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

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text. / Aguirrezabal Zabaleta, Manex; Amann, Janek.

Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. Dublin : Association for Computational Linguistics, 2022. p. 245–250.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Aguirrezabal Zabaleta, M & Amann, J 2022, KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text. in Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. Association for Computational Linguistics, Dublin, pp. 245–250. <https://aclanthology.org/2022.ltedi-1.35/>

APA

Aguirrezabal Zabaleta, M., & Amann, J. (2022). KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion (pp. 245–250). Association for Computational Linguistics. https://aclanthology.org/2022.ltedi-1.35/

Vancouver

Aguirrezabal Zabaleta M, Amann J. KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. Dublin: Association for Computational Linguistics. 2022. p. 245–250

Author

Aguirrezabal Zabaleta, Manex ; Amann, Janek. / KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text. Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. Dublin : Association for Computational Linguistics, 2022. pp. 245–250

Bibtex

@inproceedings{ba0ea40b848c46ef86942df61676ae3d,
title = "KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text",
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.",
author = "{Aguirrezabal Zabaleta}, Manex and Janek Amann",
year = "2022",
language = "English",
pages = "245–250",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
publisher = "Association for Computational Linguistics",

}

RIS

TY - GEN

T1 - KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text

AU - Aguirrezabal Zabaleta, Manex

AU - Amann, Janek

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

M3 - Article in proceedings

SP - 245

EP - 250

BT - Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

PB - Association for Computational Linguistics

CY - Dublin

ER -

ID: 306304302