10 citations · 12 across the 4 of their papers we have counts for
4 papers
Neighborhood-based Pooling for Population-level Label Distribution Learning
Tharindu Cyril Weerasooriya, Tong Liu, Christopher M. Homan
Supervised machine learning often requires human-annotated data. While annotator disagreement is typically interpreted as evidence of noise, population-level label distribution lea…
Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop
Tong Liu, Christopher M. Homan
We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and…
Learning from various labeling strategies for suicide-related messages on social media: An experimental study
Tong Liu, Qijin Cheng, Christopher M. Homan +1
Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media. Due in large part to the fuzzy nature of wh…
Job-related discourse on social media
Tong Liu, Christopher M. Homan, Cecilia Ovesdotter Alm +3
Working adults spend nearly one third of their daily time at their jobs. In this paper, we study job-related social media discourse from a community of users. We use both crowdsour…