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Published on: November 19, 2014
Schizophrenia Stigma in News Media: A Comprehensive Natural Language Processing Approach
Luz Maria Alliende Serra1,2, Rob Voigt3, Victor Pokorny1
1Psychology Department, Northwestern University, Evanston, IL 60208, United States.
Background And Hypothesis:
Stigma toward individuals with schizophrenia is well documented, yet the extent to which stigmatizing views are present in media remains understudied. This study investigates sentiment and violence-related content in US news coverage. We hypothesize that media with schizophrenia-related keywords will have higher negative and lower positive ratings, and more violent ratings than news for illnesses of comparable burden.
Study Design:
We conducted an observational comparative content analysis on 116 866 news transcripts from CNN, Fox News, and MSNBC (2016-2023). Sentiment ratings were derived using a roBERTa large language model, and violence-related language was measured using a dictionary-based natural language processing tool (LIWC-22).
Study Results:
News related to schizophrenia demonstrated significantly higher negative sentiment (β = 0.078, t (4061) = 6.85, P < .001), lower positive sentiment (β = -0.10, t (4061) = -11.55, P < .001), and higher violent content (β = 0.14, t (4061) = 3.31, P < .001) compared to news on comparable illnesses. Additionally, schizophrenia-related keywords were used in clinical contexts significantly less often (SZ = 48.49%, ILL = 97.84%, Χ2(1) = 1276.4, P < .001).
Conclusions:
The results indicate a pervasive association between schizophrenia and negative, violent language in media, reinforcing existing stigma. These findings highlight a need for interventions targeting media portrayal to reduce public stigma against individuals with schizophrenia.
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