RV-ML: An Effective Rumor Verification Scheme Based on Multi-Task Learning Model

摘要

Social platforms are full of rumors (i.e., unverified contents). Naturally, it is imperative but challenging to effectively determine the veracity of these rumors on popular social platforms. Previously deep learning based rumor verification schemes usually treat the issue as an independent and single task. Considering the rumor verification and stance classification are relevant tasks, we propose an effective Rumor verification scheme based on Multi-task learning Model, RV-ML, in which the shared long-short term memory (LSTM) layer for both rumor verification and stance classification can effectively deal with the sequential information for the original input, and generate macro-level virtual features, and the convolution neural network (CNN) layer uniquely designed for rumor verification task is used to mine local features from shared LSTM layer. Comparisons between our RV-ML and several typical rumor verification schemes on the real RumourEval and PHEME datasets demonstrate that our proposed scheme gains better performance for the task of rumor verification.