Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis

Matthew Matero, Huy Vu, August Nilsson, Syeda Mahwish, Young Min Cho, James McKay, Johannes Eichstaedt, Richard Rosenthal, Lyle Ungar, H. Andrew Schwartz


Abstract
Analyses for linking language with psychological factors or behaviors predominately treat linguistic features as a static set, working with a single document per person or aggregating across multiple posts (e.g. on social media) into a single set of features. This limits language to mostly shed light on between-person differences rather than changes in behavior within-person. Here, we collected a novel dataset of daily surveys where participants were asked to describe their experienced well-being and report the number of alcoholic beverages they had within the past 24 hours. Through this data, we first build a multi-level forecasting model that is able to capture within-person change and leverage both the psychological features of the person and daily well-being responses. Then, we propose a longitudinal version of differential language analysis that finds patterns associated with drinking more (e.g. social events) and less (e.g. task-oriented), as well as distinguishing patterns of heavy drinks versus light drinkers.
Anthology ID:
2024.clpsych-1.10
Volume:
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
Month:
March
Year:
2024
Address:
St. Julians, Malta
Editors:
Andrew Yates, Bart Desmet, Emily Prud’hommeaux, Ayah Zirikly, Steven Bedrick, Sean MacAvaney, Kfir Bar, Molly Ireland, Yaakov Ophir
Venues:
CLPsych | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
133–144
Language:
URL:
https://aclanthology.org/2024.clpsych-1.10
DOI:
Bibkey:
Cite (ACL):
Matthew Matero, Huy Vu, August Nilsson, Syeda Mahwish, Young Min Cho, James McKay, Johannes Eichstaedt, Richard Rosenthal, Lyle Ungar, and H. Andrew Schwartz. 2024. Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 133–144, St. Julians, Malta. Association for Computational Linguistics.
Cite (Informal):
Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis (Matero et al., CLPsych-WS 2024)
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PDF:
https://aclanthology.org/2024.clpsych-1.10.pdf
Video:
 https://aclanthology.org/2024.clpsych-1.10.mp4