Twitter Dataset for Mental Disorders Detection

Citation Author(s):
Miryam Elizabeth
Villa-Pérez
Tecnologico de Monterrey
Luis A.
Trejo
Tecnologico de Monterrey
Submitted by:
Miryam Villa
Last updated:
Thu, 05/16/2024 - 12:12
DOI:
10.21227/6pxp-4t91
Data Format:
*.csv (zip)
Research Article Link:
Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach
License:
Creative Commons Attribution
7559 Views
Categories:
Machine Learning
Health
Keywords:
mental health, disorders detection, twitter dataset, spanish tweets, english tweets
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Abstract 

 We provide two datasets extracted from Twitter, in Spanish and English, and annotate each one with approximately 1,500 users who have been diagnosed with one of nine different mental disorders (ADHD, Autism, Anxiety, Bipolar, Depression, Eating disoders, OCD, PTSD and Schizophrenia) along with 1,700 matched-control users. For both datasets, the outcome is a total of just over 3,000 Twitter users with their corresponding timelines (the texts retrieved from each user cover at least 3 months of activity on the social media), which support two user-level classification tasks, binary and multiclass.

 — Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, and (ii) remain in compliance with Twitter's Developer Policy.

Instructions: 
  • Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs.
  • Only the tweet IDs and Annotation are available. The tweet IDs of should be hydrated to form the corpus.
  • If you need the full dataset please contact me on: miryam@exatec.tec.mx

Please cite:

Miryam Elizabeth Villa-Pérez, Luis A. Trejo, Maisha Binte Moin, and Eleni Stroulia. 2023. Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach. IEEE Access 11, (2023), 128135–128152. doi: 10.1109/ACCESS.2023.3332289

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Submitted by DAYANA M on Fri, 08/30/2024 - 03:07

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Submitted by Tomas Moder on Tue, 09/10/2024 - 16:13

@ARTICLE{10315126,

  author={Villa-Pérez, Miryam Elizabeth and Trejo, Luis A. and Moin, Maisha Binte and Stroulia, Eleni},

  journal={IEEE Access}, 

  title={Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach}, 

  year={2023},

  volume={11},

  number={},

  pages={128135-128152},

  keywords={Binary sequences;Classification algorithms;Machine learning;Mental health;Social networking (online);Linguistics;Natural language processing;Cultural aspects;Binary classification;machine learning;mental health disorders;multiclass classification;social media;Twitter},

  doi={10.1109/ACCESS.2023.3332289}}

 

Submitted by Gourav Sen on Fri, 11/15/2024 - 09:39

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