| Autor: |
Sagar Jilka, Clarissa Mary Odoi, Janet van Bilsen, Daniel Morris, Sinan Erturk, Nicholas Cummins, Matteo Cella, Til Wykes |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
npj Schizophrenia, Vol 8, Iss 1, Pp 1-8 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2334-265X |
| DOI: |
10.1038/s41537-021-00197-6 |
| Popis: |
Abstract Stigma has negative effects on people with mental health problems by making them less likely to seek help. We develop a proof of principle service user supervised machine learning pipeline to identify stigmatising tweets reliably and understand the prevalence of public schizophrenia stigma on Twitter. A service user group advised on the machine learning model evaluation metric (fewest false negatives) and features for machine learning. We collected 13,313 public tweets on schizophrenia between January and May 2018. Two service user researchers manually identified stigma in 746 English tweets; 80% were used to train eight models, and 20% for testing. The two models with fewest false negatives were compared in two service user validation exercises, and the best model used to classify all extracted public English tweets. Tweets classed as stigmatising by service users were more negative in sentiment (t (744) = 12.02, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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