Citation

Marengo D, Azucar D, Giannotta F, Basile V, Settanni M. Heliyon 2019; 5(10): e02523.

Affiliation

Department of Psychology, University of Turin, Turin, Italy.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.heliyon.2019.e02523

PMID

31667380

PMCID

PMC6812202

Abstract

Recent literature suggests that variations in both formal and content aspects of texts shared on social media tend to reflect user-level differences in demographic, psychosocial, and behavioral characteristics. In the present study, we examined associations between language use on Facebook and problematic alcohol use. We collected texts shared on Facebook by a sample of 296 adult social media users (66.9% females; mean age = 28.44 years (SD = 7.38)). Texts were mined using the closed-vocabulary approach based on the Linguistic Inquiry Word Count (LIWC) semantic dictionary, and an open-vocabulary approach performed via Latent Dirichlet Allocation (LDA). Then, we examined associations between emerging textual features and alcohol-drinking scores as assessed using the AUDIT-C questionnaire. As a final aim, we employed the Random Forest machine-learning algorithm to determine and compare the predictive accuracy of closed- and open-vocabulary features over users' AUDIT-C scores. We found use of words about family, school, and positive feelings and emotions to be negatively associated with alcohol use and problematic drinking, while words suggesting interest in sport events, politics and economics, nightlife, and use of coarse language were more frequent among problematic drinkers.

RESULTS coming from LIWC and LDA analyses were quite similar, but LDA added information that could not be retrieved only with LIWC analysis. Furthermore, open-vocabulary features outperformed closed-vocabulary features in terms of predictive power over participants' AUDIT-C scores (r =.46 vs. r =.28, respectively). Emerging relationships between text features and offline behaviors may have important implications for alcohol screening purposes in the online environment.

© 2019 The Authors.

Language: en

Keywords

Data mining; Digital footprints; Linguistics; Problem alcohol drinking; Psychology; Social media; Text analysis