[Paper Review] Big Data and Social/Medical Sciences: State of the Art and Future Trends

Adil Rajput, Samara M. Ahmed|arXiv (Cornell University)|Feb 2, 2019

Mental Health via Writing23 references10 citations

TL;DR

This paper reviews the state of the art in leveraging Big Data from social media for social and medical sciences, focusing on extracting clinical insights into mental health. It outlines technological and methodological frameworks for analyzing vast user-generated data to infer psychological states, offering guidelines for future research in digital phenotyping and mental health monitoring.

ABSTRACT

The explosion of data on the internet is a direct corollary of the social media platform. With petabytes of data being generated by end users, the researchers have access to unprecedented amount of data (Big Data). Such data provides an insight into user mental state and hence can be utilized to produce clinical evidence. This lofty goal requires a thorough understanding of not only the mental health issues but also the technology trends underlying the Big Data and how they can be leveraged effectively. The paper looks at various such concepts, provides an overview and enumerates the work that has been done in this realm. Furthermore, we provide guidelines for future work that will help in streamlining the Big Data use in social/medical sciences.

Motivation & Objective

• To examine the current state of Big Data applications in social and medical sciences, particularly in mental health research.
• To identify technological and methodological challenges in extracting clinically relevant insights from user-generated online content.
• To analyze existing studies that use Big Data to infer mental health conditions such as depression and anxiety.
• To provide actionable guidelines for future research in digital phenotyping and large-scale behavioral analysis.
• To bridge gaps between data science, mental health, and ethical considerations in Big Data research.

Proposed method

• Systematic review of existing literature on Big Data applications in social and medical sciences.
• Analysis of data sources such as social media platforms, online forums, and mobile app logs generating petabytes of user-generated content.
• Application of natural language processing (NLP) and machine learning techniques to detect linguistic markers of mental health conditions.
• Integration of computational social science methods with clinical psychology frameworks to validate behavioral indicators.
• Evaluation of data quality, privacy, and ethical concerns in large-scale data collection and analysis.
• Development of a conceptual framework for future research on digital phenotyping and real-time mental health monitoring.

Experimental results

Research questions

• RQ1 How can Big Data from social media be effectively used to infer mental health states such as depression or anxiety?
• RQ2 What are the key technological and methodological challenges in analyzing large-scale, unstructured online user data for clinical insights?
• RQ3 What existing studies demonstrate successful application of Big Data in detecting mental health patterns?
• RQ4 How can ethical and privacy concerns be addressed when collecting and analyzing sensitive behavioral data?
• RQ5 What guidelines are needed to standardize and improve the reliability of Big Data applications in mental health research?

Key findings

• Big Data from social media provides unprecedented access to real-time behavioral and linguistic patterns linked to mental health conditions.
• Existing studies demonstrate that machine learning models can detect signs of depression and anxiety with moderate to high accuracy using linguistic features.
• The integration of NLP and computational social science enables the identification of digital phenotypes associated with psychological disorders.
• Despite progress, challenges remain in data quality, representativeness, and ethical data use, particularly regarding informed consent and privacy.
• There is a clear need for standardized frameworks and interdisciplinary collaboration to advance reliable and reproducible Big Data research in mental health.
• The paper identifies a gap in longitudinal validation studies and calls for more research on causal inference in digital behavioral data.