Résumé
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. © 2023 Owner/Author.
Résumé
Information and communication technologies (ICT), including artificial intelligence, internet of things, and mobile applications, can be utilized to tackle important societal challenges, such as the ongoing COVID-19 pandemic. While they may increase societal resilience, their design, functionality, and underlying infrastructures must be resilient against disruptions caused by anthropogenic, natural and hybrid crises, emergencies, and threats. In order to research challenges, designs, and potentials of interactive technologies, the second iteration of the workshop investigates the space of mobile technologies and resilient systems for crisis response, including the application domains of cyber threat and pandemic response. © 2021 Owner/Author.