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Recently, fake news is shared via social networks and makes wrong rumors more diffusible. This problem is serious because the wrong rumor sometimes make social damage by deceived people. Fact-checking is a solution to measure the credibility of news articles. However the process usually takes a long time and it is hard to make it before their diffusion. Automatic detection of fake news is a popular researching topic. It is confirmed that considering not only articles but also social contexts(i.e. likes, retweets, replies, comments) supports to spot fake news correctly. However, the social contexts are naturally unavailable when an article comes out, making early fake news detection by means of the social context useless. We propose a fake news detector with the ability to generate fake social contexts, aiming to detect fake news in the early stage of its diffusion where few social contexts are available. The fake context generation is based on a fake news generator model. This model is trained to generate comments using a dataset which consists of news articles and their social contexts. In addition, we also trained a classify model. This used news articles, real-posted comments, and generated comments. To measure our detector's effectiveness, we examined the performance of the generated comments for articles with real comments and generated ones by the classifying model. As a result, we conclude that considering a generated comment help detect more fake news than considering real comments only. It suggests that our proposed detector will be effective to spot fake news on social networks.
Yanagi et al. (Wed,) studied this question.