Job-related discourse on social media

TL;DR

This paper investigates job-related discourse on Twitter, developing a classifier to detect such messages and analyzing linguistic and temporal patterns, including mood rhythms, among individual and commercial accounts.

Contribution

It introduces a new classifier for identifying job-related tweets and provides insights into the linguistic and temporal patterns of job-related social media discourse.

Findings

Distinct daily, monthly, and hourly patterns in job-related tweets.

Unique diurnal rhythms in positive and negative moods related to jobs.

Differences in linguistic features between individual and commercial accounts.

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.