Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter (2022.lrec-1)
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| Challenge: | Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection. |
| Approach: | They propose to use deep learning to detect polarity and type of use in tweets . they propose to combine polarization detection with typeof use detection to improve polarities . |
| Outcome: | The proposed models can detect the polarity of a tweet and the types of use on a dataset that is not yet available. |
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| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
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Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework (2020.coling-main)
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Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit Sheth, Jeremiah Schumm
| Challenge: | Existing studies on social media for deriving mental health status of users focus on the depression detection task. |
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MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
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A Survey on Multilingual Mental Disorders Detection from Social Media Data (2026.eacl-long)
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| Challenge: | Existing studies on mental disorders focus on English data, overlooking critical signals that may be present in non-English texts. |
| Approach: | They present a list of 108 social media datasets that can be used to train NLP models for mental health screening in 25 languages. |
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Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
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| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
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Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twitter (2024.naacl-short)
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| Challenge: | Social media data is used for detecting users with mental disorders, but public datasets lack crucial metadata related to this aspect. |
| Approach: | They use a custom geo-located Twitter dataset to evaluate the generalization of depressiondetection models on cross-cultural Twitter data. |
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Do Models of Mental Health Based on Social Media Data Generalize? (2020.findings-emnlp)
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| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
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A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets (P18-3)
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| Challenge: | Suicidal ideation on social media websites is associated with higher suicide rates . suicide is the second leading cause of death among 15-29-year-olds . |
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Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
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Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (D19-62)
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| Challenge: | Existing methods for mental illness detection have limited data available for training . lack of sufficient annotated data and inability to extract explanations on the derived outcome have restricted researchers to use traditional methods. |
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