Scientific Rumors Detection in Short Online Texts

The huge amount of information on social media contains much false information that has not been confirmed or has been confirmed but not known by all users, which may cause improper public attention or mislead the lives of Internet users. Among the widely spread false information, there are many rumors that require professional knowledge to be distinguished. In this paper, through in-depth analysis of the data collected from Sina Weibo official false news refuting account and questionnaires sent out by us, we propose the indicator of the relative influence of scientific rumors, which demonstrates that the rumors which requires professional scientific knowledge for identification are small but have strong propagation ability. This makes it particularly important to use technical manners to automatically detect them from a great amount of information. Targeting at this, we build up an Internet rumor dataset which contains three categories: the first is called scientific rumors which needs to be clarified by professional knowledge; the second is named as social rumors that need to be validated by authoritative media, organizations, or officials; and the third is ordinary texts. A long short term memory (LSTM)-based model is developed to detect scientific rumors and social rumors among the collected data. A benchmark on the proposed dataset is provided by comparing multiple competitors, which shows that our model is competitive to the state-of-the-arts. As far as we know, this is the first work that detects scientific rumors uses deep learning tools.