Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

By Jing Ma, Wei Gao, Kam-Fai Wong. Annual Meeting of the Association for Computational Linguistics, 2017

642 citations recorded by the source corpus.

How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-of-the-art rumor detection models.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7e1bdd2306625a9e8f48ad1ee41254de30f316ac

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Citations in the corpus

Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, methodology): “Ma et al. (2017) used tree kernel to capture the similarity of propagation trees by counting their similar sub-structures in order to identify different types of ru-mors on Twitter.”
Predicting Factuality of Reporting and Bias of News Media Sources (2018, methodology): “, based on how users comment on the target claim (Canini et al., 2011; Castillo et al., 2011; Ma et al., 2015, 2016; Zubiaga et al., 2016; Ma et al., 2017; Dungs et al., 2018; Kochkina et al., 2018).”
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, background): “Some other works model propagation patterns as tree structures based on kernel methods (Wu et al., 2015; Ma et al., 2017).”
Combating Fake News (2019, methodology): “Ma et al. 2017 defined a tree kernel which is utilized to compute the similarity between two trees as follows.”
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, methodology): “Note that we do not employ SVM-TK on the Weibo dataset due to its exponential complexity on large datasets.”
A survey on fake news and rumour detection techniques (2019, background): “For instance, the authors in [63, 65, 75] have used a traditional top-down approach, employing keywords related to known rumours as well as extracted from rumour debunking services.”
Fighting an Infodemic: COVID-19 Fake News Dataset (2020, background): “In a similar way, several approaches have been developed to identify as-well-as limit the spread of (mis-)information (Acemoglu et al., 2010; Budak et al., 2011; Kwon et al., 2013; Ma et al., 2017; Nguyen et al., 2020; Chandra et al., 2020).”
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, methodology): “Two well-known datasets compiled by Ma et al. (2017), Twitter15 and Twitter16, are utilized.”