Informatics School Theses and Dissertations

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https://hdl.handle.net/1805/954
Please go to "Informatics Graduate Theses and PhD Dissertations" to submit dissertations and theses for the School of Informatics and Computing, at: http://hdl.handle.net/1805/303.

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Browsing Informatics School Theses and Dissertations by Subject "activism"

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    Advocacy in Mental Health Social Interactions on Public Social Media
    (2022-02) Cornet, Victor P.; Holden, Richard J.; Bolchini, Davide; Brady, Erin; Mohler, George; Hong, Michin; Lee, Sangwon
    Health advocacy is a social phenomenon in which individuals and collectives attempt to raise awareness and change opinions and policies about health-related causes. Mental health advocacy is health advocacy to advance treatment, rights, and recognition of people living with a mental health condition. The Internet is reshaping how mental health advocacy is performed on a global scale, by facilitating and broadening the reach of advocacy activities, but also giving more room for opposing mental health advocacy. Another factor contributing to mental health advocacy lies in the cultural underpinnings of mental health in different societies; East Asian countries like South Korea have higher stigma attached to mental health compared to Western countries like the US. This study examines interactions about schizophrenia, a specific mental health diagnosis, on public social media (Facebook, Instagram, and Twitter) in two different languages, English and Korean, to determine how mental health advocacy and its opposition are expressed on social media. After delineation of a set of keywords for retrieval of content about schizophrenia, three months’ worth of social media posts were collected; a subset of these posts was then analyzed qualitatively using constant comparing with a proposed model describing online mental heath advocacy based on existing literature. Various expressions of light mental health advocacy, such as sharing facts about schizophrenia, and strong advocacy, showcasing offline engagement, were found in English posts; many of these expressions were however absent from the analyzed Korean posts that heavily featured jokes, insults, and criticisms. These findings were used to train machine learning classifiers to detect advocacy and counter-advocacy. The classifiers confirmed the predominance of counter-advocacy in Korean posts compared to important advocacy prevalence in English posts. These findings informed culturally sensitive recommendations for social media uses by mental health advocates and implications for international social media studies in human-computer interaction.