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Attention-based LSTM network for rumor veracity estimation of tweets
Singh, J.P. Kumar, A. Rana, Nripendra P. Dwivedi, Y.K.
Singh, J.P.
Kumar, A.
Rana, Nripendra P.
Dwivedi, Y.K.
Publication Date
2022
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© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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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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Citation
Singh JP, Kumar A, Rana NP et al (2022) Attention-based LSTM network for rumor veracity estimation of tweets. Information Systems Frontiers. 24: 459-474.
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