Job-related discourse on social media
Social and Information Networks
2015-11-17 v1
Abstract
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 crowdsourcing and local expertise to train a classifier to detect job-related messages on Twitter. Additionally, we analyze the linguistic differences in a job-related corpus of tweets between individual users vs. commercial accounts. The volumes of job-related tweets from individual users indicate that people use Twitter with distinct monthly, daily, and hourly patterns. We further show that the moods associated with jobs, positive and negative, have unique diurnal rhythms.
Cite
@article{arxiv.1511.04805,
title = {Job-related discourse on social media},
author = {Tong Liu and Christopher M. Homan and Cecilia Ovesdotter Alm and Ann Marie White and Megan C. Lytle-Flint and Henry A. Kautz},
journal= {arXiv preprint arXiv:1511.04805},
year = {2015}
}
Comments
9 pages, 7 figures, 7 tables