Modelling User Influence and Rumor Propagation on Twitter using Hawkes Processes

Nie, Huanhuan; Zhang, Xiuzhen; Li, Minyi; Dolgun, Anıl; Baglin, James

doi:10.1109/dsaa49011.2020.00090

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…News in social media is directly exposed to the public, and its spread involves many social media users who interact with the news, forming unique propagation patterns. Studies have found that fake news often spreads differently from real news, e.g., news published by official sources vs. rumours disseminated through social media [64], [65]. Researchers thus have proposed propagation-based methods aiming to exploit such differences to identify fake news.…”

Section: B External Knowledge-based Methodsmentioning

confidence: 99%

Heterogeneous graph neural networks with denoising for graph embeddings

Dong

Zhang

Pang

et al. 2022

Knowledge-Based Systems

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Section: B External Knowledge-based Methodsmentioning

confidence: 99%

Heterogeneous graph neural networks with denoising for graph embeddings

Dong

Zhang

Pang

et al. 2022

Knowledge-Based Systems

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“…A two-stage Time-Dependent Hawkes Process (TiDeH) model was built for characterizing the process of fake news dissemination on Twitter before/after fact-checking occurs [16], which considered tweet posts as the user behaviors over time. A related study applied a Multivariate Hawkes Process incorporating user networks to measures the influence rate between online users to model the process of rumor propagation on Twitter [17]. A classification model was developed for user stances of rumors on Twitter using Hawkes Processes.…”

Section: B Hawkes Point Processes Modeling On Information Disseminati...mentioning

confidence: 99%

Simulating Fake News Dissemination on Twitter with Multivariate Hawkes Processes

Jiang

Porter

2022

2022 IEEE International Conference on Big Data (Big Data)

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Fake news has emerged as a pervasive problem within Online Social Networks, leading to a surge of research interest in this area. Understanding the dissemination mechanisms of fake news is crucial in comprehending the propagation of disinformation/misinformation and its impact on users in Online Social Networks. This knowledge can facilitate the development of interventions to curtail the spread of false information and inform affected users to remain vigilant against fraudulent/malicious content. In this paper, we specifically target the Twitter platform and propose a Multivariate Hawkes Point Processes model that incorporates essential factors such as user networks, response tweet types, and user stances as model parameters. Our objective is to investigate and quantify their influence on the dissemination process of fake news. We derive parameter estimation expressions using an Expectation Maximization algorithm and validate them on a simulated dataset. Furthermore, we conduct a case study using a real dataset of fake news collected from Twitter to explore the impact of user stances and tweet types on dissemination patterns. This analysis provides valuable insights into how users are influenced by or influence the dissemination process of disinformation/misinformation, and demonstrates how our model can aid in intervening in this process.

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Fake News Detection Through Temporally Evolving User Interactions

Gong

Sinnott

et al. 2023

Lecture Notes in Computer Science

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Modelling User Influence and Rumor Propagation on Twitter using Hawkes Processes

Cited by 3 publications

References 17 publications

Heterogeneous graph neural networks with denoising for graph embeddings

Heterogeneous graph neural networks with denoising for graph embeddings

Simulating Fake News Dissemination on Twitter with Multivariate Hawkes Processes

Fake News Detection Through Temporally Evolving User Interactions

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