The acceptance and popularity of social media platforms for the dispersion and proliferation of news articles have led to the spread of questionable and untrusted information (in part) due to the ease by which misleading content can be created and shared among the communities. While prior research has attempted to automatically classify news articles and tweets as credible and non-credible. In this work, we complement such research by proposing an approach that utilizes the amalgamation of natural language processing (NLP) and deep learning techniques such as Long Short-Term Memory (LSTM). We used publicly available datasets that contain labeled news articles and tweets to validate our model’s effectiveness. The results demonstrate that the proposed model works well for both long sequence news articles and short-sequence texts such as tweets.
Vyas, Piyush; Liu, Jun; and El-Gayar, Omar, "Fake News Detection on the Web: An LSTM-based Approach" (2021). Faculty Research & Publications. 267.