Article | 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 | Mining Auditory Hallucinations from Unsolicited Twitter Posts
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Title:
Mining Auditory Hallucinations from Unsolicited Twitter Posts
Author:
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
Download:
Full text (pdf)
Year:
2016
Conference:
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
Issue:
128
Article no.:
005
Pages:
27 to 34
No. of pages:
8
Publication type:
Abstract and fulltext
Published:
2016-06-03
ISBN:
978-91-7685-730-4
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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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.

Keywords: machine learning, text mining, hallucinations, psychosis, psychotic-like experience, social media, twitter

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

Author:
Maksim Belousov, Mladen Dinev, Rohan Morris, Natalie Berry, Sandra Bucci, Goran Nenadic
Title:
Mining Auditory Hallucinations from Unsolicited Twitter Posts
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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

Author:
Maksim Belousov, Mladen Dinev, Rohan Morris, Natalie Berry, Sandra Bucci, Goran Nenadic
Title:
Mining Auditory Hallucinations from Unsolicited Twitter Posts
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