# What the Language You Tweet Says About Your Occupation

arXiv: https://arxiv.org/abs/1701.06233 · HTML: https://mlsanity.com/paper/1701.06233/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [Style Matters! Investigating Linguistic Style in Online Communities](https://mlsanity.com/paper/2209.13114/) (2022)
- [Cognitive-aware Short-text Understanding for Inferring Professions](https://mlsanity.com/paper/2106.07363/) (2021)
- [Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles](https://mlsanity.com/paper/2511.12010/) (2025)

## Abstract (arXiv)

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.