A Novel Approach for Cantonese Rumor Detection based on Deep Neural Network

Twitter is a popular social networking platform. While people enjoy the news and anecdotes on Twitter, there are also lots of rumors, which have a negative impact on users and can compromise social order. Among these rumors, many of them are written in Cantonese. At present, the research of English rumor detection is relatively comprehensive, but Cantonese rumors are rarely studied, which brings great challenges to the detection of Cantonese rumors on Twitter. Firstly, there is no available benchmark dataset of Cantonese rumors. Secondly, it is difficult to completely extract the features of rumors. Thirdly, the classical detection approaches are not effective in detecting Cantonese rumors. In this paper, we collected and annotated Cantonese rumors on Twitter and obtained a relatively complete Cantonese rumor dataset. Next, 27 statistical features, involving four categories (user, content, propagation, and comment-based), are extracted to distinguish rumors and non-rumors in Cantonese. Seven of these features are newly proposed in this paper. Then, a novel deep learning model called BLA (namely BERT-based Bi-LSTM network with Attention mechanism) is built for Cantonese rumors detection on Twitter. BLA takes advantage of both statistical and semantic features to effectively detect Cantonese rumors. The experimental results show that the BLA model outperforms other detection models in Cantonese rumor detection.