Pub Date : 2024-04-08DOI: 10.1109/TCSS.2024.3376958
Krishanu Maity;A. S. Poornash;Shaubhik Bhattacharya;Salisa Phosit;Sawarod Kongsamlit;Sriparna Saha;Kitsuchart Pasupa
Social media platforms are a double-edged sword: on the one hand, they enable the dissemination of information; but on the other hand, they also provide an avenue for spreading online abuse and harassment, such as hate speech. While significant research efforts are being devoted to detecting online hate speech in the English language, little attention has been paid to the Thai language. In this study, we created a benchmark dataset, called HateThaiSent , which labels each post with both hate speech and sentiment information. To detect hate speech, we created a multitask model that uses a dual-channel deep learning approach based on FastText and BERT embeddings, with an added capsule network. One channel utilizes pretrained FastText embeddings while the other uses embeddings from the BERT language model. We aimed to answer two research questions: (Q1) Does incorporating sentiment information improves the performance of hate speech detection (HD) in the Thai language? (Q2) What is the comparative effectiveness of two different approaches for sentiment-aware HD in the Thai language: feature engineering versus multitasking? Our proposed approach outperformed other baselines and state-of-the-art models on the HateThaiSent dataset, with overall accuracy/macro- $F_{1}$ values of 89.67%/89.79%, and 80.92%/80.97% for hate speech and sentiment detection tasks, respectively. We concluded that multitasking is more effective than feature engineering in enhancing the performance of the main task (HD).
{"title":"HateThaiSent: Sentiment-Aided Hate Speech Detection in Thai Language","authors":"Krishanu Maity;A. S. Poornash;Shaubhik Bhattacharya;Salisa Phosit;Sawarod Kongsamlit;Sriparna Saha;Kitsuchart Pasupa","doi":"10.1109/TCSS.2024.3376958","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3376958","url":null,"abstract":"Social media platforms are a double-edged sword: on the one hand, they enable the dissemination of information; but on the other hand, they also provide an avenue for spreading online abuse and harassment, such as hate speech. While significant research efforts are being devoted to detecting online hate speech in the English language, little attention has been paid to the Thai language. In this study, we created a benchmark dataset, called \u0000<italic>HateThaiSent</i>\u0000, which labels each post with both hate speech and sentiment information. To detect hate speech, we created a multitask model that uses a dual-channel deep learning approach based on FastText and BERT embeddings, with an added capsule network. One channel utilizes pretrained FastText embeddings while the other uses embeddings from the BERT language model. We aimed to answer two research questions: (Q1) Does incorporating sentiment information improves the performance of hate speech detection (HD) in the Thai language? (Q2) What is the comparative effectiveness of two different approaches for sentiment-aware HD in the Thai language: feature engineering versus multitasking? Our proposed approach outperformed other baselines and state-of-the-art models on the \u0000<italic>HateThaiSent</i>\u0000 dataset, with overall accuracy/macro-\u0000<inline-formula><tex-math>$F_{1}$</tex-math></inline-formula>\u0000 values of 89.67%/89.79%, and 80.92%/80.97% for hate speech and sentiment detection tasks, respectively. We concluded that multitasking is more effective than feature engineering in enhancing the performance of the main task (HD).","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5714-5727"},"PeriodicalIF":4.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368395","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 : 2024-04-08DOI: 10.1109/TCSS.2024.3376732
Hung-Yue Suen;Kuo-En Hung;Che-Wei Liu;Yu-Sheng Su;Han-Chih Fan
Whether an interviewee’s honest and deceptive responses can be detected by the signals of facial expressions in videos has been debated and called to be researched. We developed deep learning models enabled by computer vision to extract the temporal patterns of job applicants’ facial expressions and head movements to identify self-reported honest and deceptive impression management (IM) tactics from video frames in real asynchronous video interviews. A 12- to 15-min video was recorded for each of the N = 121 job applicants as they answered five structured behavioral interview questions. Each applicant completed a survey to self-evaluate their trustworthiness on four IM measures. Additionally, a field experiment was conducted to compare the concurrent validity associated with self-reported IMs between our modeling and human interviewers. Human interviewers’ performance in predicting these IM measures from another subset of 30 videos was obtained by having N = 30 human interviewers evaluate three recordings. Our models explained 91% and 84% of the variance in honest and deceptive IMs, respectively, and showed a stronger correlation with self-reported IM scores compared to human interviewers.
能否通过视频中的面部表情信号检测出面试者的诚实和欺骗性回答一直是人们争论的焦点，也是有待研究的问题。我们利用计算机视觉技术开发了深度学习模型，以提取求职者面部表情和头部运动的时间模式，从而从真实异步视频面试的视频帧中识别出求职者自述的诚实和欺骗性印象管理（IM）策略。在 N = 121 名求职者回答五个结构化行为面试问题时，为他们每人录制了 12 至 15 分钟的视频。每位求职者都填写了一份调查问卷，就四项即时通讯措施对自己的可信度进行自我评估。此外，我们还进行了一项现场实验，以比较我们的建模与人类面试官之间与自我报告的即时通讯相关的并发有效性。通过让 N = 30 名人类访谈者对三段录像进行评估，获得了人类访谈者从另一个 30 段录像子集中预测这些即时信息测量结果的表现。我们的模型分别解释了 91% 和 84% 的诚实即时信息和欺骗性即时信息的变异，与人类访谈者相比，我们的模型与自我报告的即时信息得分显示出更强的相关性。
{"title":"Artificial Intelligence Can Recognize Whether a Job Applicant Is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers","authors":"Hung-Yue Suen;Kuo-En Hung;Che-Wei Liu;Yu-Sheng Su;Han-Chih Fan","doi":"10.1109/TCSS.2024.3376732","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3376732","url":null,"abstract":"Whether an interviewee’s honest and deceptive responses can be detected by the signals of facial expressions in videos has been debated and called to be researched. We developed deep learning models enabled by computer vision to extract the temporal patterns of job applicants’ facial expressions and head movements to identify self-reported honest and deceptive impression management (IM) tactics from video frames in real asynchronous video interviews. A 12- to 15-min video was recorded for each of the \u0000<italic>N</i>\u0000 = 121 job applicants as they answered five structured behavioral interview questions. Each applicant completed a survey to self-evaluate their trustworthiness on four IM measures. Additionally, a field experiment was conducted to compare the concurrent validity associated with self-reported IMs between our modeling and human interviewers. Human interviewers’ performance in predicting these IM measures from another subset of 30 videos was obtained by having \u0000<italic>N</i>\u0000 = 30 human interviewers evaluate three recordings. Our models explained 91% and 84% of the variance in honest and deceptive IMs, respectively, and showed a stronger correlation with self-reported IM scores compared to human interviewers.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5949-5960"},"PeriodicalIF":4.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377133","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 : 2024-04-05DOI: 10.1109/TCSS.2024.3378349
Lina Gao;Jiguo Yu;Jianli Zhao;Chunqiang Hu
Social recommendation improved the quality and efficiency of recommendation but increased the risk of privacy leakage, especially with the introduction of social networks. Consequently, the social recommendation considering user privacy has drawn tremendous attention from academia to industry. Nevertheless, most of the existing work regards the recommender systems as static, ignoring the diffusion of social influence over time. In this article, we propose a secure and efficient framework, temporal privacy-preserving social recommendation model (PrivTSR), to capture the changes of user preference for items and item types with time. PrivTSR first utilizes differential privacy to encrypt the data owned by the data owner. Then, inspired by the long short-term memory (LSTM), at each time step the initial user embedding and the initial item embedding are generated via DeepWalk as new ratings of users for items emerges in the user–item-type graph. The initial user-preference embedding is generated randomly at the first time step, and it is equivalent to the updated embedding of the previous time step for the later time steps. Most importantly, on the social graph, PrivTSR updates the user embedding and the user-preference embedding with graph attention convolutional network and graph attention diffused network, which aggregates (diffuses) social influence from (to) neighbors in depth and breadth. On the user–item-type graph, the user embedding and the item embedding are updated by aggregating the embedding of users and items in the six paths. Final, taking into account the users’ preference for items and item types, PrivTSR predicts the ratings of users to the items for the next time step. The extensive experiments are conducted on two real-world datasets, which demonstrated the superiority of our model over several competitive baselines.
{"title":"A Novel Temporal Privacy-Preserving Model for Social Recommendation","authors":"Lina Gao;Jiguo Yu;Jianli Zhao;Chunqiang Hu","doi":"10.1109/TCSS.2024.3378349","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3378349","url":null,"abstract":"Social recommendation improved the quality and efficiency of recommendation but increased the risk of privacy leakage, especially with the introduction of social networks. Consequently, the social recommendation considering user privacy has drawn tremendous attention from academia to industry. Nevertheless, most of the existing work regards the recommender systems as static, ignoring the diffusion of social influence over time. In this article, we propose a secure and efficient framework, temporal privacy-preserving social recommendation model (PrivTSR), to capture the changes of user preference for items and item types with time. PrivTSR first utilizes differential privacy to encrypt the data owned by the data owner. Then, inspired by the long short-term memory (LSTM), at each time step the initial user embedding and the initial item embedding are generated via DeepWalk as new ratings of users for items emerges in the user–item-type graph. The initial user-preference embedding is generated randomly at the first time step, and it is equivalent to the updated embedding of the previous time step for the later time steps. Most importantly, on the social graph, PrivTSR updates the user embedding and the user-preference embedding with graph attention convolutional network and graph attention diffused network, which aggregates (diffuses) social influence from (to) neighbors in depth and breadth. On the user–item-type graph, the user embedding and the item embedding are updated by aggregating the embedding of users and items in the six paths. Final, taking into account the users’ preference for items and item types, PrivTSR predicts the ratings of users to the items for the next time step. The extensive experiments are conducted on two real-world datasets, which demonstrated the superiority of our model over several competitive baselines.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5658-5670"},"PeriodicalIF":4.5,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368319","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 : 2024-04-05DOI: 10.1109/TCSS.2024.3378065
Daniel Wai Kit Chin;Kwan Hui Lim;Roy Ka-Wei Lee
The rise of social media has enabled individuals to rapidly share information, including rumors, which can have significant impacts on various domains. Traditional approaches to rumor control are impractical for social media platforms due to the volume and speed of information. Automated detection methods are needed that not only identify rumors early but also provide explanations for their decisions to protect free speech. Recent advancements in deep learning have shown promise in automating rumor detection. Graph-based models, such as bidirectional graph convolution network (Bi-GCN), capture propagation, and dispersion patterns to differentiate rumors from the truth. However, the interpretability of these deep learning models is a challenge. This article focuses on graph convolution networks (GCNs), which lack attention maps for easy model attribution but excel at capturing global structural features. We investigate the importance of graph structure in rumor detection using two GCN models on a real-world dataset, analyzing the learned latent propagation and dispersion features. To the best of our knowledge, this is the first study to explore GCNs in rumor detection and investigate the significance of graph structure in this task. Our research addresses three primary questions: 1) the primary contributors to GCN-based rumor detection models and their differences across models; 2) the importance of graph structure for accurate predictions in GCN-based models; and 3) the latent propagation and dispersion features learned by GCN-based detection models during the rumor detection process.
{"title":"RumorGraphXplainer: Do Structures Really Matter in Rumor Detection","authors":"Daniel Wai Kit Chin;Kwan Hui Lim;Roy Ka-Wei Lee","doi":"10.1109/TCSS.2024.3378065","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3378065","url":null,"abstract":"The rise of social media has enabled individuals to rapidly share information, including rumors, which can have significant impacts on various domains. Traditional approaches to rumor control are impractical for social media platforms due to the volume and speed of information. Automated detection methods are needed that not only identify rumors early but also provide explanations for their decisions to protect free speech. Recent advancements in deep learning have shown promise in automating rumor detection. Graph-based models, such as bidirectional graph convolution network (Bi-GCN), capture propagation, and dispersion patterns to differentiate rumors from the truth. However, the interpretability of these deep learning models is a challenge. This article focuses on graph convolution networks (GCNs), which lack attention maps for easy model attribution but excel at capturing global structural features. We investigate the importance of graph structure in rumor detection using two GCN models on a real-world dataset, analyzing the learned latent propagation and dispersion features. To the best of our knowledge, this is the first study to explore GCNs in rumor detection and investigate the significance of graph structure in this task. Our research addresses three primary questions: 1) the primary contributors to GCN-based rumor detection models and their differences across models; 2) the importance of graph structure for accurate predictions in GCN-based models; and 3) the latent propagation and dispersion features learned by GCN-based detection models during the rumor detection process.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6038-6055"},"PeriodicalIF":4.5,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377132","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":"11 3","pages":"4385-4395"},"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 : 2024-04-03DOI: 10.1109/TCSS.2024.3376718
Haoran Yin;Jiaxiang Sun;Wei Cai
With the rapid advancement of free-to-play games featuring in-game payments, the loot box sales model challenges traditional fixed-price purchases by stimulating players’ psychological desires. However, concerns have arisen among players regarding the transparency of loot box drop rates as claimed by game companies. Considering players’ “partial information” and “bounded rationality” in-game, this study explores strategic interactions and evolutionary outcomes under various decision-making scenarios through a bipartite evolutionary game model, employing the replication dynamics approach. In contrast to conventional studies that examine market manipulation post hoc, evolutionary game theory enables a proactive examination of strategies to foster fairness within the loot box game market. The findings indicate that the dynamic between companies and players may ultimately converge to either a lose–lose or win–win scenario. Through simulation analysis, the impact of stakeholders’ initial strategic ratios and critical parameters on the evolutionary trajectory is examined. It is observed that companies hold a dominant position in enhancing cooperation, whereas regulatory bodies can foster industry development through heightened regulation. This assertion is substantiated with real-world examples. The goal of this research is to advocate for integrity and transparency within the gaming industry, ensuring a fair environment for players.
{"title":"Honest or Dishonest? Promoting Integrity in Loot Box Games Through Evolutionary Game Theory","authors":"Haoran Yin;Jiaxiang Sun;Wei Cai","doi":"10.1109/TCSS.2024.3376718","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3376718","url":null,"abstract":"With the rapid advancement of free-to-play games featuring in-game payments, the loot box sales model challenges traditional fixed-price purchases by stimulating players’ psychological desires. However, concerns have arisen among players regarding the transparency of loot box drop rates as claimed by game companies. Considering players’ “partial information” and “bounded rationality” in-game, this study explores strategic interactions and evolutionary outcomes under various decision-making scenarios through a bipartite evolutionary game model, employing the replication dynamics approach. In contrast to conventional studies that examine market manipulation post hoc, evolutionary game theory enables a proactive examination of strategies to foster fairness within the loot box game market. The findings indicate that the dynamic between companies and players may ultimately converge to either a lose–lose or win–win scenario. Through simulation analysis, the impact of stakeholders’ initial strategic ratios and critical parameters on the evolutionary trajectory is examined. It is observed that companies hold a dominant position in enhancing cooperation, whereas regulatory bodies can foster industry development through heightened regulation. This assertion is substantiated with real-world examples. The goal of this research is to advocate for integrity and transparency within the gaming industry, ensuring a fair environment for players.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5961-5972"},"PeriodicalIF":4.5,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377048","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":"11 2","pages":"C3-C3"},"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":"11 2","pages":"1531-1550"},"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":"11 2","pages":"C4-C4"},"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":"11 2","pages":"1551-1555"},"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}
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