Early risk detection of self-harm and depression severity using BERT-based transformers: iLab at CLEF eRisk 2020

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

Research output: Contribution to conferencePaper

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 languageEnglish
Publication statusAccepted/In press - 1 Jul 2020
EventEarly Risk Prediction on the Internet: CLEF workshop - Thessaloniki, Greece
Duration: 22 Sep 202025 Sep 2020
https://early.irlab.org/

Workshop

WorkshopEarly Risk Prediction on the Internet
Abbreviated titleeRisk at CLEF
CountryGreece
CityThessaloniki
Period22/09/2025/09/20
Internet address

Keywords

  • self harm
  • depression
  • classification
  • social media
  • early detection

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    Martínez-Castaño, R., Htait, A., Azzopardi, L., & Moshfeghi, Y. (Accepted/In press). Early risk detection of self-harm and depression severity using BERT-based transformers: iLab at CLEF eRisk 2020. Paper presented at Early Risk Prediction on the Internet, Thessaloniki, Greece.