Fake news detection based on statement conflict

摘要

The detection of fake news has become essential in recent years. This paper presents a new technique that is highly effective in identifying fake news articles. We assume a scenario where the relationship between a news article and a statement has already been classified as either agreeing or disagreeing with the statement, being uncertain about it, or being unrelated to it. Using this information, we focus on selecting the news articles that are most likely to be fake. We propose two models: the first one uses only the agree and disagree classifications; the second uses a subjective opinions based model that can also handle the uncertain cases. Our experiments on a real-world dataset (the Fake News Challenge 1 dataset) and a simulated dataset validate that both proposed models achieve state-of-the-art performance. Furthermore, we show which model to use in different scenarios to get the best performance.