Research Area:  Machine Learning

Abstract:

Combining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insights into the connections between language patterns and depression and anxiety symptoms, particularly within specific stressful contexts.We found significant positive bivariate correlations between total DASS symptoms and hypothesized LIWC categories: first-person singular pronouns, absolute language, and negative emotion words. These results remained largely similar when using negative sentiment scores and when statistically controlling for gender, age, and education. Exploratory n-gram analyses also revealed new individual words and phrases correlated with total DASS symptoms. Lastly, our regression models demonstrated a significant association between language use and total DASS symptoms (R2?=?0.36–0.62).The current study is one of the first to examine associations between language use and DASS symptoms during the pandemic using both traditional and data-driven techniques. These results replicate and extend prior findings regarding negative emotion and absolute language and identify unique correlates of DASS symptoms during pandemic-related stress, contributing to the literature on language and mental health more broadly.

Keywords:  

Author(s) Name:  Abigail Beech , Haoxue Fan , Jocelyn Shu , Javiera Oyarzun , Peter Nadel , Olivia T. Karaman , Sophia Vranos , Elizabeth A. Phelps , M. Alexandra Kredlow

Publisher name:  ScienceDirect

DOI:  10.1016/j.jad.2025.01.139

Volume Information:  Volume: 9, (2025)

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0165032725001594