Pub Date : 2024-04-26DOI: 10.1109/TCSS.2024.3385025
Tun Li;Zhou Li;Kexin Ma;Qian Li;Rong Wang;Chaolong Jia;Yunpeng Xiao
The open and free nature of online platforms presents challenges for tracing malicious information. To address this, we propose a traceability model based on neighborhood similarity and multitype interaction. First, we propose neighborhood similarity algorithms (D-NTC) to address the universality of malicious information dissemination. This algorithm evaluates the impact of user node importance on malicious information propagation by combining node degrees and the topological overlap of neighboring nodes. Second, we consider the interactive nature of multiple types of elements in the network and construct an interactive module based on user-path-malicious information. This module effectively captures the mutual influence relationships among diverse elements. Additionally, we employ representation learning to optimize the transition probability matrix between elements, leveraging hidden relationships to further characterize their interactive impact. Finally, we propose the NSMTI-Rank algorithm, which tackles the complexity of quantifying the influence of multiple types of elements. Drawing inspiration from mutual reinforcement effects, NSMTI-Rank comprehensively quantifies element influence through an iterative framework. Experimental results demonstrate the effectiveness of our approach in mining user node importance and capturing the interaction information among diverse elements in the network. Moreover, it enables the timely and effective identification of sources of malicious information dissemination.
{"title":"A Malicious Information Traceability Model Based on Neighborhood Similarity and Multiple Types of Interaction","authors":"Tun Li;Zhou Li;Kexin Ma;Qian Li;Rong Wang;Chaolong Jia;Yunpeng Xiao","doi":"10.1109/TCSS.2024.3385025","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3385025","url":null,"abstract":"The open and free nature of online platforms presents challenges for tracing malicious information. To address this, we propose a traceability model based on neighborhood similarity and multitype interaction. First, we propose neighborhood similarity algorithms (D-NTC) to address the universality of malicious information dissemination. This algorithm evaluates the impact of user node importance on malicious information propagation by combining node degrees and the topological overlap of neighboring nodes. Second, we consider the interactive nature of multiple types of elements in the network and construct an interactive module based on user-path-malicious information. This module effectively captures the mutual influence relationships among diverse elements. Additionally, we employ representation learning to optimize the transition probability matrix between elements, leveraging hidden relationships to further characterize their interactive impact. Finally, we propose the NSMTI-Rank algorithm, which tackles the complexity of quantifying the influence of multiple types of elements. Drawing inspiration from mutual reinforcement effects, NSMTI-Rank comprehensively quantifies element influence through an iterative framework. Experimental results demonstrate the effectiveness of our approach in mining user node importance and capturing the interaction information among diverse elements in the network. Moreover, it enables the timely and effective identification of sources of malicious information dissemination.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5815-5827"},"PeriodicalIF":4.5,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368316","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 : 2024-04-26DOI: 10.1109/TCSS.2024.3384698
Sujit Kumar;Saurabh Kumar;Sanasam Ranbir Singh
The prevalence of incongruent news has demonstrated its significant role in propagating fake news, which catalyzes the dissemination of both misinformation and disinformation. Consequently, detecting incongruent news articles is an important research problem to counter early spreading of misinformation. In the literature, researchers have explored various bag-of-word-based features, news body-centric and news headline-centric encoding methods for incongruent news article detection. However, headline-centric and body-centric approaches in the literature fail to detect partially incongruent articles efficiently. Motivated by the above limitations, this study proposes graph-based dual context matching (GDCM), which first represents headlines and news bodies as a bigram network to capture contextual relations between words and document structure. For every word in the headline, GDCM extracts dual contexts (positive and negative) from the bigram network representing news body and estimates similarity between dual contexts and the headline for incongruent news detection. We conduct extensive experiments on three publicly available benchmark datasets and compare its performance with 16 baseline models. Our experimental results suggest that the proposed model outperforms existing state-of-the-art models and efficiently detects partially incongruent news. We further validate the performance of the proposed model through several ablation studies. The following key observations can be made from the ablation studies: 1) extracting dual bigram context of words in the headline from different segments of news body and then estimating the similarity between dual bigram contexts from news body and the headline helps in incongruent news detection and also helps in detecting partial incongruent news efficiently; and 2) representing news headlines and bodies in the form of a network based on bigram context helps to capture better nonlinear and contextual relationships between headline and body.
{"title":"A Headline-Centric Graph-Based Dual Context Matching Approach for Incongruent News Detection","authors":"Sujit Kumar;Saurabh Kumar;Sanasam Ranbir Singh","doi":"10.1109/TCSS.2024.3384698","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3384698","url":null,"abstract":"The prevalence of incongruent news has demonstrated its significant role in propagating fake news, which catalyzes the dissemination of both misinformation and disinformation. Consequently, detecting incongruent news articles is an important research problem to counter early spreading of misinformation. In the literature, researchers have explored various bag-of-word-based features, news body-centric and news headline-centric encoding methods for incongruent news article detection. However, headline-centric and body-centric approaches in the literature fail to detect partially incongruent articles efficiently. Motivated by the above limitations, this study proposes graph-based dual context matching (GDCM), which first represents headlines and news bodies as a \u0000<italic>bigram</i>\u0000 network to capture contextual relations between words and document structure. For every word in the headline, GDCM extracts dual contexts (positive and negative) from the \u0000<italic>bigram</i>\u0000 network representing news body and estimates similarity between dual contexts and the headline for incongruent news detection. We conduct extensive experiments on three publicly available benchmark datasets and compare its performance with 16 baseline models. Our experimental results suggest that the proposed model outperforms existing state-of-the-art models and efficiently detects partially incongruent news. We further validate the performance of the proposed model through several ablation studies. The following key observations can be made from the ablation studies: 1) extracting dual \u0000<italic>bigram</i>\u0000 context of words in the headline from different segments of news body and then estimating the similarity between dual \u0000<italic>bigram</i>\u0000 contexts from news body and the headline helps in incongruent news detection and also helps in detecting partial incongruent news efficiently; and 2) representing news headlines and bodies in the form of a network based on \u0000<italic>bigram</i>\u0000 context helps to capture better nonlinear and contextual relationships between headline and body.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5913-5924"},"PeriodicalIF":4.5,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377152","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 : 2024-04-25DOI: 10.1109/TCSS.2024.3383241
Liman Du;Wenguo Yang;Suixiang Gao
Social-media platforms provide citizens a new way to stay informed and offer marketers a shot at promoting their brand. In social advertising, information diversity can create a level playing field for competitive information dissemination. Team diversity is important because teams with a diverse composition tend to perform better over time. The former demands that all the social networks’ users should receive diverse information. The later requires teams to be diverse with respect to team members’ attributes. However, to our knowledge, not only the diversity of the information spreading in the social network but also the influential users’ attributes are never simultaneously considered in research. Therefore, we propose a novel information- and team-based dual-diversity maximization (ITDM) problem in this article. The dual-diversity focused by the ITDM problem can be cast as a combination of information diversity and team diversity. The goal of ITDM problem is to obtain a good strategy for building marketing teams composed of influential social networks’ users. To some extent, this problem is an extension of classical IM problem that aims at selecting some influential users to trigger large information spread in social networks. The main difference between them is that team composition is taken into consideration by ITDM problem. Given that the ITDM problem is challenging, an algorithm on the foundation of Shapley value and negative-cycle-detection is designed to address it. We experimentally demonstrate the effectiveness of our algorithm on several real-world datasets.
{"title":"Team Composition for Competitive Information Spread: Dual-Diversity Maximization Based on Information and Team","authors":"Liman Du;Wenguo Yang;Suixiang Gao","doi":"10.1109/TCSS.2024.3383241","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3383241","url":null,"abstract":"Social-media platforms provide citizens a new way to stay informed and offer marketers a shot at promoting their brand. In social advertising, information diversity can create a level playing field for competitive information dissemination. Team diversity is important because teams with a diverse composition tend to perform better over time. The former demands that all the social networks’ users should receive diverse information. The later requires teams to be diverse with respect to team members’ attributes. However, to our knowledge, not only the diversity of the information spreading in the social network but also the influential users’ attributes are never simultaneously considered in research. Therefore, we propose a novel information- and team-based dual-diversity maximization (ITDM) problem in this article. The dual-diversity focused by the ITDM problem can be cast as a combination of information diversity and team diversity. The goal of ITDM problem is to obtain a good strategy for building marketing teams composed of influential social networks’ users. To some extent, this problem is an extension of classical IM problem that aims at selecting some influential users to trigger large information spread in social networks. The main difference between them is that team composition is taken into consideration by ITDM problem. Given that the ITDM problem is challenging, an algorithm on the foundation of Shapley value and negative-cycle-detection is designed to address it. We experimentally demonstrate the effectiveness of our algorithm on several real-world datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5984-5996"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376990","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}
Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with the problem of incompleteness and sparseness. Besides, the existing KG-based methods could not discriminate the importance of different factors that users consider when making decisions, which may degrade the interpretability of the methods. In this article, we propose a recommendation model named community enhanced knowledge graph for recommendation (CEKGR). By adding entities and relations, the KG is enriched with more semantic information, which would help mine users’ preference for better recommendation. With weights of each path, the interpretability of the recommendation can be improved. To validate the effectiveness of the proposed method, we conduct experiments on three public datasets. Experiment results have shown the improvement compared with other state-of-the-art methods. Besides, case study has illustrated the interpretability of the proposed recommendation model.
由于知识图谱（KG）具有编码辅助信息以缓解数据稀疏性问题的能力，因此近年来受到越来越多的关注。有了关于项目的辅助知识，与现有方法相比，基于 KG 的推荐系统取得了更好的性能。然而，基于知识图谱的方法的有效性在很大程度上取决于知识图谱的质量。遗憾的是，KG 通常存在不完整和稀疏的问题。此外，现有的基于 KG 的方法无法区分用户在决策时考虑的不同因素的重要性，这可能会降低方法的可解释性。在本文中，我们提出了一种名为 "用于推荐的社区增强知识图谱（CEKGR）"的推荐模型。通过添加实体和关系，知识图谱被赋予了更多语义信息，这将有助于挖掘用户的偏好以获得更好的推荐。有了每条路径的权重，就能提高推荐的可解释性。为了验证所提方法的有效性，我们在三个公共数据集上进行了实验。实验结果表明，与其他最先进的方法相比，该方法有了很大的改进。此外，案例研究也说明了建议推荐模型的可解释性。
{"title":"Community Enhanced Knowledge Graph for Recommendation","authors":"Zhen-Yu He;Chang-Dong Wang;Jinfeng Wang;Jian-Huang Lai;Yong Tang","doi":"10.1109/TCSS.2024.3383603","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3383603","url":null,"abstract":"Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with the problem of incompleteness and sparseness. Besides, the existing KG-based methods could not discriminate the importance of different factors that users consider when making decisions, which may degrade the interpretability of the methods. In this article, we propose a recommendation model named community enhanced knowledge graph for recommendation (CEKGR). By adding entities and relations, the KG is enriched with more semantic information, which would help mine users’ preference for better recommendation. With weights of each path, the interpretability of the recommendation can be improved. To validate the effectiveness of the proposed method, we conduct experiments on three public datasets. Experiment results have shown the improvement compared with other state-of-the-art methods. Besides, case study has illustrated the interpretability of the proposed recommendation model.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5789-5802"},"PeriodicalIF":4.5,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368339","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 : 2024-04-23DOI: 10.1109/TCSS.2024.3378454
Haibing Hu;Kai Han;Zhizhuo Yin;Defu Lian
In recent years, recommendation systems have become increasingly prevalent in various fields, facilitating quick access to the information users need. As a result, many models have been proposed to model user interests, leading to more accurate recommendation lists, superior user experience, and business value. However, characterizing the dynamically changing interests of users is a challenging task. User interests shift over time while maintaining some long-term interests, and at each time, users’ interests are diverse. To investigate the benefits of multidimensional interests for users, this article proposes to characterize user preferences based on their spatiotemporal interests. Utilizing temporal and spatial information is critical for improving recommendation accuracy. To achieve this, we present a novel approach called multilong short-term interest (MLSI) user representation for recommendation. This method extracts long-term and short-term interests of users from their behavioral sequences using decoupled self-supervised learning with different optimizers. Self-attention is then employed to capture the diverse interests of users through their behavioral sequences. Final, long-term and short-term interests, as well as diversified interests, are aggregated to represent user interests. Extensive experiments on real-world datasets show that MLSI not only outperforms state-of-the-art methods but also more effectively characterizes user interests, reflecting an improvement ranging from 5% to 20% across various metrics on multiple datasets.
{"title":"Spatial and Temporal User Interest Representations for Sequential Recommendation","authors":"Haibing Hu;Kai Han;Zhizhuo Yin;Defu Lian","doi":"10.1109/TCSS.2024.3378454","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3378454","url":null,"abstract":"In recent years, recommendation systems have become increasingly prevalent in various fields, facilitating quick access to the information users need. As a result, many models have been proposed to model user interests, leading to more accurate recommendation lists, superior user experience, and business value. However, characterizing the dynamically changing interests of users is a challenging task. User interests shift over time while maintaining some long-term interests, and at each time, users’ interests are diverse. To investigate the benefits of multidimensional interests for users, this article proposes to characterize user preferences based on their spatiotemporal interests. Utilizing temporal and spatial information is critical for improving recommendation accuracy. To achieve this, we present a novel approach called multilong short-term interest (MLSI) user representation for recommendation. This method extracts long-term and short-term interests of users from their behavioral sequences using decoupled self-supervised learning with different optimizers. Self-attention is then employed to capture the diverse interests of users through their behavioral sequences. Final, long-term and short-term interests, as well as diversified interests, are aggregated to represent user interests. Extensive experiments on real-world datasets show that MLSI not only outperforms state-of-the-art methods but also more effectively characterizes user interests, reflecting an improvement ranging from 5% to 20% across various metrics on multiple datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6087-6097"},"PeriodicalIF":4.5,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376837","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 : 2024-04-23DOI: 10.1109/TCSS.2024.3383493
Fei Gao;Qiang He;Xingwei Wang;Lin Qiu;Min Huang
Social networks currently serve as one of the primary sources from which people obtain news, with the spread of rumors emerging as a major concern. The goal of rumor suppression is to minimize the number of individuals affected by rumors through various methods, such as blocking and disseminating the truth. Although this problem has evolved into a popular research topic, existing solutions often overlook the temporal impact of rumor-refuting information and the influence of user opinions on rumor spreading. In the study, we first investigate the two-stage rumor minimization problem. The problem primarily considers two situations about only the propagation of rumors and the simultaneous propagation of rumor and rumor-refuting information, aiming to minimize the impact of rumors. We propose the two-stage user opinion rumor propagation model (TSUORP), which fully incorporates the timing of official releases of rumor-refuting information and their influence on the generation of rumors propagation. Based on this, we propose an approach using the knowledge graph convolutional network (KGCN) algorithm to rapidly and effectively select rumor-refuting information seed nodes based on user opinions. To assess the validity of our proposed approach, we perform experiments on three authentic datasets, showcasing its notable advantages.
{"title":"An Efficient Rumor Suppression Approach With Knowledge Graph Convolutional Network in Social Network","authors":"Fei Gao;Qiang He;Xingwei Wang;Lin Qiu;Min Huang","doi":"10.1109/TCSS.2024.3383493","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3383493","url":null,"abstract":"Social networks currently serve as one of the primary sources from which people obtain news, with the spread of rumors emerging as a major concern. The goal of rumor suppression is to minimize the number of individuals affected by rumors through various methods, such as blocking and disseminating the truth. Although this problem has evolved into a popular research topic, existing solutions often overlook the temporal impact of rumor-refuting information and the influence of user opinions on rumor spreading. In the study, we first investigate the two-stage rumor minimization problem. The problem primarily considers two situations about only the propagation of rumors and the simultaneous propagation of rumor and rumor-refuting information, aiming to minimize the impact of rumors. We propose the two-stage user opinion rumor propagation model (TSUORP), which fully incorporates the timing of official releases of rumor-refuting information and their influence on the generation of rumors propagation. Based on this, we propose an approach using the knowledge graph convolutional network (KGCN) algorithm to rapidly and effectively select rumor-refuting information seed nodes based on user opinions. To assess the validity of our proposed approach, we perform experiments on three authentic datasets, showcasing its notable advantages.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6254-6267"},"PeriodicalIF":4.5,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368541","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}
Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire encouragement and advertisement from conventional Web2 companies and projects due to their low market shares. In this article, we propose a seamless transition framework that transits Web2 to Web3, named WebttCom [ WebttCom stands for Web2 (two)–Web3 (three) Communicator], after exploring the connotation of Web3 and the key differences between Web2 and Web3 applications. We also provide a full-stack implementation as a use case to support the proposed framework, followed by performance evaluation and surveys with $sim$ 1000 participants that show $sim$ 80% positive and $sim$ 20% neutral responses. We confirm that the proposed framework WebttCom addresses the defined research question, and the implementation well satisfies the framework WebttCom in terms of strong necessity , usability , and completeness based on the survey results.
{"title":"Toward Web3 Applications: Easing the Access and Transition","authors":"Guangsheng Yu;Xu Wang;Qin Wang;Tingting Bi;YiFei Dong;Ren Ping Liu;Nektarios Georgalas;Andrew Reeves","doi":"10.1109/TCSS.2024.3382582","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3382582","url":null,"abstract":"Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire encouragement and advertisement from conventional Web2 companies and projects due to their low market shares. In this article, we propose a seamless transition framework that transits Web2 to Web3, named \u0000<sc>WebttCom</small>\u0000 [\u0000<sc>WebttCom</small>\u0000 stands for Web2 (two)–Web3 (three) Communicator], after exploring the connotation of Web3 and the key differences between Web2 and Web3 applications. We also provide a full-stack implementation as a use case to support the proposed framework, followed by performance evaluation and surveys with \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00001000 participants that show \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u000080% positive and \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u000020% neutral responses. We confirm that the proposed framework \u0000<sc>WebttCom</small>\u0000 addresses the defined research question, and the implementation well satisfies the framework \u0000<sc>WebttCom</small>\u0000 in terms of strong \u0000<italic>necessity</i>\u0000, \u0000<italic>usability</i>\u0000, and \u0000<italic>completeness</i>\u0000 based on the survey results.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6098-6111"},"PeriodicalIF":4.5,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377128","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}
Studying the evolution of online news communities is essential for improving the effectiveness of news recommender systems. Traditionally, this has been done through empirical research based on static data analysis. While this approach has yielded valuable insights for optimizing recommender system designs, it is limited by the lack of appropriate datasets and open platforms for controlled social experiments. This gap in the existing literature hinders a comprehensive understanding of the impact of recommender systems on the evolutionary process and its underlying mechanisms. As a result, suboptimal system designs may be developed that could negatively affect long-term utilities. In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms. SimuLine first constructs a latent space well reflecting the human behaviors and then simulates the news recommendation ecosystem via agent-based modeling. Based on extensive simulation experiments and the comprehensive analysis framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory and propose a relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the impacts of recommender system designing strategies, including the utilization of cold-start news, breaking news, and promotion, on the evolutionary process, which sheds new light on the design of recommender systems.
{"title":"Simulating News Recommendation Ecosystems for Insights and Implications","authors":"Guangping Zhang;Dongsheng Li;Hansu Gu;Tun Lu;Li Shang;Ning Gu","doi":"10.1109/TCSS.2024.3381329","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3381329","url":null,"abstract":"Studying the evolution of online news communities is essential for improving the effectiveness of news recommender systems. Traditionally, this has been done through empirical research based on static data analysis. While this approach has yielded valuable insights for optimizing recommender system designs, it is limited by the lack of appropriate datasets and open platforms for controlled social experiments. This gap in the existing literature hinders a comprehensive understanding of the impact of recommender systems on the evolutionary process and its underlying mechanisms. As a result, suboptimal system designs may be developed that could negatively affect long-term utilities. In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms. SimuLine first constructs a latent space well reflecting the human behaviors and then simulates the news recommendation ecosystem via agent-based modeling. Based on extensive simulation experiments and the comprehensive analysis framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory and propose a relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the impacts of recommender system designing strategies, including the utilization of cold-start news, breaking news, and promotion, on the evolutionary process, which sheds new light on the design of recommender systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5699-5713"},"PeriodicalIF":4.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368536","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 : 2024-04-18DOI: 10.1109/TCSS.2024.3383270
Zhong Huang;Mengyuan Tao;Ning An;Min Hu;Fuji Ren
Behavior detection receives considerable attention in real-life human–computer interaction, where the complexity of background information and the variable durations of movements are two major factors affecting the accuracy of behavior detection. To overcome the inadequacy of these factors, this article proposes an enhancing region convolutional 3-D (ER-C3D) network with adaptive shrinkage and symmetrical multiscale for behavior detection. The improved ER-C3D network includes a feature subnet, a proposal subnet, and a classification subnet. First, a 3D-RSST unit is constructed by embedding an adaptive shrinkage structure and a soft thresholding operation. Meanwhile, a residual adaptive shrinkage mechanism, composed of multiple cascaded 3D-RSST units with different parameters, is designed to reduce redundant information of video streams in the feature subnet. Second, a spatiotemporal symmetrical multiscale structure is substituted for the single-layer convolution and embedded into the proposal subnet. Specially, contextual symmetrical multiscale motion characteristics with different levels and granularities are acquired by expanding the spatiotemporal receptive field of candidate temporal proposals. Finally, a soft-nonmaximal suppression strategy is introduced to filter high-quality temporal proposals in the classification subnet. The experimental results on the THUMOS’14 and ActivityNet1.2 datasets indicate that the mAP@0.5 of the improved ER-C3D network reaches 39.4% and 42.2%, respectively, which is 10.5% and 15.4% higher than R-C3D. Compared with related methods, the proposed method shows improvement in both the positional precision of behavioral boundary and the accuracy of behavioral classification.
{"title":"ER-C3D: Enhancing R-C3-D Network With Adaptive Shrinkage and Symmetrical Multiscale for Behavior Detection","authors":"Zhong Huang;Mengyuan Tao;Ning An;Min Hu;Fuji Ren","doi":"10.1109/TCSS.2024.3383270","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3383270","url":null,"abstract":"Behavior detection receives considerable attention in real-life human–computer interaction, where the complexity of background information and the variable durations of movements are two major factors affecting the accuracy of behavior detection. To overcome the inadequacy of these factors, this article proposes an enhancing region convolutional 3-D (ER-C3D) network with adaptive shrinkage and symmetrical multiscale for behavior detection. The improved ER-C3D network includes a feature subnet, a proposal subnet, and a classification subnet. First, a 3D-RSST unit is constructed by embedding an adaptive shrinkage structure and a soft thresholding operation. Meanwhile, a residual adaptive shrinkage mechanism, composed of multiple cascaded 3D-RSST units with different parameters, is designed to reduce redundant information of video streams in the feature subnet. Second, a spatiotemporal symmetrical multiscale structure is substituted for the single-layer convolution and embedded into the proposal subnet. Specially, contextual symmetrical multiscale motion characteristics with different levels and granularities are acquired by expanding the spatiotemporal receptive field of candidate temporal proposals. Finally, a soft-nonmaximal suppression strategy is introduced to filter high-quality temporal proposals in the classification subnet. The experimental results on the THUMOS’14 and ActivityNet1.2 datasets indicate that the mAP@0.5 of the improved ER-C3D network reaches 39.4% and 42.2%, respectively, which is 10.5% and 15.4% higher than R-C3D. Compared with related methods, the proposed method shows improvement in both the positional precision of behavioral boundary and the accuracy of behavioral classification.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5997-6009"},"PeriodicalIF":4.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377154","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 : 2024-04-18DOI: 10.1109/TCSS.2024.3382944
Tsung Heng Wu;Md Amiruzzaman;Ye Zhao;Deepshikha Bhati;Jing Yang
Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this article, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate these discovered patterns into driving route planners with new visualization techniques. Finally, we present VivaRoutes, an interactive visualization prototype, to show how visualizations leveraged with these discovered patterns can help users effectively and interactively explore multiple routes. Furthermore, we conducted a user study to assess the usefulness and utility of VivaRoutes.
{"title":"Visualizing Routes With AI-Discovered Street-View Patterns","authors":"Tsung Heng Wu;Md Amiruzzaman;Ye Zhao;Deepshikha Bhati;Jing Yang","doi":"10.1109/TCSS.2024.3382944","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3382944","url":null,"abstract":"Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this article, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate these discovered patterns into driving route planners with new visualization techniques. Finally, we present VivaRoutes, an interactive visualization prototype, to show how visualizations leveraged with these discovered patterns can help users effectively and interactively explore multiple routes. Furthermore, we conducted a user study to assess the usefulness and utility of VivaRoutes.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6380-6391"},"PeriodicalIF":4.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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