Research on false information detection based on event representation
Liu Yuting 1,2,3，Ding Kun1,3，Liu Ming1,3
(1 The Sixty-Third Research Institute of National University of Defense Technology, Nanjing 210007, China;2 School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China;3 Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China)
Abstract： With the rise of the Internet, the widespread spread of false intelligence has brought difficulties to the governance of public opinion and the analysis of intelligence personnel. Accurately identifying false intelligence can help relevant departments and personnel deal with it in a targeted manner. In order to improve the efficiency of false information detection, this study proposes a false information detection method based on event representation. Firstly, the information text is collected and preprocessed. Secondly, the collected information text is transformed into word vector. Secondly the deep semantic features of the information text are captured by LSTM layer. Then the full connection layer is used to embed the high-dimensional features into the low-dimensional vector space, so as to obtain the final representation of the information text. Finally, the classification results are fed back to the relevant intelligence personnel for identification. The verification on micro-blog rumor datasets shows that the proposed method can better distinguish rumor events from non-rumor events, which proves that the proposed method can provide support for more accurate intelligence identification.
Key words : false information detection; event representation; intelligence identification
