Enhancing Multi-Domain Fake News Detection with Ambiguous Domain Labels

In the world of news, it’s essential to separate fact from fiction. However, categorizing news into different domains can be challenging when each piece of news can relate to multiple domains simultaneously. To address this issue, researchers propose using fuzzy labels that reflect the degree of similarity between news and each domain. While fuzzy labels help identify relevant news beyond a single domain, excessive emphasis on low-relevance data may hinder the acquisition of cross-domain knowledge.
To overcome this challenge, the authors adopt a dual-teacher knowledge distillation framework. This approach eliminates spurious correlations between domains and news veracity while preserving connections between content and truthfulness. By using knowledge distillation, the learned associations are more accurate and aligned with the actual truthfulness of the news.
In summary, fuzzy labels help identify relevant news by reflecting the degree of similarity to each domain. However, excessive emphasis on low-relevance data may hinder cross-domain knowledge acquisition. To address this challenge, researchers use a dual-teacher knowledge distillation framework that eliminates spurious correlations while preserving connections between content and truthfulness. By doing so, the learned associations become more accurate and aligned with actual truthfulness of news.