Maksim Belousov
School of Computer Science, University of Manchester, UK
Mladen Dinev
School of Computer Science, University of Manchester, UK
Rohan Morris
School of Psychological Sciences, University of Manchester, UK
Natalie Berry
School of Psychological Sciences, University of Manchester, UK / Health eResearch Centre (HeRC), The Farr Institute of Health Informatics Research
School of Computer Science, University of Manchester, Kilburn Building, Manchester, UKI
Sandra Bucci
School of Psychological Sciences, University of Manchester, UK
Goran Nenadic
School of Computer Science, University of Manchester, UK / Health eResearch Centre (HeRC), The Farr Institute of Health Informatics Research
School of Computer Science, University of Manchester, Kilburn Building, Manchester, UK
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Published in: Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016
Linköping Electronic Conference Proceedings 128:5, p. 27 to 34
Published: 2016-06-03
ISBN: 978-91-7685-730-4
ISSN: 1650-3686 (print), 1650-3740 (online)
Auditory hallucinations are common in people who experience psychosis and psychotic-like phenomena. This exploratory study aimed
to establish the feasibility of harvesting and mining datasets from unsolicited Twitter posts to identify potential auditory hallucinations.
To this end, several search queries were defined to collect posts from Twitter. A training sample was annotated by research psychologists
for relatedness to auditory hallucinatory experiences and a text classifier was trained on that dataset to identify tweets related to auditory
hallucinations. A number of features were used including sentiment polarity and mentions of specific semantic classes, such as fear
expressions, communication tools and abusive language. We then used the classification model to generate a dataset with potential
mentions of auditory hallucinatory experiences. A preliminary analysis of a dataset (N = 4957) revealed that posts linked to auditory
hallucinations were associated with negative sentiments. In addition, such tweets had a higher proportionate distribution between the
hours of 11pm and 5am in comparison to other tweets.
machine learning, text mining, hallucinations, psychosis, psychotic-like experience, social media, twitter
