ExFaux: A Weakly Supervised Approach to Explainable Fauxtography Detection

Fauxtography is a category of multi-modal posts that spreads misleading information on various online social platforms (e.g., Facebook, Twitter, Reddit). A fauxtography post usually consists of an image, a text description and comments from its readers. In this paper, we focus on an explainable fauxtography detection problem where the goal is to explain which a specific component of a post leads to the fauxtography decision. This problem is motivated by the limitations of current fauxtography detection solutions that only focus on the detection but ignore the important explanation aspect of their results. Two critical challenges exist in solving our problem: i) it is difficult to accurately identify the guilty component of a fauxtography post given the fact that different components of the post and their associations could all lead to the fauxtography; ii) it is expensive and time-consuming to obtain a good training set with fine-grained labels of fauxtography posts in terms of explainability, making the corresponding solutions weakly supervised in nature. To address the above challenges, we develop ExFaux, an end-to-end graph-based fauxtography explanation framework, to effectively explain which part of the post contributes to its fauxtography. We evaluate the ExFaux by creating a real-world dataset from online social media (Twitter and Reddit). The results show that ExFaux not only detects the fauxtography posts more accurately than the state-of-the-arts but also provides well-justified explanations to its results.