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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.
Background: The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put mi
An infodemic is an emerging phenomenon caused by an overabundance of information online. This proliferation of information makes it difficult for the public to distinguish trustworthy news and credible information from untrustworthy sites and non-cre
We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attrac
From global pandemics to geopolitical turmoil, leaders in logistics, product allocation, procurement and operations are facing increasing difficulty with safeguarding their organizations against supply chain vulnerabilities. It is recommended to opt
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driv