What and Why? Towards Duo Explainable Fauxtography Detection Under Constrained Supervision

Fauxtography is a category of multi-modal posts that spread misleading information on various big data online social platforms that generate billions of posts on a daily basis (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 explaining fauxtography posts by identifying what specific component and why that component of a post leads to the fauxtography (i.e., duo explanations). 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 it challenging to develop fully supervised explainable solutions. To address the above challenges, we develop a D uo Ex plainable F auxtography Detection Framework under a C onstrained Supervision (DExFC) to generate duo explanations from both content and comment parts of fauxtography posts. We evaluate the DExFC by creating real-world datasets from different online social media platforms (Twitter and Reddit). The results show that DExFC not only detects the fauxtography posts more accurately than the state-of-the-art solutions but also provides well-justified explanations to its results without the full supervision.