@inproceedings{matero-etal-2024-using,
title = "Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis",
author = "Matero, Matthew and
Vu, Huy and
Nilsson, August and
Mahwish, Syeda and
Cho, Young Min and
McKay, James and
Eichstaedt, Johannes and
Rosenthal, Richard and
Ungar, Lyle and
Schwartz, H. Andrew",
editor = "Yates, Andrew and
Desmet, Bart and
Prud{'}hommeaux, Emily and
Zirikly, Ayah and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ireland, Molly and
Ophir, Yaakov",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.10",
pages = "133--144",
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.",
}
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%0 Conference Proceedings
%T Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis
%A Matero, Matthew
%A Vu, Huy
%A Nilsson, August
%A Mahwish, Syeda
%A Cho, Young Min
%A McKay, James
%A Eichstaedt, Johannes
%A Rosenthal, Richard
%A Ungar, Lyle
%A Schwartz, H. Andrew
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F matero-etal-2024-using
%X 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.
%U https://aclanthology.org/2024.clpsych-1.10
%P 133-144
Markdown (Informal)
[Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis](https://aclanthology.org/2024.clpsych-1.10) (Matero et al., CLPsych-WS 2024)
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.