Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kenneth Lim Mingjie
dblp:194/9644
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1
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 |
Collaborative and social computing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing › social computing
online trust |
0.3 | 1 | 2017 | Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles · CSCW 2017 |
Collaborative and social computing
perceived trustworthiness |
0.3 | 1 | 2017 | Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles · CSCW 2017 |
Collaborative and social computing › sharing economy
sharing economy platforms |
0.3 | 1 | 2017 | Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles · CSCW 2017 |
Methods — techniques the papers use, named apart from their topics
topic coding · 0.3mixed-methods study · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Self-Disclosure and Perceived Trustworthiness of Airbnb Host ProfilesabstractOnline peer-to-peer platforms like Airbnb allow hosts to list a property (e.g. a house, or a room) for short-term rentals. In this work, we examine how hosts describe themselves on their Airbnb profile pages. We use a mixed-methods study to develop a categorization of the topics that hosts self-disclose in their profile descriptions, and show that these topics differ depending on the type of guest engagement expected. We also examine the perceived trustworthiness of profiles using topic-coded profiles from 1,200 hosts, showing that longer self-descriptions are perceived to be more trustworthy. Further, we show that there are common strategies (a mix of topics) hosts use in self-disclosure, and that these strategies cause differences in perceived trustworthiness scores. Finally, we show that the perceived trustworthiness score is a significant predictor of host choice--especially for shorter profiles that show more variation. The results are consistent with uncertainty reduction theory, reflect on the assertions of signaling theory, and have important design implications for sharing economy platforms, especially those facilitating online-to-offline social exchange. Xiao Ma 0010, Jeffrey T. Hancock, Kenneth Lim Mingjie, Mor Naaman |
CSCW | 3 |