Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection (2023)

Abstract: Due to the rapid upgrade of social platforms, most of today’s fake news is published and spread in a multi-modal form. Most existing multi-modal fake news detection methods neglect the fact that some label-specific features learned from the training set cannot generalize well to the testing set, thus inevitably suffering from the harm caused by the latent data bias. In this paper, we analyze and identify the psycholinguistic bias in the text and the bias of inferring news label based on only image features. We mitigate these biases from a causality perspective and propose a Causal intervention and Counterfactual reasoning based Debiasing framework (CCD) for multi-modal fake news detection. To achieve our goal, we first utilize causal intervention to remove the psycholinguistic bias which introduces the spurious correlations between text features and news label. And then, we apply counterfactual reasoning by imagining a counterfactual world where each news has only image features for estimating the direct effect of the image. Therefore we can eliminate the image-only bias by deducting the direct effect of the image from the total effect on labels. Extensive experiments on two real-world benchmark datasets demonstrate the effectiveness of our framework for improving multi-modal fake news detection.

## introduction fake news quietly sneaks into people's daily life, mixed with massive information, causing serious impact and harm to society. fake news often utilizes multimedia information such as text and images to mislead readers, spreading and expanding its influence. thus, it is crucial and urgent to find a way to discern multi-modal fake news. today, most existing methods train on known fake news instances expecting to capture the labelspecific features for judging the authenticity of unseen news @xcite 2021 ## method in this section, we first formulate the fake news detection task as a causal graph to clearly depict the causal effect between factors. and then we present our ccd framework that removes the psycholinguistic bias by means of causal intervention, as well as deducts the direct causal effect of image features (i.e. the image-only bias) via counterfactual reasoning. ## experiments in this section, we apply our ccd framework on three strong baseline multi-modal fake news detection models on two real-world datasets to evaluate the effectiveness of our proposed ccd framework. ## related work in this section, we review the related work including fake news detection and causal inference. ## conclusion in this work, we propose a novel causal intervention and counterfactual reasoning based debiasing framework ccd that eliminates the hidden biases in multi-modal fake news detection. we analyze and identify the psycholinguistic bias in the text as well as the image-only bias. then, we formulate the process of fake news detection as a causal graph, addressing the biases from the causality perspective. specifically, we address the psycholinguistic bias by causal intervention with backdoor adjustment, and mitigate the image-only bias using counterfactual reasoning that subtracts the direct image-only causal effect from the total causal ef-fect. experiments on two real-world benchmark datasets verify that ccd can effectively eliminate biases and improve multi-modal fake news detection.