Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study (Preprint)

Abstract

BACKGROUND Suicide stands as a global public health concern, with a pronounced impact, especially in low- and middle-income countries (LMICs). In many LMICs, suicide remains largely unnoticed as a significant health concern, leading to delays in diagnosis and intervention. South Asia, in particular, has seen limited development in this area of research, and applying existing models from other regions is challenging due to cost constraints and the region's distinct linguistics and behavior. Social media analysis, notably on platforms like Facebook, offers the potential for detecting Major Depressive Disorder (MDD) and aiding individuals at risk of suicidal ideation. OBJECTIVE This study primarily focuses on South Asian countries. It aims to construct a predictive model for suicidal ideation by incorporating unique, unexplored features from both public and private Facebook profiles. Moreover, the research aims to fill the existing research gap by addressing the distinct challenges posed by South Asia's unique behavioral patterns, socioeconomic conditions, and linguistic nuances. Ultimately, this research strives to enhance suicide prevention efforts in the region by offering a cost-effective solution. METHODS This quantitative research study will gather data through an online platform. Initially, participants will be asked a few demographic questions along with the PHQ-9 assessment. Eligible participants who provide consent will receive an email requesting them to upload a zip file containing their Facebook data. The study will begin by determining if Facebook is the primary application for the participants, based on their active hours and Facebook usage duration. Subsequently, the predictive model will incorporate a wide range of previously unexplored variables, including anonymous postings, as well as textual analysis features such as captions, bio information, group memberships, preferred pages, interactions with advertisement content, and search history. The model will also analyze the use of emojis and the types of games participants engage with on Facebook. RESULTS Our system is expected to automatically detect the depressed post by analyzing the text and usage pattern of the behavior with the best accuracy possible. Ultimately, our research aims to have practical utility in identifying individuals who may be at risk of depression or in need of mental health support. CONCLUSIONS This initiative aims to enhance engagement in suicidal ideation medical care in South Asia with the goal of improving health outcomes. It is set to be the first study to consider predicting participants' primary social application usage before analyzing their content to forecast behavior and mental states. The study holds the potential to revolutionize strategies and offer insights for scalable, accessible interventions, while maintaining quality through comprehensive Facebook feature analysis.