ArticleInternational Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference)2022
Language Use in Mother-Adolescent Dyadic Interaction: Preliminary Results.
Article in International Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Detecting Young Children's Deception Using Facial Expression Analysis With High Dimensional Statistical Methods.Developmental science · 2026Article
- SHAP-based Prediction of Mother's History of Depression to Understand the Influence on Child Behavior.Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2023Article
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7 authors.
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Abstract
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.
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