@inproceedings{s-etal-2023-overview-shared,
title = "Overview of the shared task on Detecting Signs of Depression from Social Media Text",
author = "S, Kayalvizhi and
D., Thenmozhi and
Chakravarthi, Bharathi Raja and
C, Jerin Mahibha and
S V, Kogilavani and
Rahood, Pratik Anil",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.4",
pages = "25--30",
abstract = "Social media has become a vital platform for personal communication. Its widespread use as a primary means of public communication offers an exciting opportunity for early detection and management of mental health issues. People often share their emotions on social media, but understanding the true depth of their feelings can be challenging. Depression, a prevalent problem among young people, is of particular concern due to its link with rising suicide rates. Identifying depression levels in social media texts is crucial for timely support and prevention of negative outcomes. However, it{'}s a complex task because human emotions are dynamic and can change significantly over time. The DepSign-LT-EDI@RANLP 2023 shared task aims to classify social media text into three depression levels: {``}Not Depressed,{''} {``}Moderately Depressed,{''} and {``}Severely Depressed.{''} This overview covers task details, dataset, methodologies used, and results analysis. Roberta-based models emerged as top performers, with the best result achieving an impressive macro F1-score of 0.584 among 31 participating teams.",
}
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%0 Conference Proceedings
%T Overview of the shared task on Detecting Signs of Depression from Social Media Text
%A S, Kayalvizhi
%A D., Thenmozhi
%A Chakravarthi, Bharathi Raja
%A C, Jerin Mahibha
%A S V, Kogilavani
%A Rahood, Pratik Anil
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F s-etal-2023-overview-shared
%X Social media has become a vital platform for personal communication. Its widespread use as a primary means of public communication offers an exciting opportunity for early detection and management of mental health issues. People often share their emotions on social media, but understanding the true depth of their feelings can be challenging. Depression, a prevalent problem among young people, is of particular concern due to its link with rising suicide rates. Identifying depression levels in social media texts is crucial for timely support and prevention of negative outcomes. However, it’s a complex task because human emotions are dynamic and can change significantly over time. The DepSign-LT-EDI@RANLP 2023 shared task aims to classify social media text into three depression levels: “Not Depressed,” “Moderately Depressed,” and “Severely Depressed.” This overview covers task details, dataset, methodologies used, and results analysis. Roberta-based models emerged as top performers, with the best result achieving an impressive macro F1-score of 0.584 among 31 participating teams.
%U https://aclanthology.org/2023.ltedi-1.4
%P 25-30
Markdown (Informal)
[Overview of the shared task on Detecting Signs of Depression from Social Media Text](https://aclanthology.org/2023.ltedi-1.4) (S et al., LTEDI-WS 2023)
ACL
- Kayalvizhi S, Thenmozhi D., Bharathi Raja Chakravarthi, Jerin Mahibha C, Kogilavani S V, and Pratik Anil Rahood. 2023. Overview of the shared task on Detecting Signs of Depression from Social Media Text. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 25–30, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.