IEEE Transactions on Computational Social Systems最新文献

{"title":"A Hierarchical Mining-Based Approach to Storyline Generation","authors":"Shuang Gu;Juntao Tu;Wengang Zhou;Qiming Fu;Ming Sun;Yajin Ge","doi":"10.1109/TCSS.2025.3648400","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3648400","url":null,"abstract":"Events serve as the fundamental units of human society. With the rapid proliferation of information networks and social media, the dissemination of event-related information has become more pervasive. However, this has also introduced challenges such as information fragmentation, redundancy, and noise, which hinder timely and comprehensive understanding of complex events. Recent research has focused on constructing coherent event narratives by organizing discrete pieces of event information into structured and logically connected developmental chains—a process commonly referred to as storyline generation. Despite notable progress, most existing approaches rely on single-dimensional associations between events and often require extensive multifaceted knowledge, limiting their scalability and effectiveness in handling complex, multifaceted event scenarios. To address this issue, we propose a hierarchical mining-based approach to storyline generation (HMSG). HMSG comprises three core modules. The data preprocessing algorithm is designed to extract and represent textual event features. The coarse-grained clustering algorithm identifies events in the form of story clusters, where events within the same story cluster belong to the same storyline. The fine-grained clustering algorithm divides each story cluster into smaller and semantically cohesive event clusters by capturing stronger local correlations among events. Subsequently, story clusters and event clusters are analyzed to construct the storyline structure, where the main narrative is represented as a trunk and subnarratives as branches. This process culminates in a tree-like representation that effectively illustrates the storyline. To validate the effectiveness of the proposed approach, we conduct extensive experiments on multiple real-world datasets from different domains. Comparative analysis against several state-of-the-art methods for storyline generation demonstrates that HMSG consistently achieves superior performance and robustness across diverse scenarios. The results further confirm that HMSG outperforms baseline approaches in both performance and robustness.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6241-6257"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096986","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":"A Comprehensive Bibliometric Analysis of Insider Threat Scholarship","authors":"Luke J. Osterritter;Kathleen M. Carley","doi":"10.1109/TCSS.2026.3705576","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3705576","url":null,"abstract":"The study of insider threat and insider risk, that of the risk trusted individuals may misuse authorized access to harm their organizations, is a relatively nascent academic field. It is a multidisciplinary problem; one of computer science, information science, and cybersecurity, as well as business, management, organizational theory, psychology, sociology, human behavior, and beyond. Existing reviews have addressed narrow subdomains, such as detection methods, taxonomies, or behavioral modeling, leaving the broader structure of the field, namely its key contributors, institutional networks, topical landscape, and interdisciplinary composition, largely unexamined. This study addresses that gap through a large-scale bibliometric analysis of insider threat and risk scholarship. We assemble a corpus of 3769 publications from 1977 to 2023, drawn from six citation databases and multiple grey literature sources, which we analyze using established bibliometric and network analytical techniques. Our findings reveal a field that has grown steadily since the early 2000s, bolstered by landmark policy events, institutional investments, and high-profile instances of insider threat. The co-authorship network identifies an “invisible college” of scholars, with a large contingent of researchers anchored by universities across nations and several U.S. national laboratories, federally funded research centers, and university-affiliated research centers. Critically, we find a pronounced imbalance in research focus where the vast majority of publications address technical solutions, while a notably smaller amount of work addresses behavioral, organizational, or hybrid approaches—despite broad consensus in the literature that effective insider threat mitigation requires both. Topic network analysis further reveals a well-defined core of research themes, alongside underexplored areas in organizational psychology, positive deterrence, and socio-technical integration. Publication venue analysis reveals a more balanced distribution between conferences and journals than is typical of computer science research, reflecting the field’s interdisciplinary nature. Based on these findings, we identify priority research directions and development of the field. This work provides the first comprehensive bibliometric characterization of insider threat and risk scholarship, offering the research community a foundational look at where the discipline stands and how it should move forward.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5564-5580"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097096","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":"Context-Aligned Latent Enhancement for Multimodal Hate Meme Detection","authors":"Liansong Zong;Qingchi Gui;Jie Wang;Xiaorong Liu;Xiaocao Ouyang;Ji Zhang","doi":"10.1109/TCSS.2026.3715754","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3715754","url":null,"abstract":"Detecting hate memes on social media presents a formidable challenge due to their multimodal and often subtle nature. The hateful intent typically emerges from a nuanced interplay between visual and textual elements, which existing methods often fail to capture by inadequately modeling these cross-modal correlations. To address this gap, we propose semantic context alignment and latent enhancement network (SCALE), a novel framework designed to achieve a deep understanding of hateful content. Our approach operates in two core stages. First, for Semantic Context Alignment, we introduce an attention-enhanced multimodal alignment (AEMA) module. It dynamically aligns image and text features into a unified semantic space, enabling a fine-grained grasp of the visual-textual context. Second, to achieve Latent Enhancement, we employ a lightweight convolutional semantic enhancement network (CSEN). This network refines the aligned and fused representations, distilling high-level, abstract semantics that are crucial for deciphering implicit hateful meanings. Extensive experiments on three benchmark datasets validate the superiority of SCALE. Notably, it achieves a new state-of-the-art area under the receiver operating characteristic curve (AUROC) of 92.95% on the HarMeme dataset, significantly outperforming prior methods and demonstrating its effectiveness in tackling the complexities of hate meme detection.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5944-5955"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097203","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":"Sparing User Time With a Socially Aware Independent Metaverse Avatar","authors":"Theofanis P. Raptis;Chiara Boldrini;Marco Conti;Andrea Passarella","doi":"10.1109/TCSS.2026.3677669","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3677669","url":null,"abstract":"The metaverse is redefining digital interactions by merging physical, virtual, and social dimensions, yet its effects on social networking remain largely unexplored. This work examines the role of <italic>independent avatars</i> (autonomous digital entities capable of managing social interactions on behalf of users), to optimize social time allocation and reshape metaverse-based online social networks. We propose a novel computational model that integrates a quantitative and realistic representation of user social life, grounded in evolutionary anthropology, with a framework for avatar-mediated interactions. Our model quantifies the effectiveness of a partial replacement of in-person interactions with independent avatar interactions. Additionally, it accounts for social conflicts and specific socialization constraints. We leverage our model to explore the benefits and trade-offs of an avatar-augmented social life in the metaverse. Since the exact problem formulation leads to an NP-hard optimization problem when incorporating avatars into the social network, we tackle this challenge by introducing a heuristic solution. Through simulations, we compare avatar-mediated and non-avatar-mediated social networking, demonstrating the potential of independent avatars to enhance social connectivity and efficiency. Our findings provide a foundation for optimizing metaverse-based social interactions, as well as useful insights for future digital social network design.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6132-6144"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11482858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097264","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":"Identifying Information Initiators in Mobile Social Networks: A Community-Aware Multisource Detection Framework","authors":"Chong Zhang;Chao Shen;Chenhao Lin;Luoyi Fu;Qian Wang;Xinbing Wang","doi":"10.1109/TCSS.2025.3628448","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3628448","url":null,"abstract":"The widespread adoption of mobile social networks (MSNs) has profoundly reshaped how people access and disseminate information. However, the same platforms also facilitate the rapid spread of misinformation—such as rumors, fake news, and fraudulent messages—which can lead to severe social, economic, and psychological consequences. Identifying the original initiators of such information cascades, referred to as the multiple source detection (MSD) problem, is crucial for public safety and information governance. Although prior methods have achieved promising results, most rely on specific propagation models, which restrict their applicability in real-world scenarios where the true diffusion mechanisms are often unknown or hard to estimate. Some approaches attempt to circumvent this limitation through label propagation schemes that highlight nodes surrounded by a high proportion of infected neighbors. However, the detection accuracy may be compromised due to the fact that node labels are generally represented as 1-D integers, and all infected or uninfected nodes are initialized uniformly. As a result, the structural features are not sufficiently distinguished. To address this, we propose a community-based label propagation (CLP) framework to identify numerous information sources by utilizing the community structures present in infected networks. Specifically, CLP incorporates two key effects—node prominence and exoneration—to enhance source detection. The <italic>prominence effect</i> suggests that nodes surrounded by a higher proportion of infected neighbors are more likely to be sources. The <italic>exoneration effect</i> relies on uninfected nodes on the boundary or infected nodes in neighboring communities to play a critical role in exonerating an infected node from being the source. Extensive experiments conducted on both synthetic networks and large-scale real-world datasets demonstrate that CLP significantly outperforms existing state-of-the-art methods across a wide range of propagation models, achieving robust, scalable, and accurate multiple source detection.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6625-6641"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097339","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":"Modeling and Control of Sustainable Transitions Through Opinion-Behavior Coupling in Heterogeneous Networks","authors":"Martina Alutto;Sofia Bellotti;Fabrizio Dabbene;Chiara Ravazzi","doi":"10.1109/TCSS.2026.3715278","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3715278","url":null,"abstract":"Understanding how sustainable behaviors spread within heterogeneous societies requires the integration of behavioral data, social influence mechanisms, and structured control strategies. In this article, we propose a data-driven computational framework for coupled opinion-adoption dynamics in social systems. Each node in the multilayer network represents a community characterized by age group and mobility level, derived from large-scale survey data on the propensity to adopt electric vehicles (EVs) in Northern Europe. The proposed model captures three mechanisms: behavioral contagion through social and informational diffusion, abandonment driven by dissatisfaction, and feedback between opinions and adoption levels through social influence. By analyzing the equilibria of the coupled system, we identify conditions that enable widespread adoption. The model is empirically calibrated using survey data to generate synthetic populations and social similarity networks, which are then used to evaluate targeted interventions promoting sustainable transitions. We focus on two control strategies: 1) opinion-based interventions acting on the social network layer; and 2) experience- or dissatisfaction-based policies aimed at reducing abandonment. Simulation results indicate that policies targeting dissatisfaction are more effective in ensuring stable and long-term adoption, providing actionable insights for designing interventions in sociotechnical transitions toward sustainability.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5620-5633"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11644402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097340","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":"Blockchain-Based Decentralized Production Progress Early Warning in Social Manufacturing Paradigm","authors":"Kangqian Zheng;Shaowei Zhi;Kai Ding;Qingdi Liu;Fuqiang Zhang;Jizhuang Hui;Felix T. S. Chan","doi":"10.1109/TCSS.2025.3642911","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3642911","url":null,"abstract":"Social manufacturing (SM) is a task-oriented production paradigm that enables coordinated scheduling across distributed manufacturing resources. However, its operational efficiency is often limited by reliance on centralized management systems. In this context, blockchain technology shows strong potential for decentralized management in SM. However; the corresponding models for early warnings of production progress, especially for task fulfillment and delay risks, remain largely unexplored. To address the limitations, this article first proposes a blockchain-based decentralized production progress early warning system in SM. Furthermore, a corresponding early warning workflow is developed to define the overall interaction logic among participants. Then, a hybrid state machine is implemented within the smart contract to construct a task fulfillment status early warning model. Afterward, a Bayesian network-based model is designed to assess the evolving delay risks of interdependent tasks. Finally, a case study on air-conditioner production is conducted to validate the proposed models. Results show that the prototype system maintains scalable performance, achieving up to 17.56 transactions/s under write-intensive workloads and 52.45 transactions/s under read-intensive workloads, demonstrating more stable scalability than traditional centralized and directed acyclic graph (DAG)-based systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6342-6358"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097412","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":"A Bilevel Consensus Model Based on Network Game for Maximizing Satisfaction and Minimizing Cost","authors":"Shengli Li;Yuhua Qian;Cuiping Wei;Peide Liu;Xihong Yan","doi":"10.1109/TCSS.2026.3704106","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3704106","url":null,"abstract":"During the consensus reaching process (CRP), decision makers (DMs) typically achieve the desired consensus through discussion and persuasion. In this process, the moderator invests time and resources to persuade the DMs to change their opinions. Consequently, various interaction behaviors occur during the CRP, encompassing interactions between the moderator and DMs, as well as interactions among DMs. This article aims to develop a minimum cost-based consensus model by considering these interaction behaviors during the CRP. We utilize a network game to model the interplay among the behaviors of DMs and employ the Stackelberg game architecture to design an interactive mechanism between the DMs and the moderator. In the proposed model, the moderator provides compensation strategies and feedback suggestions to guide the DMs toward achieving the desired consensus level with minimal consensus cost. Meanwhile, the DMs adjust their opinions with the aim of maximizing their satisfaction. We then present an equilibrium analysis for the proposed model. Finally, we conduct an example application to illustrate and justify the performance of the proposed model.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6062-6076"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097608","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":"Competing for Attention: A Stochastic Analysis of Influencer Popularity Dynamics","authors":"Franco Galante;Chiara Ravazzi;Luca Vassio;Michele Garetto;Emilio Leonardi","doi":"10.1109/TCSS.2026.3715706","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3715706","url":null,"abstract":"We present a stochastic approach to describe the popularity dynamics of competing users seeking public attention. Using a data-driven methodology, we identify and analyze the key factors that govern these dynamics, and propose a new analytical model that integrates multiple determinants of user success, including individual activity, experience in producing viral content, exogenous events, and the platform’s role in enhancing visibility. We derive sufficient conditions to ensure the ergodicity of the system, enabling a probabilistic analysis of influencer popularity. Our sensitivity analysis explores different system configurations and identifies conditions under which meritocracy (based on content quality and expertise) may prevail over the concentration of influence among a few dominant users, offering insights into how social networks may evolve toward more equitable or biased arenas of influence.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5932-5943"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097857","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":"MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation","authors":"Xiaoxian Yang;Qi Wang;Kaiqi Zhang;Ke Wei;Jun Lyu;Lingchao Chen","doi":"10.1109/TCSS.2025.3562441","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3562441","url":null,"abstract":"Echocardiographic image segmentation plays a crucial role in analyzing cardiac function and diagnosing cardiovascular diseases. Ultrasound imaging frequently encounters challenges, such as those related to elevated noise levels, diminished spatiotemporal resolution, and the complexity of anatomical structures. These factors significantly hinder the model’s ability to accurately capture and analyze structural relationships and dynamic patterns across various regions of the heart. Mamba, an emerging model, is one of the most cutting-edge approaches that is widely applied to diverse vision and language tasks. It efficiently captures global information with linear complexity and compensates for the shortcomings of convolutional neural networks (CNNs) and conventional transformers. To this end, this article introduces a U-shaped deep learning model incorporating a large-window Mamba scale (LMS) module and a hierarchical feature fusion approach for echocardiographic segmentation. First, a cascaded residual block serves as an encoder and is employed to incrementally extract multiscale detailed features. It addresses the vanishing gradient issue by leveraging a residual structure that ensures stable and rapid convergence throughout the training process. Second, a large-window multiscale mamba module is integrated into the decoder to capture global dependencies across regions and enhance the segmentation capability for complex anatomical structures. Furthermore, our model introduces auxiliary losses at each decoder layer and employs a dual attention mechanism to fuse multilayer features both spatially and across channels. This approach enhances segmentation performance and accuracy in delineating complex anatomical structures. Finally, the experimental results using the EchoNet-Dynamic and CAMUS datasets demonstrate that the model outperforms other methods in terms of both accuracy and robustness. For the segmentation of the left ventricular endocardium (<inline-formula><tex-math>$text{LV}_{text{endo}}$</tex-math></inline-formula>), the model achieved optimal values of 95.01 and 93.36, respectively, while for the left ventricular epicardium (<inline-formula><tex-math>$text{LV}_{text{epi}}$</tex-math></inline-formula>), values of 87.35 and 87.80, respectively, were achieved. This represents an improvement ranging between 0.54 and 1.11 compared with the best-performing model.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 4","pages":"5387-5399"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661505","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}