Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets

Jyoti Prakash Singh (), Abhinav Kumar (), Nripendra P. Rana () and Yogesh K. Dwivedi ()
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Jyoti Prakash Singh: National Institute of Technology Patna
Abhinav Kumar: National Institute of Technology Patna
Nripendra P. Rana: University of Bradford
Yogesh K. Dwivedi: Swansea University

Information Systems Frontiers, 2022, vol. 24, issue 2, No 7, 459-474

Abstract: Abstract Twitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.

Keywords: Rumor; Twitter; Deep learning; Machine learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-020-10040-5

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