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

arXiv: https://arxiv.org/abs/2410.07673 · HTML: https://mlsanity.com/paper/2410.07673/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [All in One: Exploring Unified Video-Language Pre-training](https://mlsanity.com/paper/2203.07303/) (2022)
- [Causality Inspired Representation Learning for Domain Generalization](https://mlsanity.com/paper/2203.14237/) (2022)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue](https://mlsanity.com/paper/2009.09945/) (2020)
- [The Role of User Profile for Fake News Detection](https://mlsanity.com/paper/1904.13355/) (2019)
- [Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective](https://mlsanity.com/paper/2111.14820/) (2021)
- [Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation](https://mlsanity.com/paper/1903.09196/) (2019)
- [Aesthetic-based Clothing Recommendation](https://mlsanity.com/paper/1809.05822/) (2018)
- [Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting](https://mlsanity.com/paper/2302.08635/) (2023)
- [Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?](https://mlsanity.com/paper/1703.09400/) (2017)
- [We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!](https://mlsanity.com/paper/1612.01340/) (2016)
- [Fake News Detection as Natural Language Inference](https://mlsanity.com/paper/1907.07347/) (2019)
- [Mining Dual Emotion for Fake News Detection](https://mlsanity.com/paper/1903.01728/) (2019)
- [E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation](https://mlsanity.com/paper/2112.07106/) (2021)
- [Clickbait in YouTube Prevention, Detection and Analysis of the Bait using Ensemble Learning](https://mlsanity.com/paper/2112.08611/) (2021)

## Built on by (most cited first, 6 of 6)

- [Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation](https://mlsanity.com/paper/2504.02458/) (2025)
- [What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews](https://mlsanity.com/paper/2601.05563/) (2026)
- [Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models](https://mlsanity.com/paper/2602.18171/) (2026)
- [BanClickThumb: A Multimodal Dataset and Transformer Fusion Benchmarks for Clickbait Detection in Bengali YouTube Videos](https://mlsanity.com/paper/2607.17182/) (2026)
- [ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models](https://mlsanity.com/paper/2607.20463/) (2026)
- [Summarize, Judge, Refine: Decoupled Content Understanding and Policy Learning for Multimodal Content Moderation](https://mlsanity.com/paper/2609.22094/) (2026)

## Abstract (arXiv)

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.