JMIR Publications

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 an infodemic on 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 114 thousand suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE. We also proposed an algorithm that efficiently cluster text messages into groups, where each group contains text messages within limited difference in content. Each group then represents a rumor and elements in each group is a message about the rumor.

Results:

114 thousand messages were separated into 937 groups with at least 10 elements. Of the 936 rumors, 44.5% (417) were related to COVID-19. By studying 3 popular false COVID-19 rumors, we identified that key authoritative figures, mostly medical personnel, were often quoted in the messages. Also, rumors resurfaced multiple times after being fact-checked, and the resurfacing pattern were influenced by major societal events and successful content alterations, such as changing whom to quote in a message.

Conclusions:

To fight infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media gives rise to 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.