Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes

Abstract

Background The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users’ self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression. Methods We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies. Discussion We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media. Systematic review registration International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).

Social media information in assessment and implications for minoritized social identities

“…Jacobson and Gruzd (2020) posit that social media screening has introduced new forms of discrimination, such as evaluating a candidate's influence based on the size of their social network and using photo scanners to predict the mental well-being of a candidate. Algorithms have been designed to predict with high accuracy whether someone is suffering from a mental disorder such as depression, suicide ideation, and schizophrenia (Chancellor et al, 2019;Owusu et al, 2021). Such advanced algorithms can be used to evaluate mental health based on image characteristics such as colorfulness, sharpness, naturalness, and facial presentation (Owusu et al, 2021).…”

“…Algorithms have been designed to predict with high accuracy whether someone is suffering from a mental disorder such as depression, suicide ideation, and schizophrenia (Chancellor et al, 2019;Owusu et al, 2021). Such advanced algorithms can be used to evaluate mental health based on image characteristics such as colorfulness, sharpness, naturalness, and facial presentation (Owusu et al, 2021). Although these efforts are laudable because they drive public health efforts to understand and design interventions for mental health disorders, the use of such algorithms in using social media information to assess applicants could promote selection biases and Type I errors (concluding falsely that a candidate is mentally ill when they are not).…”