Rumor Diffusion Model Based on Representation Learning and Anti-Rumor

Xiao, Yunpeng; Yang, Qiufan; Sang, Chunyan; Liu, Yanbing

doi:10.1109/tnsm.2020.2994141

Cited by 51 publications

(9 citation statements)

References 37 publications

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“…Having combined the characteristics of trajectory data and the definition of a knowledge graph, Wu Xian et al [38] extracted the entities, relationships, and attributes of trajectory data and constructed the trajectory map, which supports basic queries, range queries, nearest neighbour queries, keyword queries and trajectory mode queries. Yunpeng Xiao et al [39] proposed a rumour propagation dynamics model based on an evolutionary game and anti-rumour information, and the model can effectively describe the propagation of rumours and the dynamic change rule of the influence of anti-rumour information, and further proposed a group behavior model for rumor and anti-rumor [40]. In paper [41], Yunpeng Xiao et al used user multidimensional attributes and evolutionary games combined with the traditional susceptible-infectedrecovered (SIR) epidemic model, which was used to quantify the impacts of external and internal driving factors on group state transitions during hotspot propagation.…”

Section: B Geographical Knowledge Graphmentioning

confidence: 99%

Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19

Jiang

Xiong

et al. 2022

IEEE Access

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In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this paper combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of the related technologies of the geographic knowledge graph and big data visual analysis. Then, it realizes the situational analysis of COVID-19 (Coronavirus Disease 2019) and the exploration of patient relationships through interactive collaborative analysis. The main contributions of the paper are as follows. (1) Based on the characteristics of the geographic knowledge graph, a patient entity model and an entity relationship type and knowledge representation method are proposed, and a knowledge graph of the spatiotemporal information of COVID-19 is constructed. (2) To analyse the COVID-19 patients' situations and explore their relationships, an analytical framework is designed. The framework, combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, allows one to analyse the semantic web by using the node attribute similarity calculation, key stage mining, community prediction and other methods. (3) An efficient epidemic prevention and anti-epidemic method, which is of referential significance, is proposed. It is based on experiments and the collaborative analysis of the semantic web and spatial information, allowing for real-time situational understanding, the discovery of patients' relationships, the analysis of the spatiotemporal distribution of patients, super spreader mining, key node analysis, and the prevention and control of high-risk groups. INDEX TERMS COVID-19; geographic knowledge graph; spatiotemporal big data; visual analysis

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Section: B Geographical Knowledge Graphmentioning

confidence: 99%

Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19

Jiang

Xiong

et al. 2022

IEEE Access

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“…Other recent research [71]- [72] on the aspect of how to understand social media application management explains that the complexity of user behaviour, the multidimensional communication space, the imbalance of the data sample, and the symbiosis and competition between rumours and anti-rumours are challenges associated with in-depth studies of the topic of misinformation and rumour context.…”

Section: A Attention-based Designmentioning

confidence: 99%

Attention-Based Design and User Decisions on Information Sharing: A Thematic Literature Review

2021

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The spread of misinformation and disinformation online can do serious damage to individuals, organizations, and society in general. To fully comprehend user interaction when sharing information online, we need to examine why users decide to share misinformation without attentive behavior and how the latest attention-based design approach can address this. To try to understand this issue, we investigate and represent knowledge based on Human-Computer Interaction by applying an ontological approach through a thematic literature review to describe a clearly coherent and well-defined pattern about the relationship between attentionbased design and user decisions on information sharing. We conducted a review to collect, examine, and synthesize previous studies, mixing both forward and backward search strategies. Three key themes we identified include attention-based design, attentive behavior for information sharing, and attention-based design on information sharing. The review interpreted that, (1) attention-based design is significantly related to user decisions on information sharing, and a better understanding of the link between these is not yet properly described, (2) attention-based design has a further influence on increasing task effectiveness when users are dealing with a task where they are more focused and aware, (3) attention-based design, including selective attention, can influence user decisions, especially in completing tasks that emphasize a visual-based approach, (4) attention-based design is an indispensable feature to increase user attention when sharing information on the omnipresence of social media, and (5) psychological factors such as social influences, epistemic belief and cognitive dissonance affect user decisions when sharing information.

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“…eir model considered anti-rumor information to find the reasons that users have spread rumors. To deal with the diversity and complexity of data in rumor communication, Xiao et al [28] proposed a group behavior model for rumor and anti-rumor. Considering the conflict and symbiotic relationships between rumor and anti-rumor, they integrated userinfluenced rumor with anti-rumor based on the evolutionary game theory and representation learning methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Tripartite Evolutionary Game Model for Misinformation Propagation in Social Networks

et al. 2022

Security and Communication Networks

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Misinformation has brought great challenges to the government and network media in social networks. To clarify the influences of behaviors of the network media, government, and netizen on misinformation propagation, a large number of influence parameters are proposed for the three participants. Then, a tripartite evolutionary game model for misinformation propagation is constructed. According to the proposed game model, the expected payoffs of three participants are analyzed when they adopt different strategies. The evolutionary stabilities of the game model are also analyzed theoretically. Finally, the impacts of different parameters on expected payoffs of three participants are analyzed experimentally. Meanwhile, coping strategies of three participants under different conditions are given. The experimental results show that the proposed tripartite evolutionary game model can properly describe the influence of network media, government, and netizen on misinformation propagation.

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Rumor Diffusion Model Based on Representation Learning and Anti-Rumor

Cited by 51 publications

References 37 publications

Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19

Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19

Attention-Based Design and User Decisions on Information Sharing: A Thematic Literature Review

A Novel Tripartite Evolutionary Game Model for Misinformation Propagation in Social Networks

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