Regret, Delete, (Do Not) Repeat : An Analysis of Self-Cleaning Practices on Twitter After the Outbreak of the COVID-19 Pandemic

Autor:	Nicolás Emilio Diaz Ferreyra, Gautam Kishore Shahi, Catherine Tony, Stefan Stieglitz, Riccardo Scandariato
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Social and Information Networks (cs.SI) FOS: Computer and information sciences Computer Science - Human-Computer Interaction Medizin Computer Science - Social and Information Networks Human-Computer Interaction (cs.HC)
Popis:	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. Comment: Accepted at CHI '23 Late Breaking Work (LBW)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86d5b39c82a9aa27ccf5251c615bddcf https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85158168699 Zobrazit plný text záznamu