#ActuallyAutistic Twitter dataset for precision diagnosis of Autism Spectrum Disorder (ASD)

Abstract

The increasing usage of social media platforms has given rise to an unprecedented surge in user-generated content with millions of users sharing their thoughts, experiences, and health-related information. Because of this social media has turned out to be a useful means to study and understand public health. Twitter is one such platform that has proven to be a valuable source of such information for both public and health officials. We present a novel dataset consisting of 6,515,470 tweets collected from users self identifying with autism using "#ActuallyAutistic" and a control group. The dataset also has supporting information such as posting dates, follower count, geographical location, and interaction metrics. We illustrate the utility of the dataset through common Natural Language Processing (NLP) applications such as sentiment analysis, tweet and user classification, and topic modeling. The textual differences in social media communications can help researchers and clinicians to conduct symptomatology studies, in natural settings, by establishing effective biomarkers to distinguish an autistic individual from their typical peers. For better accessibility, reusability and new research insights, we have released the dataset publicly.