What the Language You Tweet Says About Your Occupation

TL;DR

This study analyzes social media data to identify linguistic and interest-based markers that distinguish different occupations, demonstrating that language use on Twitter can predict job categories with high accuracy.

Contribution

It introduces a novel approach combining social media analysis, crowd-sourced occupation data, and personality traits to classify jobs based on language features.

Findings

Distinct language styles are associated with different occupations.

Personality traits vary significantly across job categories.

A classifier achieves high accuracy in predicting job types from tweets.

Abstract

Many aspects of people's lives are proven to be deeply connected to their jobs. In this paper, we first investigate the distinct characteristics of major occupation categories based on tweets. From multiple social media platforms, we gather several types of user information. From users' LinkedIn webpages, we learn their proficiencies. To overcome the ambiguity of self-reported information, a soft clustering approach is applied to extract occupations from crowd-sourced data. Eight job categories are extracted, including Marketing, Administrator, Start-up, Editor, Software Engineer, Public Relation, Office Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for understanding their linguistic styles, interests, and personalities. Our results suggest that people of different jobs have unique tendencies in certain language styles and interests. Our results also clearly reveal distinctive levels in terms of Big Five Traits for different jobs. Finally, a classifier is built to predict job types based on the features extracted from tweets. A high accuracy indicates a strong discrimination power of language features for job prediction task.