Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis

Shan, Yi; Ji, Meng; Xie, Wenxiu; Lam, Kam-Yiu; Chow, Chi-Yin

doi:10.2196/38799

Cited by 20 publications

(6 citation statements)

References 58 publications

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“…To find the appropriate k value, we used a topic coherence score [ 21 , 24 ]. Coherence score is used to measure the performance of a topic model with different number of clusters and can help differentiate between topics that are semantically interpretable and topics that are artifacts of statistical inference [ 24 , 25 ]. We tested 5 different k values (k=10, 20, 30, 40, and 50) for each data set and found that when k=20, we generated the highest coherence score, and this score did not change significantly with an increase in the k value.…”

Section: Methodsmentioning

confidence: 99%

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis

Honcharov¹,

Li²,

Sierra³

et al. 2023

JMIR Infodemiology

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Background Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. Objective We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. Methods We used a data set of COVID-19–related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags “antivaxxing,” “antivaxx,” “antivaxxers,” “antivax,” “anti-vaxxer,” “discredit,” “undermine,” “confidence,” and “immune.” Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. Results Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using “anti-vax” as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. Conclusions Most discussions surrounding public figures in common hashtags labelled as “anti-vax” did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax–related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse.

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Section: Methodsmentioning

confidence: 99%

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis

Honcharov¹,

Li²,

Sierra³

et al. 2023

JMIR Infodemiology

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“…Despite the rapid proliferation of chatbots, 36,57 less than half of participants were comfortable sharing mental health information with a chatbot, which may simply signify that these types of tools should be usable on an opt-in basis. Previous studies have suggested that it may be easier for someone to share these sensitive feelings with a computer or AI, 58 and this may be true for certain people, but our findings did not universally support this assertion.…”

Section: Comfort With Ai Accomplishing Mental Health Tasks (Rq3)mentioning

confidence: 99%

“…Although, others have explored narrower issues related to feedback on specific mental health apps and specific prediction tasks (i.e., suicide). 35,36 With the simultaneous increase in AI applications for mental health, patient access and ownership to their data, ethical concerns regarding the creation and use of AI, and the stigmas surrounding mental health, understanding patient perceptions of if and how AI may be appropriately used for mental health is critical. 37,38 We also elicited open-ended responses from participants to add to their quantitative feedback.…”

Section: Introductionmentioning

confidence: 99%

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study (Preprint)

Benda,

Desai,

Reza

et al. 2024

Preprint

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BACKGROUND The application of artificial intelligence to health and healthcare is rapidly increasing. Several studies have assessed the attitudes of health professionals but far fewer have explored perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues including radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health solutions, but broader perspectives towards AI for mental health have been under-explored. OBJECTIVE To understand public perceptions regarding potential benefits of AI, concerns, comfort with AI accomplishing various tasks, and values related to AI, all pertaining to mental health. METHODS We conducted a one-time cross-section survey with a nationally representative sample of 500 United States-based adults. Participants provided structured responses on their perceived benefits, concerns, comfortability, and values on AI related to mental health. They could also add free text responses to elaborate on their concerns and values. RESULTS A plurality of participants (49.3%) believed AI may be beneficial for mental healthcare, but this perspective differed based on socio-demographic variables (p<0.05). Specifically, Black participants (OR = 1.76) and those with lower health literacy (OR=2.16), perceived AI to be more beneficial, and females (OR=0.68) perceived AI to be less beneficial. Participants endorsed concerns related to the use of AI for mental health regarding its accuracy, possible unintended consequences such as misdiagnosis, confidentiality of their information, and loss of connection with their health professional. Over 80% of participants also valued being able to understand individual factors driving their risk, confidentiality, and autonomy as it pertained to the use of AI for their mental health. When asked about who was responsible for misdiagnosis of mental health conditions using AI, 81.6% of participants found the health professional to be responsible. Qualitative results revealed similar concerns related to the accuracy of AI and how its use may impact the confidentiality of their information. CONCLUSIONS Future work involving the use of AI for mental health should investigate strategies for conveying the level of AI's accuracy, factors that drive risk, and how data are used confidentially so that patients may work with their health professionals to determine when AI may be beneficial. It will also be important in a mental health context to ensure the patient-health professional relationship is preserved when AI is utilized. CLINICALTRIAL Not applicable

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“…Moreover, Simonson and Rosen [38] have shown that people use and trust reviews as a fundamental feature in their decision-making process. In addition, user reviews have been successfully used by researchers to understand users' experiences with digital technologies [39][40][41], including the contexts of health and well-being [15,[42][43][44][45][46][47][48]. Given this background, we argue that an analysis of user reviews of Natural Cycles can provide an entry point to their experiences with digital contraception.…”

Section: User Reviews As a Source Of User Experience Insightsmentioning

confidence: 99%

Analyzing User Reviews of the First Digital Contraceptive: Mixed Methods Study

Ciolfi Felice,

Søndergaard,

Balaam

2023

J Med Internet Res

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Background People in Western countries are increasingly rejecting hormone-based birth control and expressing a preference for hormone-free methods. Digital contraceptives have emerged as nonhormonal medical devices that make use of self-tracked data and algorithms to find a user’s fertile window. However, there is little knowledge about how people experience this seemingly new form of contraception, whose failure may result in unwanted pregnancies, high health risks, and life-changing consequences. As digital contraception becomes more widely adopted, examining its user experience is crucial to inform the design of technologies that not only are medically effective but also meet users’ preferences and needs. Objective We examined the user experience offered by Natural Cycles—the first digital contraceptive—through an analysis of app reviews written by its users worldwide. Methods We conducted a mixed methods analysis of 3265 publicly available reviews written in English by users of Natural Cycles on the Google Play Store. We combined computational and human techniques, namely, topic modeling and reflexive thematic analysis. Results For some users of digital contraception, the hormone-free aspect of the experience can be more salient than its digital aspect. Cultivating self-knowledge through the use of the technology can, in turn, feel empowering. Users also pointed to an algorithmic component that allows for increased accuracy over time as long as user diligence is applied. The interactivity of the digital contraceptive supports mutual learning and is experienced as agential and rewarding. Finally, a digital contraceptive can facilitate sharing the burden of contraceptive practices or highlight single-sided responsibilities while creating points of friction in the required daily routines. Conclusions Digital contraception is experienced by users as a tamed natural approach—a natural method contained and regulated by science and technology. This means that users can experience a method based on a digital product as “natural,” which positions digital contraceptives as a suitable option for people looking for evidence-based nonhormonal contraceptive methods. We point to interactivity as core to the user experience and highlight that a digital contraceptive might allow for collaboration between partners around contraceptive practices and responsibilities. We note that the user diligence required for the digital contraceptive to provide accurate and frequent data is sometimes not enough. Future research could look at designing (and redesigning) digital contraceptives with primary users and intimate partners, enhancing the experience of tamed naturalness; exploring how trust fluctuates among involved actors and in interactions with the technology; and, ultimately, designing more inclusive approaches to digital contraception.

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Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis

Cited by 20 publications

References 58 publications

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study (Preprint)

Analyzing User Reviews of the First Digital Contraceptive: Mixed Methods Study

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