Demo Data from EDBT 2026 / reviewers in the wild / expert
Maike M. Raphael
dblp:384/5542
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0007-2196-1889ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 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 |
Web and mobile security · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Web and mobile security
misinformation |
1.0 | 1 | 2026 | "That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
survey · 1.0focus groups · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | "That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based MisinformationabstractAs generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated content. However, it is unclear if labels reduce the risk of users falling for AI-generated misinformation. To address this research gap, we study the effect of labels on users’ perception and the implications of mislabeling, focusing on AI-generated images. We first explored users’ opinions and expectations of labels using five focus groups. Although participants were wary of practical implementations, they considered labeling helpful in identifying AI-generated images and avoiding deception. Second, we conducted a survey with 1 354 participants to assess how labels affect users’ ability to recognize misinformation. While labels reduced participants’ belief in false claims supported by AI-generated images, we found evidence of overreliance, leading to unintended side effects: Participants were more susceptible to false claims accompanied by human-made images, and were more hesitant to believe true claims illustrated with labeled AI-generated images. Sandra Höltervennhoff, Jonas Ricker, Maike M. Raphael, Charlotte Schwedes, Rebecca Weil, Asja Fischer, Thorsten Holz, Lea Schönherr, Sascha Fahl |
CHI | 3 |