The Evolution of Rumors on a Closed Platform during COVID-19

TL;DR

This study analyzes the evolution and spread of COVID-19 rumors on a closed messaging platform in Taiwan, revealing how misinformation adapts over time and the limited impact of fact-checking.

Contribution

Introduces a hybrid clustering algorithm for analyzing content and narratives of rumors, and provides insights into misinformation dynamics during COVID-19.

Findings

Key figures are often misquoted in false information.

Fact-checking has limited effectiveness in curbing misinformation.

Major societal events influence rumor popularity.

Abstract

In this work we looked into a dataset of 114 thousands of suspicious messages collected from the most popular closed messaging platform in Taiwan between January and July, 2020. We proposed an hybrid algorithm that could efficiently cluster a large number of text messages according their topics and narratives. That is, we obtained groups of messages that are within a limited content alterations within each other. By employing the algorithm to the dataset, we were able to look at the content alterations and the temporal dynamics of each particular rumor over time. With qualitative case studies of three COVID-19 related rumors, we have found that key authoritative figures were often misquoted in false information. It was an effective measure to increase the popularity of one false information. In addition, fact-check was not effective in stopping misinformation from getting attention. In fact, the popularity of one false information was often more influenced by major societal events and effective content alterations.