TY  - CHAP
A1  - Diaz Ferreyra, Nicolás Emilio
A1  - Shahi, Gautam Kishore
A1  - Tony, Catherine
A1  - Stieglitz, Stefan
A1  - Scandariato, Riccardo
ED  - Schmidt, Albrecht
ED  - Väänänen, Kaisa
ED  - Goyal, Tesh
ED  - Kristensson, Per Ola
ED  - Peters, Anicia
T1  - Regret, delete, (do not) repeat
BT  - an analysis of self-cleaning practices on twitter after the outbreak of the covid-19 pandemic
T2  - Extended abstracts of the 2023 CHI conference on human factors in computing systems
N2  - 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.
KW  - privacy
KW  - self-disclosure
KW  - online regrets
KW  - deleted tweets
KW  - crisis communication
KW  - COVID-19
Y1  - 2023
SN  - 978-1-45039-422-2
U6  - https://doi.org/10.1145/3544549.3585583
SP  - 1
EP  - 7
PB  - ACM
CY  - New York, NY
ER  -