Predicting the Industry of Users on Social Media

TL;DR

This paper presents a system that classifies social media users into industries with 64.3% accuracy by leveraging content and profile data, revealing industry-specific language and emotional expression patterns.

Contribution

It introduces a novel classification approach combining feature engineering and ensemble learning for industry detection on social media.

Findings

Achieved 64.3% accuracy in industry classification

Industry influences language use and emotional expression

Outperforms majority baseline significantly

Abstract

Automatic profiling of social media users is an important task for supporting a multitude of downstream applications. While a number of studies have used social media content to extract and study collective social attributes, there is a lack of substantial research that addresses the detection of a user's industry. We frame this task as classification using both feature engineering and ensemble learning. Our industry-detection system uses both posted content and profile information to detect a user's industry with 64.3% accuracy, significantly outperforming the majority baseline in a taxonomy of fourteen industry classes. Our qualitative analysis suggests that a person's industry not only affects the words used and their perceived meanings, but also the number and type of emotions being expressed.