Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter.

Social media have an enormous impact on modern life but are prone to the dissemination of false information. In several domains, such as crisis management or political communication, it is of utmost importance to detect false and to promote credible information. Although educational measures might help individuals to detect false information, the sheer volume of social big data, which sometimes need to be analysed under time-critical constraints, calls for automated and (near) real-time assessment methods. Hence, this paper reviews existing approaches before designing and evaluating three deep learning models (MLP, RNN, BERT) for real-time credibility assessment using the example of Twitter posts. While our BERT implementation achieved best results with an accuracy of up to 87.07% and an F1 score of 0.8764 when using metadata, text, and user features, MLP and RNN showed lower classification quality but better performance for real-time application. Furthermore, the paper contributes with a novel dataset for credibility assessment.