Demo Data from EDBT 2026 / reviewers in the wild / expert
Sami Foell
dblp:358/7064
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0000-0509-5860ORCID · 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.
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 50% Collaborative and social computing · 50% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction
AI-mediated communication |
0.8 | 1 | 2024 | From Text to Self: Users' Perception of AIMC Tools on Interpersonal Communication and Self · CHI 2024 |
Collaborative and social computing
interpersonal communication |
0.8 | 1 | 2024 | From Text to Self: Users' Perception of AIMC Tools on Interpersonal Communication and Self · CHI 2024 |
Natural language and speech › Language models and text generation
large language model |
0.2 | 1 | 2024 | From Text to Self: Users' Perception of AIMC Tools on Interpersonal Communication and Self · CHI 2024 |
Methods — techniques the papers use, named apart from their topics
interviews · 1.5diary study · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | From Text to Self: Users' Perception of AIMC Tools on Interpersonal Communication and SelfabstractIn the rapidly evolving landscape of AI-mediated communication (AIMC), tools powered by Large Language Models (LLMs) are becoming integral to interpersonal communication. Employing a mixed-methods approach, we conducted a one-week diary and interview study to explore users’ perceptions of these tools’ ability to: 1) support interpersonal communication in the short-term, and 2) lead to potential long-term effects. Our findings indicate that participants view AIMC support favorably, citing benefits such as increased communication confidence, finding precise language to express their thoughts, and navigating linguistic and cultural barriers. However, our findings also show current limitations of AIMC tools, including verbosity, unnatural responses, and excessive emotional intensity. These shortcomings are further exacerbated by user concerns about inauthenticity and potential overreliance on the technology. We identify four key communication spaces delineated by communication stakes (high or low) and relationship dynamics (formal or informal) that differentially predict users’ attitudes toward AIMC tools. Specifically, participants report that these tools are more suitable for communicating in formal relationships than informal ones and more beneficial in high-stakes than low-stakes communication. Sami Foell, Xuhai Xu, Alexis Hiniker |
CHI | 2 |