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":"11 4","pages":"5525-5535"},"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 : 2024-06-05DOI: 10.1109/TCSS.2024.3398044
Jiaxiang Sun;Rong Zhao;Haoran Yin;Wei Cai
Blockchain technology has garnered significant attention in recent years due to its capacity to offer secure and transparent transactional systems. However, the technology's inherent immutability can present challenges in specific scenarios. While earlier research has concentrated on the development of redactable blockchain, existing solutions have primarily focused on the modification mechanism, often overlooking the critical element of an incentive mechanism for governance, which is paramount for ensuring the security of redactable blockchain. Some previous researches have explored the design of incentive mechanisms, but these studies exhibit certain shortcomings. To promote active participation, we have designed an incentive mechanism rooted in evolutionary game theory for stakeholders in redactable blockchain, aiming to facilitate the governance of redactable blockchain. Furthermore, we have conducted a comprehensive simulation founded on game-theoretic analysis. The results substantiate the effectiveness of our redactable blockchain incentive mechanism in achieving its intended objectives.
{"title":"Incentive Mechanism for Redactable Blockchain Governance: An Evolutionary Game Approach","authors":"Jiaxiang Sun;Rong Zhao;Haoran Yin;Wei Cai","doi":"10.1109/TCSS.2024.3398044","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3398044","url":null,"abstract":"Blockchain technology has garnered significant attention in recent years due to its capacity to offer secure and transparent transactional systems. However, the technology's inherent immutability can present challenges in specific scenarios. While earlier research has concentrated on the development of redactable blockchain, existing solutions have primarily focused on the modification mechanism, often overlooking the critical element of an incentive mechanism for governance, which is paramount for ensuring the security of redactable blockchain. Some previous researches have explored the design of incentive mechanisms, but these studies exhibit certain shortcomings. To promote active participation, we have designed an incentive mechanism rooted in evolutionary game theory for stakeholders in redactable blockchain, aiming to facilitate the governance of redactable blockchain. Furthermore, we have conducted a comprehensive simulation founded on game-theoretic analysis. The results substantiate the effectiveness of our redactable blockchain incentive mechanism in achieving its intended objectives.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6953-6965"},"PeriodicalIF":4.5,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368419","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}
Recommender systems have become an indispensable tool in today's digital age, significantly enhancing user engagement on various online platforms by curating personalized item recommendations tailored to individual preferences. While the field has long been dominated by the collaborative filtering technique, which primarily leverages user–item interaction data, it often falls short in encapsulating the rich contextual intricacies and evolving dynamics inherent to these interactions. Recognizing this limitation, our research introduces the contextual semantic interaction graph embedding (CSI-GE) method. This advanced model incorporates a dynamic hop window within a multilayer graph convolutional network, ensuring a comprehensive extraction of both immediate and evolving contextual features. By amalgamating self-supervised contrastive learning, we achieve a refinement of user and item embeddings. Furthermore, our innovative variance–invariance–covariance (VIC) regularization-based loss function fortifies the robustness of these embeddings. Through rigorous testing, CSI-GE consistently outperformed contemporary methods, underscoring its superior accuracy and stability.
{"title":"Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems","authors":"Shiyu Zhao;Yong Zhang;Mengran Li;Xinglin Piao;Baocai Yin","doi":"10.1109/TCSS.2024.3394701","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3394701","url":null,"abstract":"Recommender systems have become an indispensable tool in today's digital age, significantly enhancing user engagement on various online platforms by curating personalized item recommendations tailored to individual preferences. While the field has long been dominated by the collaborative filtering technique, which primarily leverages user–item interaction data, it often falls short in encapsulating the rich contextual intricacies and evolving dynamics inherent to these interactions. Recognizing this limitation, our research introduces the contextual semantic interaction graph embedding (CSI-GE) method. This advanced model incorporates a dynamic hop window within a multilayer graph convolutional network, ensuring a comprehensive extraction of both immediate and evolving contextual features. By amalgamating self-supervised contrastive learning, we achieve a refinement of user and item embeddings. Furthermore, our innovative variance–invariance–covariance (VIC) regularization-based loss function fortifies the robustness of these embeddings. Through rigorous testing, CSI-GE consistently outperformed contemporary methods, underscoring its superior accuracy and stability.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6333-6346"},"PeriodicalIF":4.5,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368516","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 : 2024-06-05DOI: 10.1109/TCSS.2024.3396413
Haitao Huang;Hu Tian;Xiaolong Zheng;Xingwei Zhang;Daniel Dajun Zeng;Fei-Yue Wang
With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous associations among users within social media contexts, especially the heterogeneous integration of social bots into human communities within the network. To address this challenge, we propose a heterogeneous compatibility perspective for social bot detection, in which we preserve more detailed information about the varying associations between neighbors in social media contexts. Subsequently, we develop a compatibility-aware graph neural network (CGNN) for social bot detection. CGNN consists of an efficient feature processing module, and a lightweight compatibility-aware GNN encoder, which enhances the model’s capacity to depict heterogeneous neighbor relations by emulating the heterogeneous compatibility function. Through extensive experiments, we showed that our CGNN outperforms the existing state-of-the-art (SOTA) method on three commonly used social bot detection benchmarks while utilizing only about 2% of the parameter size and 10% of the training time compared with the SOTA method. Finally, further experimental analysis indicates that CGNN can identify different edge categories to a significant extent. These findings, along with the ablation study, provide strong evidence supporting the enhancement of GNN’s capacity to depict heterogeneous neighbor associations on social media bot detection tasks.
随着社交机器人的兴起和盛行,其对社会的负面影响逐渐被人们所认识,促使人们开始关注有效的检测和对策研究。近年来,图神经网络(GNN)得到了蓬勃发展,并被应用于社交僵尸的检测研究,有效提高了检测方法的性能。然而,现有的基于图神经网络的社交僵尸检测方法往往不能考虑社交媒体语境中用户之间的异构关联,尤其是社交僵尸与网络中人类社区的异构整合。为了应对这一挑战,我们提出了社交僵尸检测的异构兼容性视角,其中我们保留了社交媒体上下文中邻居之间不同关联的更详细信息。随后,我们开发了一种用于社交僵尸检测的兼容性感知图神经网络(CGNN)。图神经网络由一个高效的特征处理模块和一个轻量级兼容性感知图神经网络编码器组成,后者通过模拟异构兼容性函数增强了模型描绘异构邻居关系的能力。通过大量实验,我们发现在三个常用的社交僵尸检测基准上,我们的 CGNN 优于现有的最先进方法(SOTA),而与 SOTA 方法相比,CGNN 的参数大小仅为 SOTA 方法的 2%,训练时间仅为 SOTA 方法的 10%。最后,进一步的实验分析表明,CGNN 可以在很大程度上识别不同的边缘类别。这些发现以及消融研究为增强 GNN 在社交媒体僵尸检测任务中描绘异质邻居关联的能力提供了有力证据。
{"title":"CGNN: A Compatibility-Aware Graph Neural Network for Social Media Bot Detection","authors":"Haitao Huang;Hu Tian;Xiaolong Zheng;Xingwei Zhang;Daniel Dajun Zeng;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3396413","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3396413","url":null,"abstract":"With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous associations among users within social media contexts, especially the heterogeneous integration of social bots into human communities within the network. To address this challenge, we propose a heterogeneous compatibility perspective for social bot detection, in which we preserve more detailed information about the varying associations between neighbors in social media contexts. Subsequently, we develop a compatibility-aware graph neural network (CGNN) for social bot detection. CGNN consists of an efficient feature processing module, and a lightweight compatibility-aware GNN encoder, which enhances the model’s capacity to depict heterogeneous neighbor relations by emulating the heterogeneous compatibility function. Through extensive experiments, we showed that our CGNN outperforms the existing state-of-the-art (SOTA) method on three commonly used social bot detection benchmarks while utilizing only about 2% of the parameter size and 10% of the training time compared with the SOTA method. Finally, further experimental analysis indicates that CGNN can identify different edge categories to a significant extent. These findings, along with the ablation study, provide strong evidence supporting the enhancement of GNN’s capacity to depict heterogeneous neighbor associations on social media bot detection tasks.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6528-6543"},"PeriodicalIF":4.5,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368542","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 : 2024-06-05DOI: 10.1109/TCSS.2024.3392069
Uttam K. Roy;Pranab K. Muhuri;Sajib K. Biswas
Fuzzy community detection (FCD) aims to reveal the community structure by allocating quantitative values to nodes across different communities. This article proposes a fast FCD approach called the Expandable Local Community based Fuzzy Community (XLoCoFC) detection method based on max-membership degree propagation (max-MDP) and normalized peripheral similarity index ( $ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$