Demo Data from SIGIR 2026 / reviewers in the wild / expert
Michael Fagan 0002
dblp:44/1622-2
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
user profiling |
0.2 | 1 | 2016 | Predicting Gaming Related Properties from Twitter Accounts · AAAI 2016 |
Computational social science and digital humanities
social media analysis |
0.2 | 1 | 2016 | Predicting Gaming Related Properties from Twitter Accounts · AAAI 2016 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Predicting Gaming Related Properties from Twitter AccountsabstractWe demonstrate a system for predicting gaming related properties from Twitter accounts. Our system predicts various traits of users based on the tweets publicly available in their profiles. Such inferred traits include degrees of tech-savviness and knowledge on computer games, actual gaming performance, preferred platform, degree of originality, humor and influence on others. Our system is based on machine learning models trained on crowd-sourced data. It allows people to select Twitter accounts of their fellow gamers, examine the trait predictions made by our system, and the main drivers of these predictions. We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset. Maria I. Gorinova 0001, Yoad Lewenberg, Yoram Bachrach, Freddie Kalaitzis, Michael Fagan 0002, Dean Carignan, Nitin Gautam |
AAAI | 5 |