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Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
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Yogesh Dwivedi
Information Systems Frontiers, Volume: 24, Issue: 2, Pages: 459 - 474
Swansea University Authors:
Nripendra Rana , Yogesh Dwivedi
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DOI (Published version): 10.1007/s10796-020-10040-5
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 debun...
| Published in: | Information Systems Frontiers |
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| ISSN: | 1387-3326 1572-9419 |
| Published: |
Springer Science and Business Media LLC
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa54558 |
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| 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. |
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| Keywords: |
Rumor; Twitter; Deep learning; Machine learning |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
2 |
| Start Page: |
459 |
| End Page: |
474 |