  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.
