Retrieval Enhanced Ensemble Model Framework For Rumor Detection On Micro-blogging Platforms

Abstract

Automatic rumor detection is the task of finding rumors on social networks. Previous techniques leveraged the propagation structure of tweets to detect the rumors, which makes the propagation of tweets necessary to detect rumors. However, current text-based works provide sub-optimal results as compared to propagation-based techniques. This work presents a retrieval-based framework that leverages the similar tweets from the given train set and chooses the best model from an ensemble of models to predict the test tweet label. Our proposed framework is based on transformers-based pre-trained models (PTM's). Experiments on two public data sets used in previous works, show that our framework can detect the tweets with equivalent accuracy as propagation-based techniques. The primary advantage of this work is in early rumor detection. The proposed framework can detect rumors in few minutes compared to propagation-based works, which requires a significant amount of propagation of tweets that can take hours before they can be detected.