The results describe cross-camp bridging structure in three rumor cascades and connect the structure to a co-opetition reading of camp relations, which shows a persistent standoff, a hardening toward a single camp after an official correction, and a reversal with an alliance between two camps.
Abstract
Rumor events on social media generate opposing camps whose interaction structure is not captured by spreading models or content detectors. This study describes the camp and bridging structure of three rumor events on Sina Weibo, selected from confirmed cases published by the platform’s rumor-refutation channel. Each event is represented as a multilayer interaction network built from repost, comment, and mention relations. Camps are detected by modularity-based community assignment, and overlap is measured through a fractional membership distribution over communities. Three information-theoretic quantities characterize the structure. In the three events, membership entropy separates committed users from bridging users. Structure-to-stance mutual information measures the alignment between interaction communities and text stance. Cross-layer mutual information measures the consistency of camps across interaction types. A co-opetition matrix of mean edge sentiment describes cooperation within camps and competition between camps and identifies alliance structure. In the three events, membership entropy is bimodal, structure-to-stance mutual information is positive and above a permutation null, and the co-opetition matrix has positive diagonal entries. The three events show three distinct temporal patterns, namely a persistent standoff, a hardening toward a single camp after an official correction, and a reversal with an alliance between two camps. The patterns are recovered under a look-ahead-free temporal scheme. In the three events, bridging users hold higher betweenness centrality than non-bridging users. The results describe cross-camp bridging structure in three rumor cascades and connect the structure to a co-opetition reading of camp relations.
The study introduces reputational adjacency to describe a crisis-like condition in which reputational pressure on a firm arises from a competitor’s technological success rather than organisational failure.
R. K. Gupta, V. Vohra, Neerja Pande· International Journal of Bus...· 0 citations
LLM-driven agents can autonomously exchange opinions on online platforms and form communities. Such agent-operated social platforms raise a new security concern: attackers may manipulate agents to induce group polarization. Existing methods manipulate agent prompts or construct echo chambers, both of which are difficul...
Haoran Bu, Zejian Chen, Li-Tian Zhang et al.· 0 citations
This work introduces MSTRD, an explainable multi-scale temporal-structural feature framework for rumor detection on social media, and finds that temporal features are the most informative feature group, with a 2.4%-point accuracy drop when removed.
Nan Dai· Discover Artificial Intellig...· 0 citations
The results reveal that in the context of Weibo, rumor posts are more likely to have a larger average number of child posts as well as deeper levels of diffusion and stronger betweenness centrality in the context of key posts as compared to posts that are non-rumor in nature.
Tingwei Zhang· SHS Web of Conferences· 0 citations
A large-scale empirical analysis of more than 50,000 English-language starter packs and over 600,000 associated users shows that starter packs form a highly interconnected ecosystem with substantial overlap across packs that largely reflects pre-existing communities.
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