Demo Data from VLDB 2026 / reviewers in the wild / expert
Foram Patel
dblp:371/7416
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-5977-3188ORCID · reported
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.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Web and social media mining · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
text classification |
0.8 | 1 | 2024 | Wildfire: A Twitter Social Sensing Platform for Layperson · WSDM 2024 |
Natural language and speech › Information extraction and text analysis › text classification › social media text classification
tweet classification |
0.8 | 1 | 2024 | Wildfire: A Twitter Social Sensing Platform for Layperson · WSDM 2024 |
Web and social media mining › social media analysis
social sensing |
0.8 | 1 | 2024 | Wildfire: A Twitter Social Sensing Platform for Layperson · WSDM 2024 |
Methods — techniques the papers use, named apart from their topics
topic generation · 2.3heuristic graph exploration · 2.3entity recognition · 2.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Wildfire: A Twitter Social Sensing Platform for LaypersonabstractWe present Wildfire, an innovative social sensing platform designed for laypersons. The goal is to support users in conducting social sensing tasks using Twitter data without programming and data analytics skills. Existing open-source and commercial social sensing tools only support data collection using simple keyword-based or account-based search. On the contrary, Wildfire employs a heuristic graph exploration method to selectively expand the collected tweet-account graph in order to further retrieve more task-relevant tweets and accounts. This approach allows for the collection of data to support complex social sensing tasks that cannot be met with a simple keyword search. In addition, Wildfire provides a range of analytic tools, such as text classification, topic generation, and entity recognition, which can be crucial for tasks such as trend analysis. The platform also provides a web-based user interface for creating and monitoring tasks, exploring collected data, and performing analytics. Zhengyuan Zhu, Foram Patel, Josue Caraballo, Patrick Hennecke, Chengkai Li 0001 |
WSDM | 4 |