Pub Date : 2024-12-11DOI: 10.1109/TCSS.2024.3508733
Jothi Prakash V;Arul Antran Vijay S
In the dynamic landscape of social media, the strategic use of hashtags has emerged as a crucial tool for enhancing content discoverability and engagement. This research introduces the neurosymbolic contrastive framework (NSCF), an innovative methodology designed to address the multifaceted challenges inherent in automated hashtag recommendation, such as the integration of multimodal data, the context sensitivity of content, and the dynamic nature of social media trends. By combining deep learning's representational strengths with the deductive prowess of symbolic artificial Intelligence (AI), NSCF crafts contextually relevant and logically coherent hashtag suggestions. Its dual-stream architecture meticulously processes and aligns textual and visual content through contrastive learning, ensuring a comprehensive understanding of multimodal social media data. The framework's neurosymbolic integration leverages structured knowledge and logical inference, significantly enhancing the relevance and coherence of its recommendations. Evaluated against a variety of datasets, including MM-INS, NUS-WIDE, and HARRISON, NSCF has demonstrated exceptional performance, outshining existing models and baseline methods across key metrics such as precision (0.721–0.701), recall (0.736–0.716), and F1 score (0.728–0.708). This research represents a major advancement in social media analytics as it not only demonstrates NSCF's novel approach but also sheds light on its potential to transform hashtag recommendation systems.
{"title":"A Comprehensive Multimodal Framework for Optimizing Social Media Hashtag Recommendations","authors":"Jothi Prakash V;Arul Antran Vijay S","doi":"10.1109/TCSS.2024.3508733","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3508733","url":null,"abstract":"In the dynamic landscape of social media, the strategic use of hashtags has emerged as a crucial tool for enhancing content discoverability and engagement. This research introduces the neurosymbolic contrastive framework (NSCF), an innovative methodology designed to address the multifaceted challenges inherent in automated hashtag recommendation, such as the integration of multimodal data, the context sensitivity of content, and the dynamic nature of social media trends. By combining deep learning's representational strengths with the deductive prowess of symbolic artificial Intelligence (AI), NSCF crafts contextually relevant and logically coherent hashtag suggestions. Its dual-stream architecture meticulously processes and aligns textual and visual content through contrastive learning, ensuring a comprehensive understanding of multimodal social media data. The framework's neurosymbolic integration leverages structured knowledge and logical inference, significantly enhancing the relevance and coherence of its recommendations. Evaluated against a variety of datasets, including MM-INS, NUS-WIDE, and HARRISON, NSCF has demonstrated exceptional performance, outshining existing models and baseline methods across key metrics such as precision (0.721–0.701), recall (0.736–0.716), and F1 score (0.728–0.708). This research represents a major advancement in social media analytics as it not only demonstrates NSCF's novel approach but also sheds light on its potential to transform hashtag recommendation systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"2144-2155"},"PeriodicalIF":4.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315391","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}
Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communities formed in a rumor propagation process to improve rumor detection accuracy. CDGCN uses a designed method that combines node features and topology features to identify the communities and learn the community features of rumors. Following this, a graph convolutional network (GCN) with a community-aware attention mechanism is proposed to enable the nodes to dynamically aggregate information from their neighboring nodes’ global and community features, effectively prioritizing critical neighborhood information, enhancing the representation of both local community structures and global network patterns for improved analytical performance. The final rumor representations generated by the GCN are processed by a classifier to detect false rumors. Comprehensive experiments and comparison studies are conducted on four real-world datasets to validate the effectiveness of CDGCN.
{"title":"Community-Enhanced Dynamic Graph Convolutional Networks for Rumor Detection on Social Networks","authors":"Wei Zhou;Chenzhan Wang;Fengji Luo;Yu Wang;Min Gao;Junhao Wen","doi":"10.1109/TCSS.2024.3505892","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3505892","url":null,"abstract":"Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communities formed in a rumor propagation process to improve rumor detection accuracy. CDGCN uses a designed method that combines node features and topology features to identify the communities and learn the community features of rumors. Following this, a graph convolutional network (GCN) with a community-aware attention mechanism is proposed to enable the nodes to dynamically aggregate information from their neighboring nodes’ global and community features, effectively prioritizing critical neighborhood information, enhancing the representation of both local community structures and global network patterns for improved analytical performance. The final rumor representations generated by the GCN are processed by a classifier to detect false rumors. Comprehensive experiments and comparison studies are conducted on four real-world datasets to validate the effectiveness of CDGCN.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"818-831"},"PeriodicalIF":4.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769473","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 : 2024-12-11DOI: 10.1109/TCSS.2024.3504398
Keyi Chen;Tianxing Wang;Haibin Zhu;Bing Huang
Role-based collaboration (RBC) is a role-centered computational approach designed to solve collaboration problems. Group role assignment is an essential and extensive part of this research. Based on group multirole assignment (GMRA), this article addresses some issues in the current research. First, managers often hope to obtain the highest benefits rather than maximizing the team performance, which is emphasized in the traditional RBC research. This article introduces the use of expected utility theory to assign roles in order to maximize team effectiveness. Second, the existing studies need to provide expressions of agent and role conflicts, which have yet to be reasonably addressed. This article classifies conflicts by employing agent and role capability combined with the three-way conflict analysis theory. Based on these, this article puts forward the utility-based GMRA with conflicting agent and role problems. The validity is verified through several experiments and comparative analysis, which provides more possibilities for future research.
{"title":"Maximizing Group Utilities While Avoiding Conflicts Through Agent Qualifications","authors":"Keyi Chen;Tianxing Wang;Haibin Zhu;Bing Huang","doi":"10.1109/TCSS.2024.3504398","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3504398","url":null,"abstract":"Role-based collaboration (RBC) is a role-centered computational approach designed to solve collaboration problems. Group role assignment is an essential and extensive part of this research. Based on group multirole assignment (GMRA), this article addresses some issues in the current research. First, managers often hope to obtain the highest benefits rather than maximizing the team performance, which is emphasized in the traditional RBC research. This article introduces the use of expected utility theory to assign roles in order to maximize team effectiveness. Second, the existing studies need to provide expressions of agent and role conflicts, which have yet to be reasonably addressed. This article classifies conflicts by employing agent and role capability combined with the three-way conflict analysis theory. Based on these, this article puts forward the utility-based GMRA with conflicting agent and role problems. The validity is verified through several experiments and comparative analysis, which provides more possibilities for future research.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"552-562"},"PeriodicalIF":4.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783262","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 : 2024-12-11DOI: 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}
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