@inproceedings{DiazFerreyraShahiTonyetal.2023,
  author    = {Diaz Ferreyra, Nicol{\´a}s Emilio and Shahi, Gautam Kishore and Tony, Catherine and Stieglitz, Stefan and Scandariato, Riccardo},
  title     = {Regret, delete, (do not) repeat},
  series = {Extended abstracts of the 2023 CHI conference on human factors in computing systems},
  booktitle = {Extended abstracts of the 2023 CHI conference on human factors in computing systems},
  editor    = {Schmidt, Albrecht and V{\"a}{\"a}n{\"a}nen, Kaisa and Goyal, Tesh and Kristensson, Per Ola and Peters, Anicia},
  publisher = {ACM},
  address   = {New York, NY},
  isbn      = {978-1-45039-422-2},
  doi       = {10.1145/3544549.3585583},
  pages     = {1 -- 7},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{ErmakovaFabianBenderetal.2018,
  author    = {Ermakova, Tatiana and Fabian, Benjamin and Bender, Benedict and Klimek, Kerstin},
  title     = {Web Tracking},
  series = {Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS 51)},
  booktitle = {Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS 51)},
  publisher = {HICSS Conference Office University of Hawaii at Manoa},
  address   = {Maile Way},
  issn      = {2572-6862},
  doi       = {10.24251/HICSS.2018.596},
  pages     = {4732 -- 4741},
  year      = {2018},
  abstract  = {Web tracking seems to become ubiquitous in online business and leads to increased privacy concerns of users. This paper provides an overview over the current state of the art of web-tracking research, aiming to reveal the relevance and methodologies of this research area and creates a foundation for future work. In particular, this study addresses the following research questions: What methods are followed? What results have been achieved so far? What are potential future research areas? For these goals, a structured literature review based upon an established methodological framework is conducted. The identified articles are investigated with respect to the applied research methodologies and the aspects of web tracking they emphasize.},
  language  = {en}
}