Predicting the Industry of Users on Social Media
This addresses the need for user profiling in social media applications, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of automatically detecting a user's industry from social media data, achieving 64.3% accuracy in a 14-class classification task, which significantly outperformed a baseline.
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.