KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text

Manex Aguirrezabal Zabaleta, Janek Amann*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.
Original languageEnglish
Title of host publicationProceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Place of PublicationDublin
PublisherAssociation for Computational Linguistics
Publication date2022
Pages245–250
Publication statusPublished - 2022

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