  During the outbreak of the COVID-19 pandemic, many people shared their
symptoms across Online Social Networks (OSNs) like Twitter, hoping for others'
advice or moral support. Prior studies have shown that those who disclose
health-related information across OSNs often tend to regret it and delete their
publications afterwards. Hence, deleted posts containing sensitive data can be
seen as manifestations of online regrets. In this work, we present an analysis
of deleted content on Twitter during the outbreak of the COVID-19 pandemic. For
this, we collected more than 3.67 million tweets describing COVID-19 symptoms
(e.g., fever, cough, and fatigue) posted between January and April 2020. We
observed that around 24% of the tweets containing personal pronouns were
deleted either by their authors or by the platform after one year. As a
practical application of the resulting dataset, we explored its suitability for
the automatic classification of regrettable content on Twitter.
