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

TL;DR

This study compares how rumors and non-rumors spread on Twitter and Weibo, revealing differences in depth, speed, participant involvement, and user roles, with implications for understanding social media information dynamics.

Contribution

It provides a multi-platform analysis of rumor propagation, highlighting user roles, cascade features, and introducing the Credibility Erosion Effect in social media.

Findings

Rumors spread more deeply but slower and involve fewer participants.

Non-rumors distribute more broadly and spread faster.

Reputable users often unwittingly spread rumors, while celebrities are cautious.

Abstract

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.