Summary of Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference, By Jianxing Yu et al.
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference
by Jianxing Yu, Shiqi Wang, Han Yin, Zhenlong Sun, Ruobing Xie, Bo Zhang, Yanghui Rao
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of detecting clickbait posts online by developing a novel method based on causal inference to overcome the limitations of traditional detectors. Clickbait posts often use mixed modalities and disinformation to mislead users into clicking, which can be detrimental to user experience. To evade detection, malicious creators inject irrelevant content into bait posts, making it challenging for detectors to identify them accurately. Traditional detectors rely on simple co-occurrence rather than understanding the underlying factors driving malicious behavior, leading to biased predictions. The proposed debiased method first extracts features from multiple modalities and then disentangles three types of latent factors: invariant (indicating bait intention), causal (reflecting deceptive patterns), and non-causal noise. By removing this noise, the model can be built using invariant and causal factors, achieving robustness and good generalization ability. The approach is evaluated on three popular datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to identify clickbait posts online that try to trick people into clicking. These fake posts often use eye-catching information in different forms to get our attention. To make it harder for us to spot them, the bad guys add some extra content that looks legitimate. This makes it difficult for computers to accurately detect these posts. The paper proposes a new way to do this by using special techniques to separate out the good and bad parts of the post. They show that their approach is effective in spotting clickbait posts on three big datasets. |
Keywords
» Artificial intelligence » Attention » Generalization » Inference
