Demo Data from EDBT 2026 / reviewers in the wild / expert
Tapan Khopkar
dblp:31/3011
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorTheory of computation · 1 · 1 first-author
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 50% Computational social science and digital humanities · 50% | |
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational finance and economics › electronic commerce
online marketplace |
0.1 | 1 | 2005 | Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBay · EC 2005 |
Computational social science and digital humanities › social computing
reputation system |
0.1 | 1 | 2005 | Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBay · EC 2005 |
Empirical software engineering
mining software repositories |
0.1 | 1 | 2005 | Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBay · EC 2005 |
Methods — techniques the papers use, named apart from their topics
panel data · 0.1empirical analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBayabstractAnalysis of usage history for a large panel of eBay sellers suggests that both seller and buyer behavior change in response to changes in a seller's feedback profile. Sellers are more likely to stop listing items right after receiving a negative feedback. Sellers who continue listing do not seem to improve their performance in order to salvage their reputations. Instead, sellers get more negative feedback after receiving a negative feedback. One reason is that observed negative feedback appears to be symptomatic of a temporary decline in the seller's quality, which is also reflected in other transactions around the same time. Receipt of negative feedback might also cause a decline in seller quality, but we find only weak evidence of that. Empirical evidence does support a second hypothesis, that buyers appear to be more willing to give negative feedback to sellers who have recently received other negative feedback. Tapan Khopkar, Paul Resnick |
EC | 1 |