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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: May 21, 2021
Open Peer Review Period: May 17, 2021 - Jul 12, 2021
Date Accepted: Sep 10, 2021
Date Submitted to PubMed: Nov 24, 2021
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

Wang AWY, Lan JY, Wang MH, Yu C

The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

JMIR Med Inform 2021;9(11):e30467

DOI: 10.2196/30467

PMID: 34623954

PMCID: 8612313

The evolution of rumors on a closed platform during COVID-19

  • Andrea Wen-Yi Wang; 
  • Jo-Yu Lan; 
  • Ming-Hung Wang; 
  • Chihhao Yu

ABSTRACT

Background:

In 2020, the COVID-19 pandemic put the world in crisis on both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020.

Objective:

We want to study the propagation patterns and textual transformation of COVID-19 related rumors on a closed-platform.

Methods:

We obtained a dataset of suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE, between January and July 2020. We proposed a Classification-based Clustering algorithm that efficiently clusters messages into groups, where each group represents a rumor. For ease of understanding, a group is referred to as a “rumor group”. Messages in a rumor group could be identical or within limited textual differences with one another. Therefore, each message in a rumor group is a form of the rumor.

Results:

A total of 936 rumor groups with at least 10 messages were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of 396 COVID-related rumors, 134 (33.8%) had been fact-checked by IFCN-certified agencies to be false or misleading. By studying the Chinese characters or idioms used in messages, it is statistically significant that COVID-related messages were more likely to have originated from non-Taiwanese people. By the qualitative investigations of 3 most popular false COVID-19 rumors, we identified that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or text transformations.

Conclusions:

To fight infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media gives rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study rumor propagations and the interactions with society. Therefore, we must put more effort in the areas.


 Citation

Please cite as:

Wang AWY, Lan JY, Wang MH, Yu C

The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

JMIR Med Inform 2021;9(11):e30467

DOI: 10.2196/30467

PMID: 34623954

PMCID: 8612313

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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