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

Enthalten in
Conference on Human Factors in Computing Systems 
Start Page
1
End Page
7
Article Number
246
Citation
Conference on Human Factors in Computing Systems - Proceedings: 246 1-7 (2023-03-19)
Contribution to Conference
Conference on Human Factors in Computing Systems (CHI EA '23), 2023 
Publisher DOI
10.1145/3544549.3585583
Scopus ID
2-s2.0-85158168699
ArXiv ID
2303.09135v1
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.
Schlagworte
COVID-19
crisis communication
deleted tweets
online regrets
privacy
self-disclosure
cs.SI
cs.SI
Computer Science - Human-Computer Interaction