Coevolution modeling of group behavior and opinion based on public opinion perception

Li, Weimin; Guo, Chang; Deng, Zhibin; Liu, Fangfang; Wang, Jianjia; Guo, Ruiqiang; Wang, Can; Jin, Qun

doi:10.1016/j.knosys.2023.110547

Cited by 10 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each community, the candidate anti-rumor seeds has faster and broader influence propagation than other general nodes. [60][61][62] The higher the propagation capability, the larger probability that a node will be selected as a candidate antirumor seed. Inspired by three degree of separation [63,64] and community structure in networks, we define the potential local centrality (PLC) for calculating the propagation capability of nodes in each community.…”

Section: Selecting Candidate Anti-rumor Seedsmentioning

confidence: 99%

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Dong 董,

Xu 徐,

Meng 孟

2024

Chinese Phys. B

View full text Add to dashboard Cite

The virtuality and openness of online social platforms make it a hotbed for the rapid propagation of various rumors. In order to block the outbreak of rumor, one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor. The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues. Firstly, in order to simulate the dissemination of multiple types of information, we propose a competitive linear threshold model with state transition (CLTST) to describe the spreading process of rumor and anti-rumor in the same network. Subsequently, we put forward a community-based rumor blocking algorithm (CRB) based on influence maximization theory in social networks. Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes, which includes community detection, selection of candidate anti-rumor seeds and generation of anti-rumor seed set. Under CLTST model, CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance. Experimental results show that the proposed model can better reflect the process of rumor propagation, and review the propagation mechanism of rumor and anti-rumor in online social networks. Moreover, the proposed CRB algorithm has better performance in weakening the rumor dissemination ability, which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread, sensitivity analysis, seeds distribution and running time.

show abstract