@inproceedings{s-etal-2023-team,
title = "Team-{KEC}@{LT}-{EDI}: Detecting Signs of Depression from Social Media Text",
author = "S, Malliga and
Shanmugavadivel, Kogilavani and
S, Arunaa and
R, Gokulkrishna and
A, Chandramukhii",
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-1.14/",
pages = "97--102",
abstract = "The rise of social media has led to a drastic surge in the dissemination of hostile and toxic content, fostering an alarming proliferation of hate speech, inflammatory remarks, and abusive language. The exponential growth of social media has facilitated the widespread circulation of hostile and toxic content, giving rise to an unprecedented influx of hate speech, incendiary language, and abusive rhetoric. The study utilized different techniques to represent the text data in a numerical format. Word embedding techniques aim to capture the semantic and syntactic information of the text data, which is essential in text classification tasks. The study utilized various techniques such as CNN, BERT, and N-gram to classify social media posts into depression and non-depression categories. Text classification tasks often rely on deep learning techniques such as Convolutional Neural Networks (CNN), while the BERT model, which is pre-trained, has shown exceptional performance in a range of natural language processing tasks. To assess the effectiveness of the suggested approaches, the research employed multiple metrics, including accuracy, precision, recall, and F1-score. The outcomes of the investigation indicate that the suggested techniques can identify symptoms of depression with an average accuracy rate of 56{\%}."
}
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<abstract>The rise of social media has led to a drastic surge in the dissemination of hostile and toxic content, fostering an alarming proliferation of hate speech, inflammatory remarks, and abusive language. The exponential growth of social media has facilitated the widespread circulation of hostile and toxic content, giving rise to an unprecedented influx of hate speech, incendiary language, and abusive rhetoric. The study utilized different techniques to represent the text data in a numerical format. Word embedding techniques aim to capture the semantic and syntactic information of the text data, which is essential in text classification tasks. The study utilized various techniques such as CNN, BERT, and N-gram to classify social media posts into depression and non-depression categories. Text classification tasks often rely on deep learning techniques such as Convolutional Neural Networks (CNN), while the BERT model, which is pre-trained, has shown exceptional performance in a range of natural language processing tasks. To assess the effectiveness of the suggested approaches, the research employed multiple metrics, including accuracy, precision, recall, and F1-score. The outcomes of the investigation indicate that the suggested techniques can identify symptoms of depression with an average accuracy rate of 56%.</abstract>
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%0 Conference Proceedings
%T Team-KEC@LT-EDI: Detecting Signs of Depression from Social Media Text
%A S, Malliga
%A Shanmugavadivel, Kogilavani
%A S, Arunaa
%A R, Gokulkrishna
%A A, Chandramukhii
%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-team
%X The rise of social media has led to a drastic surge in the dissemination of hostile and toxic content, fostering an alarming proliferation of hate speech, inflammatory remarks, and abusive language. The exponential growth of social media has facilitated the widespread circulation of hostile and toxic content, giving rise to an unprecedented influx of hate speech, incendiary language, and abusive rhetoric. The study utilized different techniques to represent the text data in a numerical format. Word embedding techniques aim to capture the semantic and syntactic information of the text data, which is essential in text classification tasks. The study utilized various techniques such as CNN, BERT, and N-gram to classify social media posts into depression and non-depression categories. Text classification tasks often rely on deep learning techniques such as Convolutional Neural Networks (CNN), while the BERT model, which is pre-trained, has shown exceptional performance in a range of natural language processing tasks. To assess the effectiveness of the suggested approaches, the research employed multiple metrics, including accuracy, precision, recall, and F1-score. The outcomes of the investigation indicate that the suggested techniques can identify symptoms of depression with an average accuracy rate of 56%.
%U https://aclanthology.org/2023.ltedi-1.14/
%P 97-102
Markdown (Informal)
[Team-KEC@LT-EDI: Detecting Signs of Depression from Social Media Text](https://aclanthology.org/2023.ltedi-1.14/) (S et al., LTEDI 2023)
ACL
- Malliga S, Kogilavani Shanmugavadivel, Arunaa S, Gokulkrishna R, and Chandramukhii A. 2023. Team-KEC@LT-EDI: Detecting Signs of Depression from Social Media Text. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 97–102, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.