The Evolution of Rumors on a Closed Platform during COVID-19
This work addresses misinformation spread on closed platforms during crises, offering insights for public health communication, but it is incremental as it applies existing methods to new data.
The study analyzed 114,000 suspicious messages from a closed messaging platform in Taiwan during COVID-19, using a hybrid clustering algorithm to track rumor evolution and found that misquoting authoritative figures increased false information popularity, while fact-checks were ineffective.
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.