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  - 
TY  - CHAP
A1  - Kocur, Alexander
A1  - Clausen, Sünje
A1  - Hofeditz, Lennart
A1  - Brünker, Felix
A1  - Fromm, Jennifer
A1  - Stieglitz, Stefan
T1  - Fighting false information
BT  - designing a conversational agent for public sector organizations
T2  - ECIS 2023 research-in-progress papers
N2  - The digital transformation poses challenges for public sector organizations (PSOs) such as the dissemination of false information in social media which can cause uncertainty among citizens and decrease trust in the public sector. Some PSOs already successfully deploy conversational agents (CAs) to communicate with citizens and support digital service delivery. In this paper, we used design science research (DSR) to examine how CAs could be designed to assist PSOs in fighting false information online. We conducted a workshop with the municipality of Kristiansand, Norway to define objectives that a CA would have to meet for addressing the identified false information challenges. A prototypical CA was developed and evaluated in two iterations with the municipality and students from Norway. This research-in-progress paper presents findings and next steps of the DSR process. This research contributes to advancing the digital transformation of the public sector in combating false information problems.
KW  - false information
KW  - conversational agents
KW  - crisis communication
KW  - media literacy
Y1  - 2023
UR  - https://aisel.aisnet.org/ecis2023_rip/65
PB  - AIS Electronic Library (AISeL)
CY  - [Erscheinungsort nicht ermittelbar]
ER  -