RT-FEND: Spark-Based Real Time FakE News Detection

Fake news is a rampant societal and organizational problem with various social media outlets further aggravating its spread. There is a pressing demand to assist people to identify misinformation from massive amount of news data in a timely manner. Detecting Fake news in a timely manner is critical for mitigating its impact. In this research, we propose a novel approach for detecting fake news in real time, RT-FEND (Real Time- FakE News Detection), which relies on distributed computing paradigm. The proposed methodology utilizes event and topic extraction techniques along with a topic- merging mechanism to process real time news data and reduce the number of topics for managing the curse of dimensionality. We report the findings from several experiments to compare RT-FEND with other systems to benchmark in different system settings. RT-FEND approach is more performance-improved and time-efficient in detecting fake news when compared to other fake news detection baselines.