IEEE Transactions on Computational Social Systems最新文献

{"title":"Guest Editorial: Special Issue on Intelligence of Social Things-Enabled Cooperative Learning for Behavioral-Cultural Modeling","authors":"Chinmay Chakraborty;Bhuvan Unhelkar;Saïd Mahmoudi;Martin Margala;Sayonara Barbosa","doi":"10.1109/TCSS.2026.3653516","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3653516","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1028-1030"},"PeriodicalIF":4.5,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2025 Index IEEE Transactions on Computational Social Systems Vol. 12","authors":"","doi":"10.1109/TCSS.2026.3652476","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3652476","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 6","pages":"1-98"},"PeriodicalIF":4.5,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345504","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Approach for Social IoT and Mental Health Emotion Detection From Videos","authors":"Abeer S. Almogren;Ghada Moh. Samir Elhessewi;Mukhtar Ghaleb;Hany Mahgoub;Asma A. Alhashmi;Umkalthoom Alzubaidi;Asmaa Mansour Alghamdi;Nojood O. Aljehane","doi":"10.1109/TCSS.2025.3628196","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3628196","url":null,"abstract":"Social Internet of Things (IoT) creates an integrated ecosystem for mental health analysis through video emotion analysis. These systems can be transformed into real-time monitoring tools and support mental health care systems such as online education, interviews, and medical treatments. This research proposed video-based emotion classification with a novel optimized hybrid deep learning model. From the video, facial expression features such as eye movements and lip movements are captured for emotion analysis. Videos are converted into frames and are trained and tested using various baseline deep learning models such as convolutional neural network (CNN), CNN for long short-term memory (LSTM), 3D convolutional neural network (3D CNN), and CNN-LSTM model. We proposed a novel optimizer-based hybrid deep learning model for improving the accuracy of emotion detection—the CNN-LSTM-GWO and CNN-LSTM-PSO. In the final task output, we employ a multitask learning mechanism, setting discrete emotion recognition as the primary task and emotion valence recognition, emotion arousal recognition, and previous information extraction as auxiliary tasks to facilitate practical information sharing across different tasks. Experimental results on the established driver emotion dataset demonstrate that our proposed method significantly improves driver emotion recognition performance, achieving an accuracy of 86.98% and an F1 score of 85.83% in the primary task. This validates the effectiveness of the proposed approach.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1169-1179"},"PeriodicalIF":4.5,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175680","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}

{"title":"Prioritizing App User Reviews Based on Multidimensional Factors: An Integrated BERTopic–SnowNLP Framework for Supporting Developer Decisions","authors":"Jinghua Zhao;Zhu Zhang;Juan Feng;Ao Li","doi":"10.1109/TCSS.2025.3627971","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3627971","url":null,"abstract":"With the rapid development of the internet, the model of app updating and maintenance is shifting from enterprise-driven approaches to collaborative mechanisms powered by real-time user feedback. However, developers face challenges in handling massive, heterogeneous user reviews with dynamic demands. This article aims to address the challenge that existing research lacks a multidimensional evaluation system for prioritizing user reviews—which leads to developers’ difficulty in allocating resources appropriately and responding to critical user needs promptly—and further seeks to provide a quantifiable decision-support tool for targeted app version optimization. This article proposes user review-based prioritization method (URPM), a novel approach that employs multidimensional evaluation to prioritize user reviews for app version optimization. The URPM framework operates in three stages: 1) quantifying user attention by clustering reviews and calculating topic proportions using the BERTopic model; 2) computing review sentiment scores using the SnowNLP tool; and 3) ranking reviews via a priority scoring function that integrates user attention, sentiment, timeliness, and ratings. Experimental results on eight music apps show that URPM achieves strong alignment between its generated high-priority reviews and actual developer update logs, with an <inline-formula><tex-math>$F_{text{hybrid}}$</tex-math></inline-formula> score of 0.625–0.702, NDCG@10 of 0.76–0.86, and NDCG@20 of 0.72–0.86. Comparative experiments on the QQ Music, NetEase Cloud Music, and KuGou Music datasets further demonstrate that URPM outperforms the second-best baseline across all three datasets, improving the <inline-formula><tex-math>$F_{text{hybrid}}$</tex-math></inline-formula> score by 43.0%, 45.3%, and 38.2%, respectively. In addition, cross-domain validation confirms the robustness of URPM, yielding <inline-formula><tex-math>$F_{text{hybrid}}$</tex-math></inline-formula> scores of 0.655–0.717, NDCG@10 of up to 0.86, and NDCG@20 of up to 0.84.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1100-1109"},"PeriodicalIF":4.5,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175841","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}

{"title":"Multiscale Social Network Information Propagation Prediction Model Integrating Graph Attention Networks and Hypergraph Neural Networks","authors":"Jinghua Zhao;Yajie Huang;Liyun Zhao;Xiaohua Zhao;Xiting Lv","doi":"10.1109/TCSS.2025.3620884","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3620884","url":null,"abstract":"Information diffusion prediction is a fundamental yet challenging task in social networks. Existing approaches typically focus on either microscopic or macroscopic prediction, but few effectively integrate both perspectives. Moreover, current models often overlook the dynamic nature of cascades and struggle to capture both diffusion patterns simultaneously. This article addresses this limitation by proposing a novel multiscale information diffusion prediction model that unifies microscopic and macroscopic prediction based on graph attention networks (GATs) and hypergraph neural networks (MS-HGNN). Specifically, MS-HGNN integrates user social features from GATs and global cascade features from HGNN to predict the next affected user. A gated recurrent unit then generates a sequence of predicted users until reaching a virtual terminal user, enabling cascade size estimation. To enhance macroscopic prediction accuracy, we embed the model within a reinforcement learning framework and optimize it using policy gradient methods. Experimental results on four real-world datasets demonstrate that MS-HGNN consistently outperforms state-of-the-art baselines. On average, it achieves approximately 3%–4% improvements in Hits@k and 10%–30% improvements in MAP@k for microscopic prediction across datasets, while also reducing cascade size estimation error in macroscopic prediction.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1110-1119"},"PeriodicalIF":4.5,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175931","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}

{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2025.3632585","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3632585","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 6","pages":"C4-C4"},"PeriodicalIF":4.5,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11272954","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2025.3632503","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3632503","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 6","pages":"C3-C3"},"PeriodicalIF":4.5,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11272955","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Opinion Trend Prediction for Public Health Events Based on Time Series Transformer","authors":"Jinghua Zhao;Xi Shu;Xiaohua Zhao;Jiale Zhao","doi":"10.1109/TCSS.2025.3617491","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3617491","url":null,"abstract":"Accurate prediction of public opinion trends during public health emergencies is crucial for understanding public attitudes and enabling proactive responses. Existing methods frequently exhibit inadequate prediction accuracy and elevated computational complexity in long-term forecasting. The proposed model is an enhanced time series transformer model that incorporates three key innovations. First, a sparse probabilistic attention mechanism reducing spatial complexity from <inline-formula><tex-math>$mathbf{O(L^{2})}$</tex-math></inline-formula> to <inline-formula><tex-math>$mathbf{O(LlnL)}$</tex-math></inline-formula>. Second, a progressive sequence decomposition architecture that explicitly separates trend and seasonal components. Third, a global attention distillation technique to mitigate error accumulation in autoregressive prediction. Experiments on a COVID-19 Weibo dataset containing over 780 000 posts demonstrate that the model accurately predicts trends up to seven times the input sequence length. The model outperforms existing methods by over 20% in terms of mean squared error (MSE) and mean absolute error (MAE). For a prediction length of 720, the model achieves an MSE of 0.457 and an MAE of 0.373, effectively capturing key fluctuation patterns and peak timings. The findings establish a substantial technical basis for public health management early-warning systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1159-1168"},"PeriodicalIF":4.5,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175932","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}

{"title":"MLFormer: Unleashing Efficiency Without Attention for Multimodal Knowledge Graph Embedding","authors":"Meng Wang;Changyu Li;Feiyu Chen;Jie Shao;Ke Qin;Shuang Liang","doi":"10.1109/TCSS.2025.3620089","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3620089","url":null,"abstract":"Multimodal knowledge graphs (MMKGs) have gained widespread adoption across various domains. However, existing transformer-based methods for MMKG representation learning primarily focus on enhancing representation performance, while overlooking time and memory costs, which reduces model efficiency. To tackle these limitations, we introduce a multimodal lightweight transformer (MLFormer) model, which not only ensures robust representation capabilities but also considerably improves computational efficiency. We find that the self-attention mechanism in transformers leads to substantial performance overheads. As a result, we optimize the traditional MMKGE model in two aspects: modality processing and modality fusion, by incorporating a filter gate and Fourier transform. Our experimental results on real-world multimodal knowledge graph completion datasets demonstrate that MLFormer achieves significant improvements in computational efficiency while maintaining competitive performance.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 6","pages":"5536-5549"},"PeriodicalIF":4.5,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652138","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}

{"title":"User Comment Brushing Behavior Identification Algorithm for Malicious Network Behavior Detection","authors":"Jingjing Shi;Zhihua Guo;Yumei Huang","doi":"10.1109/TCSS.2025.3614707","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3614707","url":null,"abstract":"As the behavior of user comment brushing on e-commerce and social platforms becomes increasingly hidden, this article constructs a detection algorithm that integrates dynamic graph neural network (dynamic GNN) and federated learning to detect the blind spots caused by deep learning-generated text and cross-platform collaborative brushing. Dynamic GNN is used to model user-device temporal associations to identify group topological features, and federated learning is used to aggregate multiplatform features to improve cross-platform detection performance while protecting privacy. Based on user comment behavior sequences, such as device ID (identifier), IP (Internet protocol), and timestamp, a dynamic heterogeneous graph (nodes: users/devices; edges: interaction frequency and time series) is constructed, and the topological structure is updated through a sliding window to capture short-term collaborative brushing patterns. A time-aware graph attention mechanism is adopted to aggregate the historical states of neighbor nodes and the current interaction features and output the temporal embedding vector of the user node to characterize its membership in the brushing group. Each platform trains the dynamic GNN model locally, and the central server aggregates cross-platform features such as device fingerprints and IP geographic distribution through federated averaging (FedAvg) to avoid the sharing of raw data. The user temporal embedding is concatenated with the federated features and input into the multilayer perceptron (MLP). The probability of user brushing is output, and the suspicious groups are marked after the threshold is determined. Experimental results show that the dynamic GNN integrated with federated learning has a false alarm rate of 12.1% and an F1-score of 83.1% under an attack density of 50%, demonstrating high cross-platform detection performance. When the time window changes from 30 to 600 s, the mean feature update delay decreases linearly with the increase of the window (38.2→15.9 ms), maintaining a millisecond-level response. The changing trend of the mean training throughput (12 450→29 450 edges/s) directly reflects the elastic expansion capability of the model architecture and has a high dynamic topology capture timeliness. The experimental data verify the effectiveness of this article’s research on the algorithm for identifying user comment brushing behavior.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 1","pages":"1180-1193"},"PeriodicalIF":4.5,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175839","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}