Machine learning approaches to identifying neurodevelopmental disorders using social media data: a systematic review
Sina Rashti Mohammad1, Fatemeh Kadaei1, Mana Mohkam2
1School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran.
Insights
Artificial intelligence (AI) and machine learning (ML) can analyze social media data for early detection of neurodevelopmental disorders (NDDs), like autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). These AI tools show promise for scalable, non-invasive screening, complementing traditional diagnostics.
Area of Science:
- Computational psychiatry and neuroimaging
- Digital phenotyping and behavioral analytics
- Machine learning applications in healthcare
Background:
- Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), significantly impact childhood development and functioning.
- Early identification of NDDs is crucial for improving long-term outcomes, but traditional diagnostic methods are often time-consuming and resource-intensive.
- Social media platforms generate vast amounts of user data, offering a novel, large-scale source for behavioral and linguistic insights potentially aiding early NDD detection.
Purpose of the Study:
- To systematically review studies employing artificial intelligence (AI) or machine learning (ML) to analyze social media data for the detection of neurodevelopmental disorders (NDDs).
- To assess the performance and identify trends in AI/ML-based social media analysis for NDD screening.
- To highlight the potential and limitations of using user-generated social media content for scalable, non-invasive NDD detection.
Main Methods:
- A comprehensive literature search was conducted across five major scientific databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library) up to July 2025.
- Inclusion criteria focused on studies utilizing AI or ML techniques to analyze social media data for NDD detection.
- Data extraction encompassed platform type, targeted NDD, dataset size, ML methodology, and reported diagnostic performance metrics (e.g., F1-scores).
Main Results:
- Nineteen studies met the inclusion criteria, primarily focusing on ASD and ADHD using platforms like Reddit, Twitter, YouTube, and Facebook.
- ML models demonstrated moderate to high classification performance, with F1-scores ranging from 0.48 to 0.89, varying by data type and disorder.
- Video analysis showed particular promise for identifying nonverbal behavioral markers; however, research on other NDDs is limited, and methodological challenges persist.
Conclusions:
- AI-driven analysis of social media data presents a promising avenue for scalable and non-invasive screening of neurodevelopmental disorders.
- Current research predominantly targets ASD and ADHD, necessitating expansion to underrepresented NDDs and addressing data validity, bias, and privacy concerns.
- These AI tools have the potential to serve as valuable complements to conventional diagnostic approaches for NDDs.
Background:
Neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), often emerge in early childhood and can significantly impact social, academic, and emotional functioning. Early identification is critical to improving long-term outcomes, yet traditional diagnostic processes are time-consuming and resource-intensive. As social media becomes an integral part of daily life, user-generated content offers a novel source of behavioral and linguistic data that may support early detection. Advances in artificial intelligence (AI) and machine learning (ML) now make it possible to analyze these large-scale data streams for clinical insights.
Methods:
A comprehensive search was performed across five databases-PubMed, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library-from inception up to July 2025. Studies were included if they used AI or ML methods to analyze social media data for detecting NDDs. Data extraction focused on platform type, targeted disorder, dataset size, ML technique, and diagnostic performance.
Results:
Nineteen studies met the inclusion criteria. Most focused on ASD and ADHD, using platforms such as Reddit, Twitter, YouTube, and Facebook. ML models achieved moderate to high classification performance, with F1-scores ranging from approximately 0.48 to 0.89 depending on the data type and disorder. Video-based models showed particular promise in identifying nonverbal behavioral markers. However, research on other NDDs remains limited, and methodological heterogeneity, small sample sizes, and ethical challenges persist.
Conclusion:
AI-driven analysis of social media data holds significant promise for scalable, non-invasive screening of neurodevelopmental disorders. While current work largely focuses on ASD and ADHD, future research should extend to underrepresented NDDs and address concerns related to data validity, bias, and privacy. With continued advancement, these tools may serve as valuable complements to traditional diagnostic methods.
