Demo Data from VLDB 2026 / reviewers in the wild / expert
Jaehyeon Myung
dblp:294/1164
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-2294-9762ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% | |
| 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 |
|---|---|---|---|---|
Computational social science and digital humanities › social computing
online collaboration |
0.7 | 1 | 2023 | Longitudinal Assessment of Reference Quality on Wikipedia · WWW 2023 |
Information retrieval › web search › web information retrieval
web content quality |
0.7 | 1 | 2023 | Longitudinal Assessment of Reference Quality on Wikipedia · WWW 2023 |
Collaborative and social computing
peer production |
0.2 | 1 | 2023 | Longitudinal Assessment of Reference Quality on Wikipedia · WWW 2023 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 2.0quasi-experiment · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Longitudinal Assessment of Reference Quality on WikipediaabstractWikipedia plays a crucial role in the integrity of the Web. This work analyzes the reliability of this global encyclopedia through the lens of its references. We operationalize the notion of reference quality by defining reference need (RN), i.e., the percentage of sentences missing a citation, and reference risk (RR), i.e., the proportion of non-authoritative references. We release Citation Detective, a tool for automatically calculating the RN score, and discover that the RN score has dropped by 20 percent point in the last decade, with more than half of verifiable statements now accompanying references. The RR score has remained below 1% over the years as a result of the efforts of the community to eliminate unreliable references. We propose pairing novice and experienced editors on the same Wikipedia article as a strategy to enhance reference quality. Our quasi-experiment indicates that such a co-editing experience can result in a lasting advantage in identifying unreliable sources in future edits. As Wikipedia is frequently used as the ground truth for numerous Web applications, our findings and suggestions on its reliability can have a far-reaching impact. We discuss the possibility of other Web services adopting Wiki-style user collaboration to eliminate unreliable content. Aitolkyn Baigutanova, Jaehyeon Myung, Diego Sáez-Trumper, Ai-Jou Chou, Miriam Redi, Changwook Jung, Meeyoung Cha |
WWW | 2 |