The characteristics of rumor spreaders on Twitter: A quantitative analysis on real data

The Rumor Categorizer: An open-source software for analyzing rumor posts on Twitter

“…Even though these variables and the way they influence diffusion have not been discovered entirely yet, but any pattern resulting from their impact at the aggregate level would be of interest. One of these patterns is the wavelike form of spreading, which has been proved to have a direct influence on the lifespan of rumors on social media [14]. Indeed, the investigation of rumor spreading on Twitter in a competitive process has shown that rumors last longer if the delay in their detection increase [15].…”

“…This means the study of wavelike patterns of diffusion aids the identification of the origins. In [14], two general forms of these wavelike patterns in rumor spreading are presented. These patterns, which are named after two oceanic waves with similar shapes; Shipstern, and Teahupoo, are illustrated in Fig.…”

“…Moreover, even though the wavelike patterns of diffusion can be seen in almost every social media, but their interpretations may differ. The introduced macro patterns in this manuscript are based on proven results from Twitter-based research [14] and may not have the same interpretations in other social media.…”

Tripartite Evolutionary Game Analysis for Rumor Spreading on Weibo Based on MA-PT

“…Kim et al used the social network analysis method to study the relationship between network information dissemination and public opinion, and the results showed that the polarization of Weibo views was correlated with the form of political consciousness [54]. Bodaghi et al found that users with lower followers were more likely to start rumors, while users with higher followers were more likely to keep rumors circulating [55]. Ma et al pointed out that Internet users play an important role in the communication of rumor.…”

The fake news graph analyzer: An open-source software for characterizing spreaders in large diffusion graphs

“…Furthermore, findings of different categories' centralities and interconnections of spreaders depict a practical perspective that can guide practitioners to build better strategies against fake news spreading on social media. Moreover, the investigations on collective behaviors such as the wave-like forms of diffusion and their associations with micro behaviors are still in their infancy [9][10][11]. In fact, research on the impact of cyberpsychology [12] at the aggregate level remains open to yield more profound insights into the diffusion process of fake news in social media.…”