Demo Data from VLDB 2026 / reviewers in the wild / expert
Siddhartha Sharma
dblp:269/4978
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Theory of computation · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 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 reviews |
0.4 | 1 | 2020 | Does Quality Improve with Customer Voice? Evidence from the Hotel Industry · EC 2020 |
Computational social science and digital humanities
user-generated content |
0.4 | 1 | 2020 | Does Quality Improve with Customer Voice? Evidence from the Hotel Industry · EC 2020 |
Methods — techniques the papers use, named apart from their topics
natural language processing · 0.4difference-in-differences · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Does Quality Improve with Customer Voice? Evidence from the Hotel IndustryabstractIn this paper, we empirically study whether firms improve their quality based on reviews left by their customers in a dynamic quality environment. We do so by analyzing the US hotel industry using data from two major online review platforms: TripAdvisor and Expedia. Using management response as a proxy for whether hotels read and listen to consumer reviews, and a difference-in-differences strategy, we demonstrate that hotels make improvements in quality by paying attention to the reviews they receive. Moreover, we show that these improvements are primarily made by low-rated hotels that have more room for improvement, and by chain hotels likely because of their lower operational marginal cost. To pin down the underlying mechanism, we analyze the text of reviews and responses using novel tools for natural language processing, and show that: (i) hotels that listen to customers improve on issues that are frequently mentioned in their reviews, and (ii) hotels that use canned responses are less likely to see improvements in quality. Overall, our results suggest that online user-generated reviews form a feedback mechanism through which consumers make themselves heard by businesses and contribute to changes in the quality of those businesses. Uttara Ananthakrishnan, Davide Proserpio, Siddhartha Sharma |
EC | 3 |