Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.
Long et al. REF proposed a model that incorporated speaker profiles into an attention based LSTM model, and used these profiles as additional input data, including speaker's party affiliation, location, title, and credic history.
Fake News Detection Through Multi-Perspective Speaker Profiles
IJCNLP
Computer Science