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Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets

Jyoti Prakash Singh, Abhinav Kumar, Nripendra Rana Orcid Logo, Yogesh Dwivedi Orcid Logo

Information Systems Frontiers, Volume: 24, Issue: 2, Pages: 459 - 474

Swansea University Authors: Nripendra Rana Orcid Logo, Yogesh Dwivedi Orcid Logo

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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...

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Published in: Information Systems Frontiers
ISSN: 1387-3326 1572-9419
Published: Springer Science and Business Media LLC 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa54558
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first_indexed 2020-06-28T23:04:37Z
last_indexed 2023-01-11T14:32:39Z
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spelling 2022-07-20T16:23:22.0438701 v2 54558 2020-06-28 Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets b00e18aa519cd578d4b242e376e70331 0000-0003-1105-8729 Nripendra Rana Nripendra Rana true false d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2020-06-28 BBU 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. Journal Article Information Systems Frontiers 24 2 459 474 Springer Science and Business Media LLC 1387-3326 1572-9419 Rumor; Twitter; Deep learning; Machine learning 1 4 2022 2022-04-01 10.1007/s10796-020-10040-5 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2022-07-20T16:23:22.0438701 2020-06-28T23:55:12.7499879 Faculty of Humanities and Social Sciences School of Management - Business Management Jyoti Prakash Singh 1 Abhinav Kumar 2 Nripendra Rana 0000-0003-1105-8729 3 Yogesh Dwivedi 0000-0002-5547-9990 4 54558__17932__4c681259b9454aad85ac4763d541370b.pdf 54558.pdf 2020-08-16T13:26:08.0659676 Output 1506265 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution 4.0 International License (CC-BY). true eng http://creativecommonshorg/licenses/by/4.0/
title Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
spellingShingle Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
Nripendra Rana
Yogesh Dwivedi
title_short Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
title_full Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
title_fullStr Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
title_full_unstemmed Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
title_sort Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
author_id_str_mv b00e18aa519cd578d4b242e376e70331
d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv b00e18aa519cd578d4b242e376e70331_***_Nripendra Rana
d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Nripendra Rana
Yogesh Dwivedi
author2 Jyoti Prakash Singh
Abhinav Kumar
Nripendra Rana
Yogesh Dwivedi
format Journal article
container_title Information Systems Frontiers
container_volume 24
container_issue 2
container_start_page 459
publishDate 2022
institution Swansea University
issn 1387-3326
1572-9419
doi_str_mv 10.1007/s10796-020-10040-5
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2022-04-01T04:08:10Z
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score 11.037581

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