An analysis of the user occupational class through Twitter content

Abstract

Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user's occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications.

Beyond Binary Labels: Political Ideology Prediction of Twitter Users

“…Beyond its research oriented goals, user profiling has important industry applications in online marketing, personalization or large-scale audience profiling. To this end, researchers have used a wide range of types of online footprints, including video (Subramanian et al, 2013), audio (Alam and Riccardi, 2014), text (Preoţiuc-Pietro et al, 2015a), profile images (Liu et al, 2016a), social data (Van Der Heide et al, 2012;Hall et al, 2014), social networks (Perozzi and Skiena, 2015;Rout et al, 2013), payment data (Wang et al, 2016) and endorsements .…”

Multitask Learning for Mental Health Conditions with Limited Social Media Data

“…Written text carries implicit information about the author, a relationship that has been exploited in natural language processing (NLP) to predict author characteristics, such as age (Goswami et al, 2009;Rosenthal and McKeown, 2011;Nguyen et al, 2011;Nguyen et al, 2014), gender (Sarawgi et al, 2011;Ciot et al, 2013;Liu and Ruths, 2013;Alowibdi et al, 2013;Volkova et al, 2015;Hovy, 2015), personality and stance (Schwartz et al, 2013b;Schwartz et al, 2013a;Volkova et al, 2014;Plank and Hovy, 2015;Preoţiuc-Pietro et al, 2015), or occupation (Preotiuc-Pietro et al, 2015a;Preoţiuc-Pietro et al, 2015b). The same signal has also been effectively used to predict mental health conditions, such as depression (Coppersmith et al, 2015b;Schwartz et al, 2014), suicidal ideation (Coppersmith et al, 2016;Huang et al, 2015), schizophrenia (Mitchell et al, 2015) or post-traumatic stress disorder (PTSD) (Pedersen, 2015), often more accurately than by traditional diagnoses.…”

Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

“…We choose this method due to interpretability of results, similar to recent work on occupational class classification (Preotiuc-Pietro et al, 2015;Lukasik et al, 2015).…”