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
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 R ). American Psychiatric Pub.
AOIR. (2012). Ethical decision-making and internet research 2.0: recommendations from the aoir ethics working committee. Retrieved from: http://www.aoir.org/reports/ethics2.pdf.
Birnbaum, M. L., Rizvi, A. F., Correll, C. U., and Kane, J. M. (2015). Role of social media and the internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early intervention in psychiatry.
Bontcheva, K., Cunningham, H., Roberts, I., Roberts, A., Tablan, V., Aswani, N., and Gorrell, G. (2013). Gate teamware: a web-based, collaborative text annotation framework. Language Resources and Evaluation, 47(4):1007–1029.
BPS, B. P. S. (2013). Ethics guidelines for internet-mediated research. Retrieved from: http://www.bps.org.uk/system/files/Public%20files/inf206-guidelines-for-internet-mediated-research.pdf.
Chen, D. and Manning, C. D. (2014). A fast and accurate dependency parser using neural networks. In EMNLP, pages 740–750.
Clarke, M. C., Kelleher, I., Clancy, M., and Cannon, M. (2012). Predicting risk and the emergence of schizophrenia. Psychiatr. Clin. North Am, 35(3):585–612.
Conway, M. and O’Connor, D. (2016). Social media, big data, and mental health: Current advances and ethical implications. Current Opinion in Psychology.
Coppersmith, G., Dredze, M., and Harman, C. (2014). Quantifying mental health signals in twitter. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 51–60.
Coppersmith, G., Dredze, M., Harman, C., and Hollingshead, K. (2015). From adhd to sad: Analyzing the language of mental health on twitter through self-reported diagnoses. NAACL HLT 2015, page 1.
Gimpel, K., Schneider, N., and O’Connor. (2013). Annotation guidelines for Twitter part-of-speech tagging version 0.3. March.
Gowen, K., Deschaine, M., Gruttadara, D., and Markey, D. (2012). Young adults with mental health conditions and social networking websites: Seeking tools to build community. Psychiatric Rehabilitation Journal, 35(3):245.
Haddock, G., McCarron, J., Tarrier, N., and Faragher, E. (1999). Scales to measure dimensions of hallucinations and delusions: the psychotic symptom rating scales (psyrats). Psychological medicine, 29(04):879–889.
Hustig, H. H. and Hafner, R. J. (1990). Persistent auditory hallucinations and their relationship to delusions and mood. The Journal of nervous and mental disease, 178(4):264–267.
INVOLVE. (2014). Guidance on the use of social media to actively involve people in research. Retrieved from: http://www.invo.org.uk/wpcontent/uploads/2014/11/9982-Social-Media-Guide-WEB.pdf.
Joseph, A. J., Tandon, N., Yang, L. H., Duckworth, K., Torous, J., Seidman, L. J., and Keshavan, M. S. (2015). # schizophrenia: Use and misuse on twitter. Schizophrenia research, 165(2):111–115.
Kwak, H., Lee, C., Park, H., and Moon, S. (2010). What is Twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, pages 591–600. ACM.
Lincoln, T. M., L¨ullmann, E., and Rief, W. (2007). Correlates and long-term consequences of poor insight in patients with schizophrenia. a systematic review. Schizophrenia bulletin, 33(6):1324–1342.
McCarney, R., Warner, J., Iliffe, S., Van Haselen, R., Griffin, M., and Fisher, P. (2007). The Hawthorne Effect: a randomised, controlled trial. BMC medical research methodology, 7(1):1.
Meehl, P. E. (1990). Toward an integrated theory of schizotaxia, schizotypy, and schizophrenia. Journal of Personality Disorders, 4(1):1.
Nayani, T. H. and David, A. S. (1996). The auditory hallucination: a phenomenological survey. Psychological medicine, 26(01):177–189.
Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., and Schneider, N. (2012). Part-of-speech tagging for Twitter: Word clusters and other advances. Carnegie Mellon University.
Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., and Smith, N. A. (2013). Improved part-ofspeech tagging for online conversational text with word clusters. Association for Computational Linguistics.
Pappas, N. and Popescu-Belis, A. (2013). Sentiment analysis of user comments for one-class collaborative filtering over TED talks. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pages 773–776. ACM.
Reavley, N. J. and Pilkington, P. D. (2014). Use of twitter to monitor attitudes toward depression and schizophrenia: an exploratory study. PeerJ, 2:e647.
Rijsbergen, K. V. (1979). Information Retrieval. Butterworths, London, 2nd edition.
Rivers, C. M. and Lewis, B. L. (2014). Ethical research standards in a world of big data. F1000Research, 3. Shepherd, A., Sanders, C., Doyle, M., and Shaw, J. (2015).
Using social media for support and feedback by mental health service users: thematic analysis of a Twitter conversation. BMC psychiatry, 15(1):1.
Statista. (2015). Number of monthly active Twitter users worldwide from 1st quarter 2010 to 4th quarter 2015 (in millions). Retrieved from: http://www.statista.com/statistics/282087/number-ofmonthly-active-twitter-users/.
Stenetorp, P., Pyysalo, S., Topi´c, G., Ohta, T., Ananiadou, S., and Tsujii, J. (2012). BRAT: a web-based tool for NLP-assisted text annotation. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the ACL, pages 102–107. ACL.
Stone, A. A. and Shiffman, S. (2002). Capturing momentary, self-report data: A proposal for reporting guidelines. Annals of Behavioral Medicine, 24(3):236–243.
Tao, K., Abel, F., Hauff, C., Houben, G.-J., and Gadiraju, U. (2013). Groundhog day: near-duplicate detection on Twitter. In Proceedings of the 22nd international conference on WWW, pages 1273–1284. International WWW Conferences Steering Committee.
Van Os, J., Linscott, R. J., Myin-Germeys, I., Delespaul, P., and Krabbendam, L. (2009). A systematic review and meta-analysis of the psychosis continuum: evidence for a psychosis proneness–persistence–impairment model of psychotic disorder. Psychological medicine, 39(02):179–195.
