Analysis Of Twitter Data For Public Health Surveillance And Precision Diagnostics Of Autism Spectrum Disorder

Abstract

The healthcare industry is a prolific source of data, with every patient record, clinical trial, drug test, and medical research generating copious amounts of information. Consequently, the interest in using machine learning algorithms in healthcare applications has increased dramatically, with numerous breakthroughs being made. One such application is using social media to study and understand public health. With millions of users sharing their thoughts, exchanging ideas, and providing health-related information on various social media platforms, researchers and clinicians can conduct studies on diseases and associated symptoms in natural settings by establishing digital phenotypic biomarkers. Twitter is one such platform that has proven to be an exceptional source of health-related information from both public and health officials. In this study, we aim to mine data related to "autism" from " #ActuallyAutistic" tweets on Twitter. The textual differences in social media communications can help identify various behavioral symptoms, which can be used to distinguish an autistic individual from their typical peers. We were able to scrape a total of 6,469,994 tweets from approximately 70,000 individual users. We illustrate the usefulness of the dataset through simple applications such as: sentiment analysis, text classification and topic modeling. Our classifier achieved an accuracy of 73%, which is consistent with previous studies published in Nature Digital Medicine journal where using different modality data such as eye gazing and facial expressions, the authors achieved a similar accuracy. The collected data will also be released publicly to help accelerate scientific research in this field. This sharing of data and interdisciplinary research can enhance its analytical capacity and enable medical practitioners to make more informed decisions.