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    Attention-based LSTM network for rumor veracity estimation of tweets

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    Rana_Information_Systems_Frontiers_Aug_2020.pdf (1.436Mb)
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    Publication date
    2022
    Author
    Singh, J.P.
    Kumar, A.
    Rana, Nripendra P.
    Dwivedi, Y.K.
    Keyword
    Rumor
    Rumour
    Twitter
    Deep learning
    Machine learning
    Rights
    © 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/.
    Peer-Reviewed
    Yes
    
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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.
    URI
    http://hdl.handle.net/10454/17942
    Version
    Published version
    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.
    Link to publisher’s version
    https://doi.org/10.1007/s10796-020-10040-5
    Type
    Article
    Collections
    Management and Law Publications

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