaodi Wang;Yangyan Zeng;Biao Leng;Xiaokang Zhou","doi":"10.1109/TCSS.2025.3600391","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3600391","url":null,"abstract":"Scene graph generation (SGG) plays an important role in the intelligence of social things (IoST) framework by extracting structured semantic representations from social device data, thereby supporting advanced scene understanding and behavioral-cultural modeling. However, the intrinsic long-tail nature of real-world social device data, coupled with the semantic entanglement between head and tail categories (e.g., “on” versus “standing on”), presents significant challenges for fine-grained SGG. This often results in biased models and suboptimal generalization to rare but semantically informative relations. To address these issues, we propose a novel cooperative dual classifier (CDC) framework for fine-grained SGG in IoST-driven social systems. CDC introduces a cooperative learning mechanism that combines two classifiers. The frozen prototype classifier is designed with maximum interclass margins to alleviate class imbalance. In parallel, a learnable classifier dynamically adjusts decision boundaries to improve discriminative precision. To further enhance the integration between the two classifiers, we introduce a weight knowledge transfer (WKT) module and a collaborative constraint term, facilitating robust adaptation to tail categories. Extensive experiments on the Visual Genome and GQA datasets demonstrate that CDC outperforms state-of-the-art SGG methods, particularly in modeling fine-grained relations under long-tail distributions. These results highlight the capability of CDC to advance semantic understanding of complex behavioral and cultural patterns within computational social systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1120-1133"},"PeriodicalIF":4.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI Bots and the Distortion of Online Social Opinions 人工智能机器人与网络社会舆论的扭曲

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-09-19 DOI: 10.1109/TCSS.2025.3599531

Fei Meng;Jiamin Yang;Wei Du;Jianliang Wei

{"title":"AI Bots and the Distortion of Online Social Opinions","authors":"Fei Meng;Jiamin Yang;Wei Du;Jianliang Wei","doi":"10.1109/TCSS.2025.3599531","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3599531","url":null,"abstract":"This study explores how proactive AI bots distort online opinion dynamics through strategic behavior in social networks. Extending the bounded confidence model, we simulate heterogeneous bot strategies along two dimensions: activeness and interaction preference. We introduce three distortion metrics including entropy, tendency, and polarization to quantify systemic effects. Simulations on scale-free networks show that even 5–15% bot presence can significantly alter opinion patterns, increasing fragmentation, bias, and convergence time, and random interaction strategies were found to generate consistently higher entropy, tendency, and polarization across various bot ratios, and moderate bot activeness (around 2–3 times that of humans) achieved peak distortion before diminishing returns set in. Validation using a real-world Twitter/X dataset confirms alignment between simulated and actual bot behavior. Findings suggest bot influence stems from behavioral nuance rather than volume, underscoring the need for detection mechanisms targeting dynamic interaction patterns. This work advances computational modeling by revealing how algorithmic agents reshape collective beliefs.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1146-1158"},"PeriodicalIF":4.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating User Relationships and Features for Intelligence of Social Things Aware Information Diffusion Prediction 集成用户关系与特征的社会物智能感知信息扩散预测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-08-11 DOI: 10.1109/TCSS.2025.3588781

Bhawani Sankar Panigrahi;Mohammed E. Seno;Balasubramani Murugesan;Omar Isam;Vemula Jasmine Sowmya;K.D.V. Prasad;Deepak Gupta;Jumaniyazov Inomjon Turayevich;Richard Rivera

{"title":"Integrating User Relationships and Features for Intelligence of Social Things Aware Information Diffusion Prediction","authors":"Bhawani Sankar Panigrahi;Mohammed E. Seno;Balasubramani Murugesan;Omar Isam;Vemula Jasmine Sowmya;K.D.V. Prasad;Deepak Gupta;Jumaniyazov Inomjon Turayevich;Richard Rivera","doi":"10.1109/TCSS.2025.3588781","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3588781","url":null,"abstract":"In the intelligence of social things (IoST) paradigm, where interconnected devices and social networks create a dynamic ecosystem, understanding information diffusion is essential. IoST integrates user interactions, device behaviors, and contextual factors, adding complexity to information networks and necessitating accurate prediction models. This work analyses user behavior in terms of both group and individual relationships and presents an information propagation prediction model that combines information propagation topology features with user relationship representations. Information diffusion prediction analyzes patterns of spread in networks to understand and forecast propagation processes. Existing studies emphasize social and dynamic influence relationships within user groups but often neglect user similarity in group relations and intrinsic factors affecting individual sharing decisions. To address these gaps, a novel model is proposed, combining user relationship representations and diffusion topological features. At the group level, a user cooccurrence graph captures similarity relationship, integrating these with diffusion topology to analyze group interactions. At the individual level, user-specific feature representations and influence factor vectors address intrinsic motivations for sharing. Experimental results validate the model’s efficacy, achieving performance improvements on public datasets. On the Memetracker dataset, the model increased MAP@k by 6.54% and hits@k by 2.75%, demonstrating its ability to capture both group and individual dynamics for enhanced diffusion prediction.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1069-1078"},"PeriodicalIF":4.5,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0