BERT-based transformers for early detection of mental health illnesses

Rodrigo Martínez-Castaño; Amal Htait; Leif Azzopardi; Yashar Moshfeghi

doi:10.1007/978-3-030-85251-1_15

BERT-based transformers for early detection of mental health illnesses

Rodrigo Martínez-Castaño, Amal Htait, Leif Azzopardi, Yashar Moshfeghi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

6 Citations (Scopus)

35 Downloads (Pure)

Abstract

This paper briefly describes our research groups' efforts in tackling Task 1 (Early Detection of Signs of Self-Harm), and Task 2 (Measuring the Severity of the Signs of Depression) from the CLEF eRisk Track. Core to how we approached these problems was the use of BERT-based classifiers which were trained specifically for each task. Our results on both tasks indicate that this approach delivers high performance across a series of measures, particularly for Task 1, where our submissions obtained the best performance for precision, F1, latency-weighted F1 and ERDE at 5 and 50. This work suggests that BERT-based classifiers, when trained appropriately, can accurately infer which social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and early detection scoring mechanism, as well as apply the same approach to other related tasks (e.g., anorexia, depression, suicide).

Original language	English
Title of host publication	Experimental IR Meets Multilinguality, Multimodality, and Interaction
Subtitle of host publication	12th International Conference of the CLEF Association, CLEF 2021, Proceedings
Editors	K. Selçuk Candan, Bogdan Ionescu, Lorraine Goeuriot, Birger Larsen, Henning Müller, Alexis Joly, Maria Maistro, Florina Piroi, Guglielmo Faggioli, Nicola Ferro
Place of Publication	Cham, Switzerland
Pages	189-200
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-030-85251-1_15
Publication status	Published - 14 Sep 2021
Event	12th International Conference of the Cross-Language Evaluation Forum for European Languages, CLEF 2021 - Virtual, Online Duration: 21 Sep 2021 → 24 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12880 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference of the Cross-Language Evaluation Forum for European Languages, CLEF 2021
City	Virtual, Online
Period	21/09/21 → 24/09/21

Keywords

BERT
classification
depression
early detection
self-harm
social media
XLM-RoBERTa

Access to Document

10.1007/978-3-030-85251-1_15Licence: CC BY 4.0

Martinez-Castano-Springer-2021-BERT-Based-transformers-for-earlyFinal published version, 365 KBLicence: CC BY 4.0

Cite this

Martínez-Castaño, R., Htait, A., Azzopardi, L., & Moshfeghi, Y. (2021). BERT-based transformers for early detection of mental health illnesses. In K. S. Candan, B. Ionescu, L. Goeuriot, B. Larsen, H. Müller, A. Joly, M. Maistro, F. Piroi, G. Faggioli, & N. Ferro (Eds.), Experimental IR Meets Multilinguality, Multimodality, and Interaction: 12th International Conference of the CLEF Association, CLEF 2021, Proceedings (pp. 189-200). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12880 LNCS).. https://doi.org/10.1007/978-3-030-85251-1_15

Martínez-Castaño, Rodrigo ; Htait, Amal ; Azzopardi, Leif et al. / BERT-based transformers for early detection of mental health illnesses. Experimental IR Meets Multilinguality, Multimodality, and Interaction: 12th International Conference of the CLEF Association, CLEF 2021, Proceedings. editor / K. Selçuk Candan ; Bogdan Ionescu ; Lorraine Goeuriot ; Birger Larsen ; Henning Müller ; Alexis Joly ; Maria Maistro ; Florina Piroi ; Guglielmo Faggioli ; Nicola Ferro. Cham, Switzerland, 2021. pp. 189-200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f57eb753d43a46709a902cbed04df588,

title = "BERT-based transformers for early detection of mental health illnesses",

abstract = "This paper briefly describes our research groups' efforts in tackling Task 1 (Early Detection of Signs of Self-Harm), and Task 2 (Measuring the Severity of the Signs of Depression) from the CLEF eRisk Track. Core to how we approached these problems was the use of BERT-based classifiers which were trained specifically for each task. Our results on both tasks indicate that this approach delivers high performance across a series of measures, particularly for Task 1, where our submissions obtained the best performance for precision, F1, latency-weighted F1 and ERDE at 5 and 50. This work suggests that BERT-based classifiers, when trained appropriately, can accurately infer which social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and early detection scoring mechanism, as well as apply the same approach to other related tasks (e.g., anorexia, depression, suicide).",

keywords = "BERT, classification, depression, early detection, self-harm, social media, XLM-RoBERTa",

author = "Rodrigo Mart{\'i}nez-Casta{\~n}o and Amal Htait and Leif Azzopardi and Yashar Moshfeghi",

year = "2021",

month = sep,

day = "14",

doi = "10.1007/978-3-030-85251-1_15",

language = "English",

isbn = "9783030852504",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "189--200",

editor = "Candan, {K. Sel{\c c}uk} and Bogdan Ionescu and Lorraine Goeuriot and Birger Larsen and Henning M{\"u}ller and Alexis Joly and Maria Maistro and Florina Piroi and Guglielmo Faggioli and Nicola Ferro",

booktitle = "Experimental IR Meets Multilinguality, Multimodality, and Interaction",

note = "12th International Conference of the Cross-Language Evaluation Forum for European Languages, CLEF 2021 ; Conference date: 21-09-2021 Through 24-09-2021",

}

Martínez-Castaño, R, Htait, A, Azzopardi, L & Moshfeghi, Y 2021, BERT-based transformers for early detection of mental health illnesses. in KS Candan, B Ionescu, L Goeuriot, B Larsen, H Müller, A Joly, M Maistro, F Piroi, G Faggioli & N Ferro (eds), Experimental IR Meets Multilinguality, Multimodality, and Interaction: 12th International Conference of the CLEF Association, CLEF 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12880 LNCS, Cham, Switzerland, pp. 189-200, 12th International Conference of the Cross-Language Evaluation Forum for European Languages, CLEF 2021, Virtual, Online, 21/09/21. https://doi.org/10.1007/978-3-030-85251-1_15

BERT-based transformers for early detection of mental health illnesses. / Martínez-Castaño, Rodrigo; Htait, Amal; Azzopardi, Leif et al.

Experimental IR Meets Multilinguality, Multimodality, and Interaction: 12th International Conference of the CLEF Association, CLEF 2021, Proceedings. ed. / K. Selçuk Candan; Bogdan Ionescu; Lorraine Goeuriot; Birger Larsen; Henning Müller; Alexis Joly; Maria Maistro; Florina Piroi; Guglielmo Faggioli; Nicola Ferro. Cham, Switzerland, 2021. p. 189-200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12880 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - BERT-based transformers for early detection of mental health illnesses

AU - Martínez-Castaño, Rodrigo

AU - Htait, Amal

AU - Azzopardi, Leif

AU - Moshfeghi, Yashar

PY - 2021/9/14

Y1 - 2021/9/14

N2 - This paper briefly describes our research groups' efforts in tackling Task 1 (Early Detection of Signs of Self-Harm), and Task 2 (Measuring the Severity of the Signs of Depression) from the CLEF eRisk Track. Core to how we approached these problems was the use of BERT-based classifiers which were trained specifically for each task. Our results on both tasks indicate that this approach delivers high performance across a series of measures, particularly for Task 1, where our submissions obtained the best performance for precision, F1, latency-weighted F1 and ERDE at 5 and 50. This work suggests that BERT-based classifiers, when trained appropriately, can accurately infer which social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and early detection scoring mechanism, as well as apply the same approach to other related tasks (e.g., anorexia, depression, suicide).

AB - This paper briefly describes our research groups' efforts in tackling Task 1 (Early Detection of Signs of Self-Harm), and Task 2 (Measuring the Severity of the Signs of Depression) from the CLEF eRisk Track. Core to how we approached these problems was the use of BERT-based classifiers which were trained specifically for each task. Our results on both tasks indicate that this approach delivers high performance across a series of measures, particularly for Task 1, where our submissions obtained the best performance for precision, F1, latency-weighted F1 and ERDE at 5 and 50. This work suggests that BERT-based classifiers, when trained appropriately, can accurately infer which social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and early detection scoring mechanism, as well as apply the same approach to other related tasks (e.g., anorexia, depression, suicide).

KW - BERT

KW - classification

KW - depression

KW - early detection

KW - self-harm

KW - social media

KW - XLM-RoBERTa

U2 - 10.1007/978-3-030-85251-1_15

DO - 10.1007/978-3-030-85251-1_15

M3 - Conference contribution book

AN - SCOPUS:85115877888

SN - 9783030852504

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 189

EP - 200

BT - Experimental IR Meets Multilinguality, Multimodality, and Interaction

A2 - Candan, K. Selçuk

A2 - Ionescu, Bogdan

A2 - Goeuriot, Lorraine

A2 - Larsen, Birger

A2 - Müller, Henning

A2 - Joly, Alexis

A2 - Maistro, Maria

A2 - Piroi, Florina

A2 - Faggioli, Guglielmo

A2 - Ferro, Nicola

CY - Cham, Switzerland

T2 - 12th International Conference of the Cross-Language Evaluation Forum for European Languages, CLEF 2021

Y2 - 21 September 2021 through 24 September 2021

ER -

Martínez-Castaño R, Htait A, Azzopardi L , Moshfeghi Y. BERT-based transformers for early detection of mental health illnesses. In Candan KS, Ionescu B, Goeuriot L, Larsen B, Müller H, Joly A, Maistro M, Piroi F, Faggioli G, Ferro N, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction: 12th International Conference of the CLEF Association, CLEF 2021, Proceedings. Cham, Switzerland. 2021. p. 189-200. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-85251-1_15

BERT-based transformers for early detection of mental health illnesses

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this