Pub Date : 2024-12-11DOI: 10.1109/TCSS.2024.3508744
Jiawei Shen;Junfeng Tian;Ziyuan Wang;Qi Zhu
Presently, the majority of social networking platforms tend to outsource the analysis of social relationship data to third-party companies. Existing methods, which generally aim to protect social relationships by erasing friendship links or introducing uniform noise into datasets, do not take into account the risk of inference attacks or the actual privacy needs of users. To address these concerns, we present a novel method, named exponential mechanism of personalized difference privacy (EPDP), for preserving the privacy of social relationships, based on the EPDP. We develop specific social relationship indices to group friendship links and divided these links into distinct privacy levels, each with a unique privacy budget. Then, we select representative elements from each group using sampling and the exponential mechanism to generalize the original datasets, ensuring compliance with personalized difference privacy principles. Metrics for privacy and utility assessment are devised to evaluate method performance. Experimental results reveal that EPDP offers superior utility compared to uniform differential privacy (UDP) and provides better privacy protection than the state-of-the-art. Moreover, we explore the impact of various parameters on data utility. This article marks the pioneering effort to introduce a privacy-preserving method based on the exponential mechanism for the safeguarding of social relationships.
{"title":"Preserving Social Relationship Privacy via the Exponential Mechanism of Personalized Differential Privacy","authors":"Jiawei Shen;Junfeng Tian;Ziyuan Wang;Qi Zhu","doi":"10.1109/TCSS.2024.3508744","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3508744","url":null,"abstract":"Presently, the majority of social networking platforms tend to outsource the analysis of social relationship data to third-party companies. Existing methods, which generally aim to protect social relationships by erasing friendship links or introducing uniform noise into datasets, do not take into account the risk of inference attacks or the actual privacy needs of users. To address these concerns, we present a novel method, named exponential mechanism of personalized difference privacy (EPDP), for preserving the privacy of social relationships, based on the EPDP. We develop specific social relationship indices to group friendship links and divided these links into distinct privacy levels, each with a unique privacy budget. Then, we select representative elements from each group using sampling and the exponential mechanism to generalize the original datasets, ensuring compliance with personalized difference privacy principles. Metrics for privacy and utility assessment are devised to evaluate method performance. Experimental results reveal that EPDP offers superior utility compared to uniform differential privacy (UDP) and provides better privacy protection than the state-of-the-art. Moreover, we explore the impact of various parameters on data utility. This article marks the pioneering effort to introduce a privacy-preserving method based on the exponential mechanism for the safeguarding of social relationships.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1164-1180"},"PeriodicalIF":4.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178885","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}
The computational analysis of big data has revolutionized social science research, offering unprecedented insights into societal behaviors and trends through digital data from online sources. However, existing tools often face limitations such as technical complexity, single-source dependency, and a narrow range of analytical capabilities, hindering accessibility and effectiveness. This article introduces DataPoll, an end-to-end big data analysis platform designed to democratize computational social science research. DataPoll simplifies data collection, analysis, and visualization, making advanced analytics accessible to researchers of diverse expertise. It supports multisource data integration, innovative analytical features, and interactive dashboards for exploratory and comparative analyses. By fostering collaboration and enabling the integration of new data sources and analysis methods, DataPoll represents a significant advancement in the field. A comprehensive case study on the Ukrainian–Russian conflict demonstrates its capabilities, showcasing how DataPoll can yield actionable insights into complex social phenomena. This tool empowers researchers to harness the potential of big data for impactful and inclusive research.
{"title":"DataPoll: A Tool Facilitating Big Data Research in Social Sciences","authors":"Antonis Charalampous;Constantinos Djouvas;Christos Christodoulou","doi":"10.1109/TCSS.2024.3506582","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3506582","url":null,"abstract":"The computational analysis of big data has revolutionized social science research, offering unprecedented insights into societal behaviors and trends through digital data from online sources. However, existing tools often face limitations such as technical complexity, single-source dependency, and a narrow range of analytical capabilities, hindering accessibility and effectiveness. This article introduces DataPoll, an end-to-end big data analysis platform designed to democratize computational social science research. DataPoll simplifies data collection, analysis, and visualization, making advanced analytics accessible to researchers of diverse expertise. It supports multisource data integration, innovative analytical features, and interactive dashboards for exploratory and comparative analyses. By fostering collaboration and enabling the integration of new data sources and analysis methods, DataPoll represents a significant advancement in the field. A comprehensive case study on the Ukrainian–Russian conflict demonstrates its capabilities, showcasing how DataPoll can yield actionable insights into complex social phenomena. This tool empowers researchers to harness the potential of big data for impactful and inclusive research.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"511-524"},"PeriodicalIF":4.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783365","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}
Pub Date : 2024-12-05DOI: 10.1109/TCSS.2024.3512113
{"title":"2024 Index IEEE Transactions on Computational Social Systems Vol. 11","authors":"","doi":"10.1109/TCSS.2024.3512113","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3512113","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"1-124"},"PeriodicalIF":4.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789046","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}
Pub Date : 2024-12-05DOI: 10.1109/TCSS.2024.3507733
Chuwei Liu;Jianping Huang;Siyu Chen;Jiaqi He;Shikang Du;Nan Yin;Chao Zhang;Danfeng Wang
Infectious diseases are posing an increasingly serious threat to human society. It is urgent to make a rapid estimate of the scale of outbreaks when the disease information is still unclear in the early stages of the outbreak, so as to buy time for a timely response to infectious diseases and provide reference for the allocation of medical resources and the formulation of control measures. Based on this, this study took the concentrated outbreak of COVID-19 in various cities in China as an example, collected 22 meteorological, social-ecological and population mobility indicators, and established a random forest-kernel density estimation-quantile stepwise regression (RF-KDE-QSR) model to make a preliminary estimate of the daily outbreak scale in cities. The RF model was used for preliminary estimation, and the KDE-QSR model was used for residual correction to correct the prediction results. The evaluation of the prediction accuracy proved the effectiveness of the prediction model. When the RF model was used alone, the R-squared (R2) was 0.82 and the corrected R2 was 0.90. The KDE-QSR model effectively improved the prediction accuracy of the model.
{"title":"RF-KDE-QSR Model for Estimating the Scale of Epidemics","authors":"Chuwei Liu;Jianping Huang;Siyu Chen;Jiaqi He;Shikang Du;Nan Yin;Chao Zhang;Danfeng Wang","doi":"10.1109/TCSS.2024.3507733","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3507733","url":null,"abstract":"Infectious diseases are posing an increasingly serious threat to human society. It is urgent to make a rapid estimate of the scale of outbreaks when the disease information is still unclear in the early stages of the outbreak, so as to buy time for a timely response to infectious diseases and provide reference for the allocation of medical resources and the formulation of control measures. Based on this, this study took the concentrated outbreak of COVID-19 in various cities in China as an example, collected 22 meteorological, social-ecological and population mobility indicators, and established a random forest-kernel density estimation-quantile stepwise regression (RF-KDE-QSR) model to make a preliminary estimate of the daily outbreak scale in cities. The RF model was used for preliminary estimation, and the KDE-QSR model was used for residual correction to correct the prediction results. The evaluation of the prediction accuracy proved the effectiveness of the prediction model. When the RF model was used alone, the R-squared (R<sup>2</sup>) was 0.82 and the corrected R<sup>2</sup> was 0.90. The KDE-QSR model effectively improved the prediction accuracy of the model.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1193-1201"},"PeriodicalIF":4.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178880","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}
Pub Date : 2024-12-02DOI: 10.1109/TCSS.2024.3493357
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2024.3493357","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3493357","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"C3-C3"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772356","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789031","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}
Pub Date : 2024-12-02DOI: 10.1109/TCSS.2024.3493359
{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2024.3493359","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3493359","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"C4-C4"},"PeriodicalIF":4.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789069","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}
Pub Date : 2024-12-02DOI: 10.1109/TCSS.2024.3493372
Fei-Yue Wang;Rui Qin;Juanjuan Li;Levente Kováacs;Bin Hu
{"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}
Pub Date : 2024-12-02DOI: 10.1109/TCSS.2024.3497725
Jinpeng Hu;Tengteng Dong;Gang Luo;Hui Ma;Peng Zou;Xiao Sun;Dan Guo;Xun Yang;Meng Wang
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.
{"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}
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.
{"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}
Pub Date : 2024-12-02DOI: 10.1109/TCSS.2024.3502357
Xuan Zhang;Tingshao Zhu;Baobin Li
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.
{"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}
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