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Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2022

01.12.2022 | Original Article

A deep dive into COVID-19-related messages on WhatsApp in Pakistan

verfasst von: R. Tallal Javed, Muhammad Usama, Waleed Iqbal, Junaid Qadir, Gareth Tyson, Ignacio Castro, Kiran Garimella

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2022

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Abstract

The spread of COVID-19 and the lockdowns that followed led to an increase in activity on online social networks. This has resulted in users sharing unfiltered and unreliable information on social networks like WhatsApp, Twitter, Facebook, etc. In this work, we give an extended overview of how Pakistan’s population used public WhatsApp groups for sharing information related to the pandemic. Our work is based on a major effort to annotate thousands of text and image-based messages. We explore how information propagates across WhatsApp and the user behavior around it. Specifically, we look at political polarization and its impact on how users from different political parties shared COVID-19-related content. We also try to understand information dissemination across different social networks—Twitter and WhatsApp—in Pakistan and find that there is no significant bot involvement in spreading misinformation about the pandemic.
Vorheriger Artikel GANBOT: a GAN-based framework for social bot detection
Nächster Artikel Decision tree Thompson sampling for mining hidden populations through attributed search

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Fußnoten
1
In this study, we only focus on text and images and leave the analysis over video content for future work.
 
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Metadaten
Titel
A deep dive into COVID-19-related messages on WhatsApp in Pakistan
verfasst von
R. Tallal Javed
Muhammad Usama
Waleed Iqbal
Junaid Qadir
Gareth Tyson
Ignacio Castro
Kiran Garimella
Publikationsdatum
01.12.2022
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2022
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-021-00833-0

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