A1 Refereed original research article in a scientific journal
'Schizophrenia' on Twitter: Content Analysis of Greek Language Tweets
Authors: Athanasopoulou C, Sakellari E
Publisher: I O S PRESS, PO BOX 10558, BURKE, VA 22009-0558 USA
Publication year: 2016
Journal: Studies in Health Technology and Informatics
Journal name in source: UNIFYING THE APPLICATIONS AND FOUNDATIONS OF BIOMEDICAL AND HEALTH INFORMATICS
Journal acronym: STUD HEALTH TECHNOL
Volume: 226
First page : 271
Last page: 274
Number of pages: 4
ISBN: 978-1-61499-663-7
ISSN: 0926-9630
DOI: https://doi.org/10.3233/978-1-61499-664-4-271
Abstract
Twitter is an online space whose users can create and share ideas and information instantly. The term schizophrenia is frequently used in a stigmatizing way in Greek language. In Greece, Twitter is the tenth most popular website. Tweets related to schizophrenia in Greek language, have not been investigated. We aimed to examine schizophrenia Tweets in comparison with other illness (diabetes). Deductive content analysis was applied. Schizophrenia Tweets (n=239), tended to be more negative, medically inappropriate, sarcastic, and used non-medically than diabetes Tweets (n=205). Our findings confirm the frequent, non-medical misuse of the term 'schizophrenia' in online sources written in Greek language. These results show that mental health education interventions are needed to raise awareness among the general population, in order to eliminate stigmatizing behaviors. Future anti-stigma actions, could also raise awareness among Internet users about the importance of, avoiding using medical terms in negative or sarcastic ways, and eliminate any potential stigmatizing content.
Twitter is an online space whose users can create and share ideas and information instantly. The term schizophrenia is frequently used in a stigmatizing way in Greek language. In Greece, Twitter is the tenth most popular website. Tweets related to schizophrenia in Greek language, have not been investigated. We aimed to examine schizophrenia Tweets in comparison with other illness (diabetes). Deductive content analysis was applied. Schizophrenia Tweets (n=239), tended to be more negative, medically inappropriate, sarcastic, and used non-medically than diabetes Tweets (n=205). Our findings confirm the frequent, non-medical misuse of the term 'schizophrenia' in online sources written in Greek language. These results show that mental health education interventions are needed to raise awareness among the general population, in order to eliminate stigmatizing behaviors. Future anti-stigma actions, could also raise awareness among Internet users about the importance of, avoiding using medical terms in negative or sarcastic ways, and eliminate any potential stigmatizing content.
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