Díaz Ferreyra, NicolásNicolásDíaz FerreyraShahi, Gautam KishoreGautam KishoreShahiTony, CatherineCatherineTonyStieglitz, StefanStefanStieglitzScandariato, RiccardoRiccardoScandariato2023-06-092023-06-092023-03-19Conference on Human Factors in Computing Systems (CHI 2023)978-1-4503-9422-2http://hdl.handle.net/11420/15395During 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.enhttps://creativecommons.org/licenses/by/4.0/COVID-19crisis communicationdeleted tweetsonline regretsprivacyself-disclosurecs.SIComputer Science - Human-Computer InteractionComputer Science, Information and General Works::004: Computer SciencesConference Paperhttps://doi.org/10.15480/882.1397310.1145/3544549.358558310.15480/882.139732303.09135v1Conference on Human Factors in Computing SystemsConference Paper