Regret, delete, (do not) repeat
- 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.
Author details: | Nicolás Emilio Diaz FerreyraORCiD, Gautam Kishore ShahiORCiD, Catherine TonyORCiD, Stefan StieglitzORCiDGND, Riccardo ScandariatoORCiD |
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DOI: | https://doi.org/10.1145/3544549.3585583 |
ISBN: | 978-1-45039-422-2 |
Title of parent work (English): | Extended abstracts of the 2023 CHI conference on human factors in computing systems |
Subtitle (English): | an analysis of self-cleaning practices on twitter after the outbreak of the covid-19 pandemic |
Publisher: | ACM |
Place of publishing: | New York, NY |
Editor(s): | Albrecht Schmidt, Kaisa Väänänen, Tesh Goyal, Per Ola Kristensson, Anicia Peters |
Publication type: | Conference Proceeding |
Language: | English |
Date of first publication: | 2023/04/19 |
Publication year: | 2023 |
Release date: | 2023/10/16 |
Tag: | COVID-19; crisis communication; deleted tweets; online regrets; privacy; self-disclosure |
Article number: | 246 |
Number of pages: | 7 |
First page: | 1 |
Last Page: | 7 |
Organizational units: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre |
DDC classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
Peer review: | Nicht ermittelbar |