Early Automatic Detection of False Information in Twitter Event Considering Occurrence Scale and Time Series

With the prevalence and rapid proliferation of SNS, dissemination of false information has become a big problem. In this paper, targeting Twitter, we propose a two-step approach for early detection of false information based on machine learning, which considers the event occurrence scale and the time series of tweets that compose the event. In Step 1, in the early stage of an event, whether it is false or true is decided if the prediction probability is high enough. In Step 2, the events whose authenticity cannot be determined in Step 1 are targeted for tracking, and their authenticity is ascertained as the tweets related to the events increase gradually. The experimental results comparing five machine learning models show that SVM is the optimal model for both steps and that our approach can achieve early detection of false information.