Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario

Chetna Kaushal; Md Abu Rumman Refat; Md Al Amin; Md Khairul Islam

doi:10.1007/978-981-16-5348-3_30

Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario

Chetna Kaushal^*
, Md Abu Rumman Refat
, Md Al Amin
, Md Khairul Islam

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The coronavirus outbreak (COVID-19) was followed by a significant number of false and unreliable content, particularly on a social media forum like Twitter, Facebook, or news portal. The Covid-19 pandemic has triggered havoc all over the planet, but the propagation of false news, termed rumor, correlates with this global fatal pandemic. The dissemination of rumors on social networking sites is quicker than the spreading of Corona Virus among people and may have heavy harmful health implications in a tragedy like COVID-19. This is compounded even during a pandemic. Therefore, such rumors may be described as a major concern for our social life. Fake news can be classified and not published on social media in order to shield users from these rumors. We tried to build a model in this paper to filter false news of Twitter. Toward this purpose, experimental evaluation on eight different machine learning models like Support Vector Machine, and different deep learning models like glove embedding or lstm on the Twitter dataset of 8560 tweets to distinguish false news regarding Covid-19 is conducted. We also employed context learning and summarization of the dataset. This research (fake news identification) allows people to get solid information and recognize those spread rumors.

Original language	English
Title of host publication	Proceedings of International Conference on Data Science and Applications, ICDSA 2021
Editors	Mukesh Saraswat, Sarbani Roy, Chandreyee Chowdhury, Amir H. Gandomi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	377-391
Number of pages	15
ISBN (Print)	9789811653476
DOIs	https://doi.org/10.1007/978-981-16-5348-3_30
State	Published - 2022
Externally published	Yes
Event	2nd International Conference on Data Science and Applications, ICDSA 2021 - Virtual, Online Duration: 10 Apr 2021 → 11 Apr 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	287
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Data Science and Applications, ICDSA 2021
City	Virtual, Online
Period	10/04/21 → 11/04/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

COVID-19
Deep learning
Fake news
Glove embedding
Machine learning

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-16-5348-3_30

Cite this

Kaushal, C., Refat, M. A. R., Amin, M. A., & Islam, M. K. (2022). Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario. In M. Saraswat, S. Roy, C. Chowdhury, & A. H. Gandomi (Eds.), Proceedings of International Conference on Data Science and Applications, ICDSA 2021 (pp. 377-391). (Lecture Notes in Networks and Systems; Vol. 287). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-5348-3_30

Kaushal, Chetna ; Refat, Md Abu Rumman ; Amin, Md Al et al. / Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario. Proceedings of International Conference on Data Science and Applications, ICDSA 2021. editor / Mukesh Saraswat ; Sarbani Roy ; Chandreyee Chowdhury ; Amir H. Gandomi. Springer Science and Business Media Deutschland GmbH, 2022. pp. 377-391 (Lecture Notes in Networks and Systems).

@inproceedings{cf204f039add4a85a2c1289cb15ed1e6,

title = "Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario",

abstract = "The coronavirus outbreak (COVID-19) was followed by a significant number of false and unreliable content, particularly on a social media forum like Twitter, Facebook, or news portal. The Covid-19 pandemic has triggered havoc all over the planet, but the propagation of false news, termed rumor, correlates with this global fatal pandemic. The dissemination of rumors on social networking sites is quicker than the spreading of Corona Virus among people and may have heavy harmful health implications in a tragedy like COVID-19. This is compounded even during a pandemic. Therefore, such rumors may be described as a major concern for our social life. Fake news can be classified and not published on social media in order to shield users from these rumors. We tried to build a model in this paper to filter false news of Twitter. Toward this purpose, experimental evaluation on eight different machine learning models like Support Vector Machine, and different deep learning models like glove embedding or lstm on the Twitter dataset of 8560 tweets to distinguish false news regarding Covid-19 is conducted. We also employed context learning and summarization of the dataset. This research (fake news identification) allows people to get solid information and recognize those spread rumors.",

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Kaushal, C, Refat, MAR, Amin, MA & Islam, MK 2022, Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario. in M Saraswat, S Roy, C Chowdhury & AH Gandomi (eds), Proceedings of International Conference on Data Science and Applications, ICDSA 2021. Lecture Notes in Networks and Systems, vol. 287, Springer Science and Business Media Deutschland GmbH, pp. 377-391, 2nd International Conference on Data Science and Applications, ICDSA 2021, Virtual, Online, 10/04/21. https://doi.org/10.1007/978-981-16-5348-3_30

Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario. / Kaushal, Chetna; Refat, Md Abu Rumman; Amin, Md Al et al.
Proceedings of International Conference on Data Science and Applications, ICDSA 2021. ed. / Mukesh Saraswat; Sarbani Roy; Chandreyee Chowdhury; Amir H. Gandomi. Springer Science and Business Media Deutschland GmbH, 2022. p. 377-391 (Lecture Notes in Networks and Systems; Vol. 287).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Kaushal C, Refat MAR, Amin MA, Islam MK. Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario. In Saraswat M, Roy S, Chowdhury C, Gandomi AH, editors, Proceedings of International Conference on Data Science and Applications, ICDSA 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 377-391. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-16-5348-3_30