Demo Data from EDBT 2026 / reviewers in the wild / expert
Nika Kartvelishvili
dblp:328/0424
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0002-4497-1031ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Privacy and data protection · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection › privacy management › multi-user privacy
interdependent privacy |
0.7 | 1 | 2023 | Modeling User Characteristics Associated with Interdependent Privacy Perceptions on Social Media · ACM Trans. Comput. Hum. Interact. 2023 |
Privacy and data protection
privacy perceptions |
0.7 | 1 | 2023 | Modeling User Characteristics Associated with Interdependent Privacy Perceptions on Social Media · ACM Trans. Comput. Hum. Interact. 2023 |
Collaborative and social computing › user-generated content
photo sharing |
0.2 | 1 | 2023 | Modeling User Characteristics Associated with Interdependent Privacy Perceptions on Social Media · ACM Trans. Comput. Hum. Interact. 2023 |
Collaborative and social computing
social media |
0.2 | 1 | 2023 | Modeling User Characteristics Associated with Interdependent Privacy Perceptions on Social Media · ACM Trans. Comput. Hum. Interact. 2023 |
Methods — techniques the papers use, named apart from their topics
user study · 1.3questionnaire · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Modeling User Characteristics Associated with Interdependent Privacy Perceptions on Social Mediaabstract“Interdependent” privacy violations occur when users share private photos and information about other people in social media without permission. This research investigated user characteristics associated with interdependent privacy perceptions, by asking social media users to rate photo-based memes depicting strangers on the degree to which they were too private to share. Users also completed questionnaires measuring social media usage and personality. Separate groups rated the memes on shareability, valence, and entertainment value. Users were less likely to share memes that were rated as private, except when the meme was entertaining or when users exhibited dark triad characteristics. Users with dark triad characteristics demonstrated a heightened awareness of interdependent privacy and increased sharing of others’ photos. A model is introduced that highlights user types and characteristics that correspond to different privacy preferences: privacy preservers, ignorers, and violators. We discuss how interventions to support interdependent privacy must effectively influence diverse users. Mary Jean Amon, Aaron Necaise, Nika Kartvelishvili, Aneka Williams, Yan Solihin, Apu Kapadia |
ACM Trans. Comput. Hum. Interact. | 3 |
| 2022 | Sharenting and Children's Privacy in the United States: Parenting Style, Practices, and Perspectives on Sharing Young Children's Photos on Social MediaabstractParents posting photos and other information about children on social media is increasingly common and a recent source of controversy. We investigated characteristics that predict parental sharing behavior by collecting information from 493 parents of young children in the United States on self-reported demographics, social media activity, parenting styles, children's social media engagement, and parental sharing attitudes and behaviors. Our findings indicate that most social media active parents share photos of their children online and feel comfortable doing so without their child's permission. The strongest predictor of parental sharing frequency was general social media posting frequency, suggesting that participants do not strongly differentiate between "regular" photo-sharing activities and parental sharing. Predictors of parental sharing frequency include greater social media engagement, larger social networks with norms encouraging parental sharing, more permissive and confident parenting styles, and greater social media engagement by their children. Contrasting previous research that often highlights benefits of parental sharing, our findings point to a number of risky online behaviors associated with parental sharing not previously uncovered. Implications for children's privacy and early social media exposure are discussed, including future directions for influencing parental sharing attitudes and behaviors. Mary Jean Amon, Nika Kartvelishvili, Bennett I. Bertenthal, Kurt Hugenberg, Apu Kapadia |
Proc. ACM Hum. Comput. Interact. | 2 |