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

arXiv: https://arxiv.org/abs/2104.13816 · HTML: https://mlsanity.com/paper/2104.13816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach](https://mlsanity.com/paper/2004.11695/) (2020)
- [Assessing the risks of "infodemics" in response to COVID-19 epidemics](https://mlsanity.com/paper/2004.03997/) (2020)
- [Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak](https://mlsanity.com/paper/2004.14484/) (2020)

## Built on by (most cited first, 1 of 1)

- [The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents](https://mlsanity.com/paper/2410.19064/) (2024)

## Abstract (arXiv)

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.