Pub Date : 2024-06-13DOI: 10.1109/TCSS.2024.3397406
Tu Nguyen;Vincenzo Piuri;Joel Rodrigues;Lianyong Qi;Shahid Mumtaz;Warren Huang-Chen Lee
{"title":"Guest Editorial: Special Issue on Knowledge-Infused Learning for Computational Social Systems","authors":"Tu Nguyen;Vincenzo Piuri;Joel Rodrigues;Lianyong Qi;Shahid Mumtaz;Warren Huang-Chen Lee","doi":"10.1109/TCSS.2024.3397406","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397406","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319709","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 : 2024-06-13DOI: 10.1109/TCSS.2024.3397415
{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2024.3397415","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397415","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319647","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 : 2024-06-13DOI: 10.1109/TCSS.2024.3397411
{"title":"IEEE Transactions on Computational Social Systems Publication Information","authors":"","doi":"10.1109/TCSS.2024.3397411","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397411","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319642","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 : 2024-06-05DOI: 10.1109/TCSS.2024.3398815
Baoyu Zhang;Tao Chen;Xiao Wang;Qiang Li;Weishan Zhang;Fei-Yue Wang
Based on an investigation of online public opinion on the Nahel Merzouk protests in France, an approach for analyzing and predicting public opinion on protests based on large language model (LLM) is proposed, revealing the impact of emerging social media on the protests. We demonstrate that protests generate public opinion on social media with some lag, but that comment sentiment and expression are consistent with protest trends. As the protests unfolded, we analyzed the evolution of public sentiment. We constructed the prompt based on historical data to predict the protests using the p-tuning and Lora approach to fine-tune LLM. In addition, we discuss how to use blockchain technology to optimize distributed, self-organizing protests and reduce the potential for disinformation and violent conflict.
{"title":"Decoding Activist Public Opinion in Decentralized Self-Organized Protests Using LLM","authors":"Baoyu Zhang;Tao Chen;Xiao Wang;Qiang Li;Weishan Zhang;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3398815","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3398815","url":null,"abstract":"Based on an investigation of online public opinion on the Nahel Merzouk protests in France, an approach for analyzing and predicting public opinion on protests based on large language model (LLM) is proposed, revealing the impact of emerging social media on the protests. We demonstrate that protests generate public opinion on social media with some lag, but that comment sentiment and expression are consistent with protest trends. As the protests unfolded, we analyzed the evolution of public sentiment. We constructed the prompt based on historical data to predict the protests using the p-tuning and Lora approach to fine-tune LLM. In addition, we discuss how to use blockchain technology to optimize distributed, self-organizing protests and reduce the potential for disinformation and violent conflict.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993943","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 : 2024-04-30DOI: 10.1109/TCSS.2024.3381840
Pengfei Zhang;Kun Zhao;Hong Liu;Wenhao Li
In crowd evacuation research, the knowledge contained in crowd evacuation is very complex and is multisource. Crowd evacuation scenarios restrict pedestrians’ movement decision-making, and the movement states of the crowd imply the movement characteristics. However, the existing studies on crowd evacuation navigation approach cannot make full use of the complex and multisource crowd evacuation knowledge, which reduces the effect of the evacuation navigation. To solve this problem, a new crowd evacuation navigation approach based on multisource knowledge is proposed. First, we collect relevant data on crowd evacuation using an image sensor network and establish a crowd evacuation knowledge graph to organize and store this data. Second, the explicit knowledge of scene structure and crowd movements is represented based on the crowd evacuation knowledge graph. Then, a deep-learning-based tacit knowledge model (DLTKM) is designed to extract the tacit knowledge of different groups and scene entities. Finally, a new crowd evacuation navigation approach based on wireless sensor network and related knowledge representations is designed to plan evacuation paths for evacuees. The experiment results show that this approach can plan reasonable evacuation paths for pedestrians, and improve the efficiency of crowd evacuations.
{"title":"Multisource-Knowledge-Based Approach for Crowd Evacuation Navigation","authors":"Pengfei Zhang;Kun Zhao;Hong Liu;Wenhao Li","doi":"10.1109/TCSS.2024.3381840","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3381840","url":null,"abstract":"In crowd evacuation research, the knowledge contained in crowd evacuation is very complex and is multisource. Crowd evacuation scenarios restrict pedestrians’ movement decision-making, and the movement states of the crowd imply the movement characteristics. However, the existing studies on crowd evacuation navigation approach cannot make full use of the complex and multisource crowd evacuation knowledge, which reduces the effect of the evacuation navigation. To solve this problem, a new crowd evacuation navigation approach based on multisource knowledge is proposed. First, we collect relevant data on crowd evacuation using an image sensor network and establish a crowd evacuation knowledge graph to organize and store this data. Second, the explicit knowledge of scene structure and crowd movements is represented based on the crowd evacuation knowledge graph. Then, a deep-learning-based tacit knowledge model (DLTKM) is designed to extract the tacit knowledge of different groups and scene entities. Finally, a new crowd evacuation navigation approach based on wireless sensor network and related knowledge representations is designed to plan evacuation paths for evacuees. The experiment results show that this approach can plan reasonable evacuation paths for pedestrians, and improve the efficiency of crowd evacuations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319563","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 : 2024-04-03DOI: 10.1109/TCSS.2024.3362811
Gitanjali Kumari;Dibyanayan Bandyopadhyay;Asif Ekbal;Santanu Pal;Arindam Chatterjee;Vinutha B. N.
Recognizing humor in meme data is a challenging task in natural language processing (NLP) and computer vision (CV) due to the complexity and variability of humor. With the explosive growth of Internet memes on social media platforms such as Facebook, Twitter, and Instagram, this task has become more important. However, there have been few studies that investigate humor recognition from memes, particularly in languages other than English. In this work, we hypothesize that humor is closely related to the valence and arousal dimensions of sentiment. We make the first attempt to release a new meme dataset for humor recognition in Hindi and propose a multitask deep learning framework to simultaneously solve three problems: humor recognition (the primary task) and valence and arousal classification (the two secondary tasks) for Internet memes. Empirical results on the Hindi meme dataset demonstrate the efficacy of our multitask learning approach over traditional pretrained models such as BERT and VGG19. The complete resources and codes will be made available for further research after acceptance of the manuscript.
{"title":"Let's All Laugh Together: A Novel Multitask Framework for Humor Detection in Internet Memes","authors":"Gitanjali Kumari;Dibyanayan Bandyopadhyay;Asif Ekbal;Santanu Pal;Arindam Chatterjee;Vinutha B. N.","doi":"10.1109/TCSS.2024.3362811","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3362811","url":null,"abstract":"Recognizing humor in meme data is a challenging task in natural language processing (NLP) and computer vision (CV) due to the complexity and variability of humor. With the explosive growth of Internet memes on social media platforms such as Facebook, Twitter, and Instagram, this task has become more important. However, there have been few studies that investigate humor recognition from memes, particularly in languages other than English. In this work, we hypothesize that humor is closely related to the valence and arousal dimensions of sentiment. We make the first attempt to release a new meme dataset for humor recognition in Hindi and propose a multitask deep learning framework to simultaneously solve three problems: humor recognition (the primary task) and valence and arousal classification (the two secondary tasks) for Internet memes. Empirical results on the Hindi meme dataset demonstrate the efficacy of our multitask learning approach over traditional pretrained models such as BERT and VGG19. The complete resources and codes will be made available for further research after acceptance of the manuscript.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319603","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 : 2024-04-02DOI: 10.1109/TCSS.2024.3377349
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2024.3377349","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3377349","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342725","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 : 2024-04-02DOI: 10.1109/TCSS.2024.3373928
Rui Qin;Fei-Yue Wang;Xiaolong Zheng;Qinghua Ni;Juanjuan Li;Xiao Xue;Bin Hu
Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.
欢迎阅读 2024 年第二期《电气和电子工程师学会计算社会系统期刊》（IEEE Transactions on Computational Social Systems，TCSS）。本期展示了令人印象深刻的 104 篇常规论文，以及 "大数据和计算社会智能保障金融安全 "特刊，重点介绍了旨在利用大数据和计算技术在数字金融发展中加强金融安全的前沿研究。本期特刊重点关注解决金融大数据的复杂挑战，提高人工智能的效率，并涵盖从数据挖掘到数字货币等关键主题，强调了跨学科工作在降低金融安全风险方面的重要作用。
{"title":"Sora for Computational Social Systems: From Counterfactual Experiments to Artificiofactual Experiments With Parallel Intelligence","authors":"Rui Qin;Fei-Yue Wang;Xiaolong Zheng;Qinghua Ni;Juanjuan Li;Xiao Xue;Bin Hu","doi":"10.1109/TCSS.2024.3373928","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3373928","url":null,"abstract":"Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140346656","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 : 2024-04-02DOI: 10.1109/TCSS.2024.3377351
{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2024.3377351","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3377351","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488823","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342783","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 : 2024-04-02DOI: 10.1109/TCSS.2024.3373929
Changjun Jiang;Fei-Yue Wang;Mengchu Zhou;Asoke K. Nandi;Guanjun Liu
The innovations in technologies have led to the emergence of digital finance such as online payment, online insurance, online lending, and supply chain finance. Digital finance has greatly facilitated people’s lives, accelerated the circulation of capital in various fields, and enhanced the vitality of financial markets. However, it exposes many increasing risks and hidden dangers such as stock volatility, trading fraud, credit card fraud, and privacy leakage [1] , [2] , [3] , [4] , [5] , [6] , [7] . How to effectively calculate, control, manage, and utilize financial big data and make full use of artificial intelligence technology to ensure financial security is an important research question. Solving it faces many challenges. These challenges not only include the complexity of data and computation but also the effectiveness of intelligent optimization algorithms and ways to deal with human behaviors and social environments [8] , [9] .
{"title":"Guest Editorial: Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security","authors":"Changjun Jiang;Fei-Yue Wang;Mengchu Zhou;Asoke K. Nandi;Guanjun Liu","doi":"10.1109/TCSS.2024.3373929","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3373929","url":null,"abstract":"The innovations in technologies have led to the emergence of digital finance such as online payment, online insurance, online lending, and supply chain finance. Digital finance has greatly facilitated people’s lives, accelerated the circulation of capital in various fields, and enhanced the vitality of financial markets. However, it exposes many increasing risks and hidden dangers such as stock volatility, trading fraud, credit card fraud, and privacy leakage \u0000<xref>[1]</xref>\u0000, \u0000<xref>[2]</xref>\u0000, \u0000<xref>[3]</xref>\u0000, \u0000<xref>[4]</xref>\u0000, \u0000<xref>[5]</xref>\u0000, \u0000<xref>[6]</xref>\u0000, \u0000<xref>[7]</xref>\u0000. How to effectively calculate, control, manage, and utilize financial big data and make full use of artificial intelligence technology to ensure financial security is an important research question. Solving it faces many challenges. These challenges not only include the complexity of data and computation but also the effectiveness of intelligent optimization algorithms and ways to deal with human behaviors and social environments \u0000<xref>[8]</xref>\u0000, \u0000<xref>[9]</xref>\u0000.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140346552","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：