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    <abstract>Depression is a prevalent mental illness characterized by feelings of sadness and a lack of interest in daily activities. Early detection of depression is crucial to prevent severe consequences, making it essential to observe and treat the condition at its onset. At ACL-2022, the DepSign-LT-EDI project aimed to identify signs of depression in individuals based on their social media posts, where people often share their emotions and feelings. Using social media postings in English, the system categorized depression signs into three labels: “not depressed,” “moderately depressed,” and “severely depressed.” To achieve this, our team has applied MentalRoBERTa, a model trained on big data of mental health. The test results indicated a macro F1-score of 0.439, ranking the fourth in the shared task.</abstract>
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%0 Conference Proceedings
%T iicteam@LT-EDI-2023: Leveraging pre-trained Transformers for Fine-Grained Depression Level Detection in Social Media
%A Vajrobol, Vajratiya
%A Aggarwal, Nitisha
%A Singh, Karanpreet
%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 vajrobol-etal-2023-iicteam
%X Depression is a prevalent mental illness characterized by feelings of sadness and a lack of interest in daily activities. Early detection of depression is crucial to prevent severe consequences, making it essential to observe and treat the condition at its onset. At ACL-2022, the DepSign-LT-EDI project aimed to identify signs of depression in individuals based on their social media posts, where people often share their emotions and feelings. Using social media postings in English, the system categorized depression signs into three labels: “not depressed,” “moderately depressed,” and “severely depressed.” To achieve this, our team has applied MentalRoBERTa, a model trained on big data of mental health. The test results indicated a macro F1-score of 0.439, ranking the fourth in the shared task.
%U https://aclanthology.org/2023.ltedi-1.12
%P 83-88

Markdown (Informal)

[iicteam@LT-EDI-2023: Leveraging pre-trained Transformers for Fine-Grained Depression Level Detection in Social Media](https://aclanthology.org/2023.ltedi-1.12) (Vajrobol et al., LTEDI-WS 2023)

• iicteam@LT-EDI-2023: Leveraging pre-trained Transformers for Fine-Grained Depression Level Detection in Social Media (Vajrobol et al., LTEDI-WS 2023)

ACL

• Vajratiya Vajrobol, Nitisha Aggarwal, and Karanpreet Singh. 2023. iicteam@LT-EDI-2023: Leveraging pre-trained Transformers for Fine-Grained Depression Level Detection in Social Media. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 83–88, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.