IEEE Transactions on Computational Social Systems最新文献

{"title":"Metacracy: A New Governance Paradigm Beyond Bounded Intelligence","authors":"Fei-Yue Wang;Rui Qin;Juanjuan Li;Levente Kováacs;Bin Hu","doi":"10.1109/TCSS.2024.3493372","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3493372","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7072-7085"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789001","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":"PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation","authors":"Jinpeng Hu;Tengteng Dong;Gang Luo;Hui Ma;Peng Zou;Xiao Sun;Dan Guo;Xun Yang;Meng Wang","doi":"10.1109/TCSS.2024.3497725","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3497725","url":null,"abstract":"Mental health has attracted substantial attention in recent years and large language model (LLM) can be an effective technology for alleviating this problem owing to its capability in text understanding and dialogue. However, existing research in this domain often suffers from limitations, such as training on datasets lacking crucial prior knowledge and evidence, and the absence of comprehensive evaluation methods. In this article, we propose a specialized psychological LLM, named PsycoLLM, trained on a proposed high-quality psychological dataset, including single-turn QA, multiturn dialogues, and knowledge-based QA. Specifically, we construct multi-turn dialogues through a three-step pipeline comprising multiturn QA generation, evidence judgment, and dialogue refinement. We augment this process with real-world psychological case backgrounds extracted from online platforms, enhancing the relevance and applicability of the generated data. Additionally, to compare the performance of PsycoLLM with other LLMs, we develop a comprehensive psychological benchmark based on authoritative psychological counseling examinations in China, which includes assessments of professional ethics, theoretical proficiency, and case analysis. The experimental results on the benchmark illustrate the effectiveness of PsycoLLM, which demonstrates superior performance compared with other LLMs.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"539-551"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783378","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":"Robust Cyber Threat Intelligence Sharing Using Federated Learning for Smart Grids","authors":"Saifur Rahman;Shantanu Pal;Zahra Jadidi;Chandan Karmakar","doi":"10.1109/TCSS.2024.3496746","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3496746","url":null,"abstract":"Given the escalating diversity, sophistication, and frequency of cyber attacks, it is imperative for critical infrastructure entities, e.g. smart grids, to recognize the inherent risks of operating in isolation. Sharing cyber threat intelligence (CTI) helps them stand together and build a collective cyber defense by knowledge, skills, and experience encompassing information related to identifying and evaluating cyber and physical threats. The present studies lack on robust CTI sharing strategies in smart grid systems. To address the critical need for secure and effective CTI sharing in smart grid systems, this article proposes a novel approach. Our solution leverages encrypted federated learning (FL) with integrated malicious client detection mechanisms. This approach facilitates collaborative learning of a threat detection model while preserving the privacy of raw CTI data. Employing real-world, heterogeneous smart grid datasets, we rigorously evaluated our approach under two distinct attack scenarios. The results demonstrate resilience against both man-in-the-middle attacks and malicious clients, exceeding the performance typically observed in traditional FL models.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"635-644"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783380","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":"Online Social Behaviors: Robust and Stable Features for Detecting Microblog Bots","authors":"Xuan Zhang;Tingshao Zhu;Baobin Li","doi":"10.1109/TCSS.2024.3502357","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3502357","url":null,"abstract":"Bot accounts on microblogging platforms significantly impact information reliability and cyberspace security. Accurately identifying these bots is essential for effective community governance and opinion management. This article introduces a category of online social behavior features (OSBF), derived from microblog behaviors such as emotional expression, language organization, and self-description. Through a series of experiments, OSBF has demonstrated the stable and robust performance in characterizing and detecting microblog bots on Twitter and Chinese Weibo. By identifying significant differences in OSBF between bot and human accounts, we established an OSBF-based detection model. This model showed excellent performance across multitask and multiscale challenges in two English Twitter datasets. Additionally, we explored cross-language and cross-dataset applications using two Chinese Weibo datasets, further affirming the model's effectiveness and robustness. The experimental results confirm that our OSBF-based model surpasses existing methods in detecting microblog bots.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"671-681"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783379","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 Publication Information","authors":"","doi":"10.1109/TCSS.2024.3493355","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3493355","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"C2-C2"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789002","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":"Harnessing Generative Large Language Models for Dynamic Intention Understanding in Recommender Systems: Insights From a Client–Designer Interaction Case Study","authors":"Zhongsheng Qian;Hui Zhu;Jinping Liu;Zilong Wan","doi":"10.1109/TCSS.2024.3494265","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3494265","url":null,"abstract":"Generative large language models (GLLMs) have achieved extreme success in the academic community of recommender systems. However, the application of such a powerful tool in the industrial world is still nascent. In Chinese home renovation industry, advisory consultants engage in offline conversations to fully understand the intentions of potential clients before subsequently recommending designers to them. Although conventional recommender systems can somewhat substitute for the consultants, they fall short in addressing two significant challenges. First, clients frequently revise their intentions during conversations, complicating the accurate capture of key intentions. Second, the process of recommending designers, which relies heavily on consultants’ manual efforts, is not only time-consuming but also prone to inaccuracies. To address the challenges, we present a recommendation agent, named DCICDRec, which leverages the robust conversational understanding and generation capabilities of the large language model MOSS. The creation of this agent involves two key steps. The first step is to prepare the corpus from the renovation domain by organizing it into conversational graphs, to which balanced sampling and profile normalization mechanisms are applied. This preparation ensures that the corpus is well-structured and unbiased before proceeding to fine-tune MOSS. The second step is to utilize the fine-tuned MOSS as a recommendation agent. In this capacity, the agent engages in conversations with potential clients and recommends designers, providing detailed reasons for each recommendation. Furthermore, if the client is dissatisfied with the recommended designers, the agent will delve deeper into understanding the client's true intentions and continually update the recommendations until the client is satisfied. We evaluate the agent's effectiveness on a real dialog dataset CRM between clients and consultants, as well as two publicly available datasets, INSPIRED and ReDIAL. Through comprehensive experiments with six baseline models, the DCICDRec agent demonstrate superior performances on the three datasets. Such experimental achievements indicate that the DCICDRec agent holds significant potential for generalization and commercial value. Moreover, the results of case study with 11 offline tests illustrate the scalability and efficiency of the agent in real-time scenarios.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"807-817"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769480","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":"Guest Editorial: Special Issue on Social Manufacturing After ChatGPT","authors":"Fei-Yue Wang;Pingyu Jiang;Gang Xiong;MengChu Zhou;Bernd Kuhlenkötter;Petri Helo;Zhen Shen","doi":"10.1109/TCSS.2024.3496032","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3496032","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7892-7897"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777626","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":"Counterfactual Music Recommendation for Mitigating Popularity Bias","authors":"Jidong Yuan;Bingyu Gao;Xiaokang Wang;Haiyang Liu;Lingyin Zhang","doi":"10.1109/TCSS.2024.3491800","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3491800","url":null,"abstract":"Music recommendation systems aim to suggest tracks that users may enjoy. However, the accuracy of recommendation results is affected by popularity bias. Previous studies have focused on mitigating the direct effect of single-item popularity in video, news, or e-commerce recommendations, but have overlooked the multisource popularity biases in music recommendations. This article proposes a causal inference-based method to reduce the influence of both track and artist popularity. First, we construct a causal graph that encompasses users, tracks, and artists within the context of music recommendations. Next, we employ matrix factorization in conjunction with counterfactual inference theory to mitigate the popularity effects of artists and tracks, taking into account both the natural direct and indirect effects of these entities on music recommendations. Experimental results evaluated on four music recommendation datasets indicate that our method outperforms other baselines and effectively alleviates the popularity bias of both tracks and artists.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"851-861"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769424","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":"Privacy-Preserving Multilayer Community Detection via Federated Learning","authors":"Shi-Yao Ma;Xiao-Ke Xu;Jing Xiao","doi":"10.1109/TCSS.2024.3493967","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3493967","url":null,"abstract":"Existing frameworks of privacy-preserving multilayer community detection have room for improving detection performance and reducing communication overhead. To address these issues, we propose a novel privacy-preserving multilayer community detection framework based on federated learning which is called federated multilayer community detection (FMCD). First, we propose a novel aggregation strategy by utilizing the network average degree of local networks to aggregate the parameters uploaded by clients in the step of aggregation, which can improve the performance of community detection. Second, we design a training procedure to complete multilayer community detection in multiorganizations, which can reduce communication overhead by transmitting merged community information instead of the global parameter. Finally, experiment results on synthetic and real networks with different criteria illustrate that FMCD can achieve significant performance gains, compared with state-of-the-art algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"832-846"},"PeriodicalIF":4.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769494","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":"Generalized Defensive Modeling of Fake News Propagation in Social Networks Using Fractional Differential Equations","authors":"Alfredo De Santis;Eslam Farsimadan;Leila Moradi;Francesco Palmieri","doi":"10.1109/TCSS.2024.3492097","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3492097","url":null,"abstract":"The rapid progress of Internet technology has led to a strong increase in the use of online social networks for disseminating information on the Internet. In this scenario, it is crucial to establish approaches that can effectively reduce the diffusion of false information (fake news) that can potentially cause harm to society. A defensive approach, based on integer-order differential equations, has been recently developed to analyze the effects of verification and blocking of users for containing the spread of fake news. Starting from it, we introduce a novel fractional model providing a more accurate, powerful, and realistic representation of the transmission of fake news messages. The model aims to predict the spread of such messages, by better considering the effect of the system's status evolution over time. The use of fractional differential equations to schematize the propagation of fake news results in incorporating a greater amount of memory information and better considering hereditary properties of the system of interest, also capturing its hidden nonlinear dynamics, mainly related to fractality and multiscale nature.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"622-634"},"PeriodicalIF":4.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783298","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}