Emotion Infused Rumour Detection model using LSTM

Abstract

Twitter is a highly favored platform for sharing brief messages, known as tweets, read and shared among users at a rapid pace. Hence, the dissemination of information occurs quickly within the community of users in network. Twitter's unregulated environment provides a suitable platform for individuals to share and circulate unverified information; this propagation of rumours can greatly affect society. The detection of rumour accurately on Twitter from tweets is a crucial task. In this study, we suggested an Emotion Infused Rumour Detection model based on an LSTM model that employs tweet text and twenty-one distinct linguistic, user, post, and network features to classify between rumour and non-rumour tweets. The performance of the proposed Emotion Infused Detection model using LSTM is compared to two different deep learning models. The findings of the experiment demonstrate the superiority of the deep learning-based model for identifying rumours. The suggested Emotion Infused Rumour Detection model, which uses an LSTM model, earned an F1-score of 0.91 in identifying rumour and non-rumour tweets, outperforming the state-of-the-art findings. The suggested approach can lessen the influence of rumours on society, prevent loss of life and money, and increase users' confidence in social media platforms. The proposed model has the ability to promptly and accurately recognize tweets containing rumours, aiding in the prevention of the spread of misinformation.