Methodological Gaps in Predicting Mental Health States from Social Media

Ernala, Sindhu Kiranmai; Birnbaum, Michael L.; Candan, Kristin; Ali, Asra; Sterling, William A.; Kane, John M.; Choudhury, Munmun De

doi:10.1145/3290605.3300364

Cited by 93 publications

(98 citation statements)

References 75 publications

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“…Seven papers are proposals of specific concepts [ 28 , 82 , 154 ], data methods [ 31 ], models [ 184 ], or systems [ 193 , 217 ], and three apply existing ML algorithms to better understand [ 209 ] and assess mental health [ 201 ], or improve the communication of mental health providers [ 205 ]. Furthermore, few papers describe the conduct of empirical studies of an end-to-end ML system [ 78 , 140 ] or assess the quality of ML predictions [ 53 ]. One paper specifically discusses design implications for user-centric, deployable ML systems [ 77 ].…”

Section: Resultsmentioning

confidence: 99%

“…Despite much enthusiasm for easier, timely, and purportedly less biased data capture; one paper questioned the validity of developing diagnostic models for mental health conditions based on proxy data (e.g ., a person's participation in a mental health community) rather than clinically validated diagnostic information [ 53 ].…”

Section: Easy Timely Unobtrusivementioning

confidence: 99%

“…, to self-report) [ 176 ] and provide alternatives to clinical assessments [ 64 , 128 ], which can be confined to health care professionals and specialized clinics; and thus, be expensive in terms of clinician time [ 23 , 63 , 122 , 222 ]. In contrast, the collection of mental health proxy data from public social media is described as inexpensive, as it can be gathered with low effort and does not require any direct engagement with individuals [ 53 ].…”

Section: Time and Cost Savings: Reducing Burden On Participant Or Patmentioning

confidence: 99%

“…For the 29 records that collect data (see Figure 4 , right), eight described recruitment or sampling of "patients" and "people with specific mental health conditions." The remaining records primarily captured data from individuals who were described as "normal users," "healthy subjects," "students", or "older adults" [ 44 , 67 , 139 , 140 , 153 , 168 , 179 , 193 , 201 , 217 , 218 , 222 ], or for whom data was sampled from public social media (n = 8, plus 1 record that also includes a diagnostic sample [ 53 ]). One record further collected audio data from mental health professionals (MHPs) [ 78 ].…”

Section: Source and Scale Of Mental Health Data ML Algorithms Build mentioning

confidence: 99%

“…These works extracted data from public posts, mostly using specific Reddit or Twitter APIs [ 33 , 89 , 141 ]. Only in one instance, there was direct engagement with social media users to recruit individuals with clinically assessed schizophrenia from inpatient and outpatient psychiatric departments [ 53 ]. For mental health diagnosis or the detection of specific mental health states, these works primarily prospected for different types of "diagnostic signals" in online social behaviors that can be grouped into the following: (i) affiliation behaviors , (ii) self-report , and (iii) external validation (see framework by [ 53 ]) .…”

Section: Data Collection and Related Conceptualizations Of Mental Heamentioning

confidence: 99%

See 4 more Smart Citations

Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

254

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High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multidisciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

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