Table 2.

Select studies on combined social media and AI use on health issues

Study	Sample	Research context	Objects of analysis	Position of responsible AI principles in the conceptual model	Research gaps/Theoretical contributions	Major findings
Rocha et al. (2018)	A total of 103 responses from GenomeConnect and Simons VIP registry participants	Rare disease community and genetic testing	Two online patient registries	Privacy. Autonomy. Reliability.	There is a paucity of literature characterizing the potential for communication, networking, privacy and membership preferences, and support needs for people with rare genetic diagnoses. This preliminary work could inform the design of more robust and nuanced research of rare disease communities collectively or specific rare disease communities individually.	• There is broad variability between individuals’ privacy preferences, according to experiences, concerns, and adaptation to their diagnosis or genetic rest results. • Patients wish to have some control over the visibility of the information they share. • Genetic counselors should provide patients with guidance about reliable social media resources for information.
Denecke et al. (2019)	22 articles and 12 clinical trials involving AI in participatory health contexts	Participatory health informatics	Seven databases and online forum (clinicaltrials.gov)	Transparency. Beneficence. Privacy.	Although AI for supporting participatory health is still in its infancy, there are a number of important research priorities that should be considered for the advancement of a field such as the psychosocial wellbeing of individuals and wider acceptance of AI into the healthcare ecosystem.	• AI may require the design to be embedded deeply or even invisibly in patients’ daily routine. • The analysis of social media data with AI can provide new insights into patient health beliefs and perspectives on their health, healthcare use, and efficacy and adverse effects of drugs and treatments. •The ethical and practical privacy issues using healthcare data (such as medical images, biological data, experiential reporting, and physiological data) need to be urgently addressed by health systems, regulators, and society.
McClellan et al. (2017)	176 million tweets from 2011 to 2014 with content related to depression or suicide	Depression or suicide	Twitter activities: expected response to planned behavioral health events and unexpected response to unanticipated events	Beneficence	Although ARIMA models have been used extensively in other fields, they have not been used widely in public health. The findings indicate that the ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioural health.	• Spikes in tweet volume following a behavioral health event often last for less than 2 days. • By monitoring social media communications and timing dissemination of information about mental health, prevention and treatment initiatives can be taken by government agencies and public health organizations.
Tutubalina, and Nikolenko, (2018)	217,485 reviews from authors tagged as ‘patient’	High blood pressure, pain, depression, chronic trouble sleeping, attention deficit disorder with hyperactivity	WebMD.com, a health information services website that provides credible information, supportive communities, and in-depth material about health subject	Reliability and safety	Medical applications, demographic information regarding the authors of reviews such as age and gender is important, but existing studies usually either assume that this information is available or overlook the issue entirely. The study found that convolutional neural networks perform best in predicting demographic information and topic models provide additional information and reflect gender-specific and age-specific symptom profiles.	• While neural networks in this kind of NLP-related problems perform better in terms of the classification/regression objective, topic models learn and provide extra information that may lead to interesting observations relevant to the underlying healthcare application.
Ahmed et al. (2009)	214,784 tweets from 28 April to 29 April 2009	H1N1 pandemic	Twitter data were retrieved from the Twitter Firehose API via a licensed reseller, Visibrain (n.d.)	Reliability and safety	Novel insights were derived on how users communicate about disease outbreaks on social media platforms. The study also provided an innovative methodological contribution.	• Twitter data could be utilized by library professionals for developing a better understanding of public views on health-related topics. • Using an in-depth qualitative method such as thematic analysis when analyzing social media data may lead to greater insights. • Misunderstandings of medical advice can lead to dangerous consequences and must be understood carefully.
Sumner et al. (2019)	95,555 social media posts and articles about an alleged suicide game were collected	Suicide	Twitter, YouTube, Reddit, Tumblr, and blogs, forums, and news articles.	Beneficence, Non-maleficence	Social media messages and online games promoting suicide are a concern for parents and clinicians. The study provided a better understanding of the degree to which social media data can provide earlier public health awareness.	• Novel online risks to mental health, such as pro-suicide games or messages, can spread rapidly and globally. • Better understanding social media and Web data may allow for detection of such threats earlier than is currently possible.
Booth et al. (2018)	Monthly outpatient mental health visits	Mental health awareness and stigma reduction	The inclusion of Twitter into the 2012 Bell Let’s Talk campaign.	Non-maleficence	The study provided important methodological implications for researchers wishing to ascertain the efficacy of social media and other digitally enabled media campaigns operated at population level.	• The 2012 Bell Let’s Talk campaign was temporally associated with an increase in the rate of mental health visits among Ontarian youth.
The current study	25 social media practitioners from health industry.	Responses of interviewees regarding responsible AI	Weibo, WeChat and other health communities, social media pages of medical consultation software.	Eight principles	A systematic discussion about responsible AI principles is limited; the application of responsible AI in facilitating digital health through social media is scarce; associated practical investigations of responsible AI are absent and therefore the research lacks an empirical foundation.