Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference

TL;DR

This paper introduces a causal inference-based method for detecting multimodal clickbait posts by disentangling invariant and causal factors to reduce bias and improve robustness.

Contribution

It proposes a novel debiased detection approach that separates intrinsic bait intent from biased correlations using causal representation inference.

Findings

Effective in reducing bias in clickbait detection

Improves generalization across datasets

Outperforms traditional co-occurrence based methods

Abstract

This paper focuses on detecting clickbait posts on the Web. These posts often use eye-catching disinformation in mixed modalities to mislead users to click for profit. That affects the user experience and thus would be blocked by content provider. To escape detection, malicious creators use tricks to add some irrelevant non-bait content into bait posts, dressing them up as legal to fool the detector. This content often has biased relations with non-bait labels, yet traditional detectors tend to make predictions based on simple co-occurrence rather than grasping inherent factors that lead to malicious behavior. This spurious bias would easily cause misjudgments. To address this problem, we propose a new debiased method based on causal inference. We first employ a set of features in multiple modalities to characterize the posts. Considering these features are often mixed up with unknown biases, we then disentangle three kinds of latent factors from them, including the invariant factor that indicates intrinsic bait intention; the causal factor which reflects deceptive patterns in a certain scenario, and non-causal noise. By eliminating the noise that causes bias, we can use invariant and causal factors to build a robust model with good generalization ability. Experiments on three popular datasets show the effectiveness of our approach.