Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from VLDB 2026 / reviewers in the wild / expert

Anderson Garron

dblp:136/9205 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 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
Medical and health informatics · 67% Computational social science and digital humanities · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › mental health informatics
depression prediction		 0.212013
Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students · EMNLP 2013
Medical and health informatics › mental health informatics
mental health prediction		 0.212013
Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students · EMNLP 2013
Computational social science and digital humanities › social computing
personality prediction		 0.212013
Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students · EMNLP 2013

Methods — techniques the papers use, named apart from their topics

topic modeling · 0.2latent dirichlet allocation · 0.2LIWC · 0.2
YearPublicationVenuePosition
2013 Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students
abstract
We investigate the value-add of topic modeling in text analysis for depression, and for neuroticism as a strongly associated personality measure.Using Pennebaker's Linguistic Inquiry and Word Count (LIWC) lexicon to provide baseline features, we show that straightforward topic modeling using Latent Dirichlet Allocation (LDA) yields interpretable, psychologically relevant "themes" that add value in prediction of clinical assessments.
Philip Resnik, Anderson Garron, Rebecca Resnik
 EMNLP2