Fake News Detection via Sentiment Neutralization.

Everybody knows how fake news or disinformation can have big impact on deciding our daily lives. As people rely on social networks rather than reliable news channels to receive information, we no longer have enough protection mechanisms to help us judge between genuine and fake news. In this work, we propose a sentiment-based analysis for fake news detection. The approach leverages a few of the most recent acclaimed techniques, namely, Large Language Models and Artificial Intelligent Generated Content for sentiment analysis and the subsequent fake news detection. We have ChatGPT to help us rewrite a text for its neutralized version. By comparing the two, before and after the neutralization, we figure out their difference, such as different frequencies of sentiment word usage which can help us for fake news detection. Also, we observe an imbalanced improvement between the fake news and genuine ones. Fake news rather than genuine news in general contains more emotional words, or negative sentiment words to be specific. Taking advantage of such observation can indeed help to further improve the detection result. After all, the evaluation confirms the aforementioned statements and shows the superiority of the proposed method to other state-of-the-art methods, in datasets that own different languages.