Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

Abstract

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-ofthe-art rumor detection models.

Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection

“…We evaluate the proposed model on three real-world data collections: Weibo [10], Twitter15 [7] and Twitter16 [7], which were originally collected from the most popular social media website in China and the U.S. respectively.…”

“…Existing studies on automatically detecting rumors mainly focused on designing effective features from various information sources, including text content [1]- [3], publisher's profiles [1], [4] and propagation patterns [5]- [7]. However, these feature-based methods are extremely time-consuming, biased, and labor-intensive.…”

Contrastive Language Adaptation for Cross-Lingual Stance Detection

“…The rise of social media has enabled the phenomenon of "fake news," which could target specific individuals and can be used for deceptive purposes (Lazer et al, 2018;Vosoughi et al, 2018). As manual fact-checking is a time-consuming and tedious process, computational approaches have been proposed as a possible alternative (Popat et al, 2017;Wang, 2017;Mihaylova et al, 2018), based on information sources such as social media (Ma et al, 2017), Wikipedia (Thorne et al, 2018), and knowledge bases (Huynh and Papotti, 2018). Fact-checking is a multi-step process (Vlachos and Riedel, 2014): (i) checking the reliability of media sources, (ii) retrieving potentially relevant documents from reliable sources as evidence for each target claim, (iii) predicting the stance of each document with respect to the target claim, and finally (iv) making a decision based on the stances from (iii) for all documents from (ii).…”

Homogeneity-Based Transmissive Process to Model True and False News in Social Networks

“…Since both the content modeling and homogeneity discovery part of our modeling fall into the Bayesian nonparametric framework, the posterior inference of our model can be done using the variational Bayes methods [23]. We train our model on a real-world Twitter dataset [36,37,39] that consists of user posting and sharing with true and false news stories. Our main contributions and the findings through the experiments are as follows:…”

“…By incorporating the label information of the news stories, the supervised formulation of our model performs the classification task of predicting the labels of the news stories. There are feature-based methods that leverage text, social network, temporal traces and propagation models to classify true and false news in supervised fashion [39,50,20,29,72,70,31,32]. Also, another line of research focuses on devising algorithms that mitigate false news and their diffusion in social networks [9,64,48,27,17].…”