Integrating Stance Detection and Factuality Checking

In this paper, we propose two models help distinguish fake news from reliable content. The first model is multi-channel LSTM-CNN with attention, where numeric features are merged with syntactic and semantic features as input. Concerning the second model, word-level and clause-level attention networks are implemented to capture the importance degrees of words in each clause and all clauses for each sentence in evidence. Other crucial features will be used in this model to guide the model in stance detection processes such as tree kernel and semantic similarities metrics. In our work, for stance detection evaluation, the PERSPECRUM data set is used for stance detection, while DLEF corpus is used for factuality checking task evaluation. Our empirical results show that merging stance detection with factuality checking helps maximize the utility of verifying the veracity of an argument. The assessment demonstrates that the accuracy improves when more focus is given on each segment (clause) rather than each sentence, so using the proposed word-level and clause-level attention networks demonstrate more effectiveness against multi-channel LSTM-CNN.