TY - GEN
T1 - Using Daily Language to Understand Drinking
T2 - 9th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2024
AU - Matero, Matthew
AU - Vu, Huy
AU - Nilsson, August Håkan
AU - Mahwish, Syeda
AU - Cho, Young Min
AU - McKay, James R.
AU - Eichstaedt, Johannes
AU - Rosenthal, Richard N.
AU - Ungar, Lyle
AU - Schwartz, H. Andrew
N1 - Publisher Copyright:
©2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - 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 documents into a single set of features. This limits language to mainly 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 multilevel forecasting model that can 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.
AB - 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 documents into a single set of features. This limits language to mainly 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 multilevel forecasting model that can 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.
UR - https://www.scopus.com/pages/publications/85189758379
M3 - Conference contribution
AN - SCOPUS:85189758379
T3 - CLPsych 2024 - 9th Workshop on Computational Linguistics and Clinical Psychology, Proceedings of the Workshop
SP - 133
EP - 144
BT - CLPsych 2024 - 9th Workshop on Computational Linguistics and Clinical Psychology, Proceedings of the Workshop
A2 - Yates, Andrew
A2 - Desmet, Bart
A2 - Prud�hommeaux, Emily
A2 - Zirikly, Ayah
A2 - Bedrick, Steven
A2 - MacAvaney, Sean
A2 - Bar, Kfir
A2 - Ireland, Molly
A2 - Ophir, Yaakov
A2 - Ophir, Yaakov
PB - Association for Computational Linguistics (ACL)
Y2 - 21 March 2024
ER -