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Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics

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arxiv 2401.17840 v1 pith:LUZPERJX submitted 2024-01-31 cs.SI

classification cs.SI
keywords platformspropagationrumorsspreaduserscascadesdiscoverdynamics
verification ladder T0 review T1 audit T2 compute T3 formal

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Studying information propagation dynamics in social media can elucidate user behaviors and patterns. However, previous research often focuses on single platforms and fails to differentiate between the nuanced roles of source users and other participants in cascades. To address these limitations, we analyze propagation cascades on Twitter and Weibo combined with a crawled dataset of nearly one million users with authentic attributes. Our preliminary findings from multiple platforms robustly indicate that rumors tend to spread more deeply, while non-rumors distribute more broadly. Interestingly, we discover that the spread of rumors is slower, persists longer, and, in most cases, involves fewer participants than that of non-rumors. And an undiscovered highlight is that reputable active users, termed `onlookers', inadvertently or unwittingly spread rumors due to their extensive online interactions and the allure of sensational fake news. Conversely, celebrities exhibit caution, mindful of releasing unverified information. Additionally, we identify cascade features aligning with exponential patterns, highlight the Credibility Erosion Effect (CEE) phenomenon in the propagation process, and discover the different contents and policies between the two platforms. Our findings enhance current understanding and provide a valuable statistical analysis for future research.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference

    cs.MM 2025-01 reject novelty 6.0 of 10

    HML is a fake news detection framework that infers latent social networks with a Hawkes process and combines self-supervised multimodal learning with a graph transformer, claiming SOTA results on FakeSV and FVC.

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