Demo Data from VLDB 2026 / reviewers in the wild / expert
Shiyuan Pan
dblp:313/9506
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 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 |
Data mining · 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
computational marketing |
0.6 | 1 | 2022 | Uncovering Causal Effects of Online Short Videos on Consumer Behaviors · WSDM 2022 |
Data mining › causal inference
causal effect estimation |
0.2 | 1 | 2022 | Uncovering Causal Effects of Online Short Videos on Consumer Behaviors · WSDM 2022 |
Data mining
causal inference |
0.2 | 1 | 2022 | Uncovering Causal Effects of Online Short Videos on Consumer Behaviors · WSDM 2022 |
Methods — techniques the papers use, named apart from their topics
dictionary-based subjectivity analysis · 1.1causal decomposition · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Uncovering Causal Effects of Online Short Videos on Consumer BehaviorsabstractIn recent years, online short videos have become more popular, especially as an online advertising intermediary. To better understand their effects as advertisements, it is essential to analyze the causal relations of online short videos on consumer behaviors. Our study is based on fine-grained consumer behavior data from a world-leading e-commerce platform, i.e., Taobao.com. We first decompose the total causal effects into informative effects and persuasive effects following a common practice in the economic literature. Moreover, we extract the subjectivity scores of short videos through a dictionary-based subjectivity analysis model and evaluate the correlation between the subjectivity scores and each causal effect. The findings of this paper are as follows: First, both causal effects (i.e., informative and persuasive effects) are significant. Second, these effects have a strong correlation with the short videos' subjectivity scores. Third, the signs of these correlations vary with the prices of the products. Our results not only shed light on the research of how short videos exert influence on online consumers, but also give sellers advice on better video design and recommendation. Shengyu Zhang 0001, Nuanxin Hong, Kun Kuang 0001, Zhou Zhao 0001, Hongxia Yang, Shiyuan Pan, Jingren Zhou 0001, Fei Wu 0001 |
WSDM | 9 |