Language Use in Mother-Adolescent Dyadic Interaction: Preliminary Results

• Published in: International Conference on Affective Computing and Intelligent Interaction and workshops Vol. 2022; pp. 1 - 8
• Main Authors: Cariola, Laura A., Hinduja, Saurabh, Bilalpur, Maneesh, Sheeber, Lisa B., Allen, Nicholas, Morency, Louis-Philippe, Cohn, Jeffrey F.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.10.2022
• Subjects: Affective computing; Analytical models; Computational modeling; Depression; dyads; language; Linguistics; LIWC; Mood; mothers; Support vector machines; SVM; dyads; LIWC; language; SVM; depression; mothers
• ISSN: 2156-8103; 2156-8111
• DOI: 10.1109/ACII55700.2022.9953886

This preliminary study applied a computer-assisted quantitative linguistic analysis to examine the effectiveness of language-based classification models to discriminate between mothers (n = 140) with and without history of treatment for depression (51% and 49%, respectively). Mothers were recorded during a problem-solving interaction with their adolescent child. Transcripts were manually annotated and analyzed using a dictionary-based, natural-language program approach (Linguistic Inquiry and Word Count). To assess the importance of linguistic features to correctly classify history of depression, we used Support Vector Machines (SVM) with interpretable features. Using linguistic features identified in the empirical literature, an initial SVM achieved nearly 63% accuracy. A second SVM using only the top 5 highest ranked SHAP features improved accuracy to 67.15%. The findings extend the existing literature base on understanding language behavior of depressed mood states, with a focus on the linguistic style of mothers with and without a history of treatment for depression and its potential impact on child development and trans-generational transmission of depression.