Early Detection of Autism Spectrum Disorder through AI-Powered Analysis of Social Media Texts

Detecting individuals with autism spectrum disorder (ASD) remains a challenge due to the resources and specialized professionals needed for accurate diagnosis, particularly for children where time is a critical factor. Early diagnosis of ASD is crucial for improving the quality of life for affected individuals, as it allows for timely intervention and support. In this study, we underscore the importance of artificial intelligence (AI) in developing innovative diagnostic methods, with the primary objective of creating AI models that assist in identifying users who may have ASD. Although several studies have utilized traditional machine learning (ML) and deep learning (DL) techniques to detect various illnesses, few have focused on detecting ASD using text as input. We employ natural language processing (NLP) techniques combined with AI models, specifically decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN) as ML models, and bidirectional encoder representations from transformers (BERT) as DL models. The core idea involves extracting tweets from Twitter users through the platform's API, classifying the texts as written by individuals who claim to have ASD (ASD users) or by those without ASD (non-ASD users). We generated a dataset of 404,627 tweets and used a subset of 90,000 tweets, comprising 45,000 from each classification group, for training and testing the models. The results demonstrate a predictive model with an accuracy of over 84% when classifying texts potentially originated from ASD users. This research paves the way for using DL models to enhance the accuracy of detecting and diagnosing ASD in individuals effectively, emphasizing the critical role of AI in advancing early diagnostic methods for better patient outcomes.