FauxWard: a graph neural network approach to fauxtography detection using social media comments
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ORIGINAL ARTICLE
FauxWard: a graph neural network approach to fauxtography detection using social media comments Lanyu Shang1 · Yang Zhang1 · Daniel Zhang1 · Dong Wang1 Received: 2 April 2020 / Revised: 11 July 2020 / Accepted: 19 August 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Online social media has been a popular source for people to consume and share news content. More recently, the spread of misinformation online has caused widespread concerns. In this work, we focus on a critical task of detecting fauxtography on social media where the image and associated text together convey misleading information. Many efforts have been made to mitigate misinformation online, but we found that the fauxtography problem has not been fully addressed by existing work. Solutions focusing on detecting fake images or misinformed texts alone on social media often fail to identify the misinformation delivered together by the image and the associated text of a fauxtography post. In this paper, we develop FauxWard, a novel graph convolutional neural network framework that explicitly explores the complex information extracted from a user comment network of a social media post to effectively identify fauxtography. FauxWard is content-free in the sense that it does not analyze the visual or textual contents of the post itself, which makes it robust against sophisticated fauxtography uploaders who intentionally craft image-centric posts by editing either the text or image content. We evaluate FauxWard on two real-world datasets collected from mainstream social media platforms (i.e., Reddit and Twitter). The results show that FauxWard is both effective and efficient in identifying fauxtography posts on social media. Keywords Fauxtography · Misinformation · Social media · Fake news · Graph neural network
1 Introduction In recent years, social media has become a popular channel for people to consume and share news content (Kwak et al. 2010; Wang et al. 2018). However, the spread of misinformation on social media platforms has raised many concerns, and a significant amount of efforts have been made to reduce the diffusion of misinformation online (Zhou and Zafarani 2018; Shang et al. 2019b). For example, leading social media platforms (e.g., Facebook and Google) have stepped up to tackle and prevent the spread of fake news (Hill 2017). * Dong Wang [email protected] Lanyu Shang [email protected] Yang Zhang [email protected] Daniel Zhang [email protected] 1
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
Many solutions have been developed to combat misinformation propagation on online social media, including the analysis of news content (Volkova et al. 2017), the assessment of news source credibility (Zhang et al. 2018f), and a set of fact-checking techniques (Vo and Lee 2018). In this paper, we focus on an important but largely unsolved problem of detecting “fauxtography” where the image(s) and the associated text of a social media post conveys a questionable
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