A Lexicon-Based Analysis of Public Knowledge Regarding the Causes and Interventions for Autism Spectrum Disorder

People around individuals with autism spectrum disorders, such as their families or teachers, use multiple sources of information, either concurrently or sequentially, to obtain autism-related information. Recently, social media platforms have become primary sources of information. However, these sources may provide information that is either accurate or inaccurate. In this study, information regarding the causes and interventions of autism spectrum disorder disseminated through X was examined and discussed in line with the literature. A total of 6,861 tweets posted between January 2000 and October 2022 were collected and filtered, resulting in 4,805 unique tweets. Using a lexicon-based factual classification approach, each tweet was labeled as accurate, inaccurate, or neutral based on its alignment with established scientific literature. Tweets labeled as neutral were subsequently excluded from further analysis, resulting in a final dataset of 3,114 tweets. The lexicon-based sentiment analysis revealed that 78.6% of tweets related to the causes of ASD were classified as inaccurate, while only 21.4% were classified as accurate. Similarly, 57.9% of tweets concerning interventions were inaccurate, whereas 42.1% were accurate. The autism-related information available on X reaches a broad audience. However, the findings highlight the persistent challenges in addressing misinformation about ASD on social media platforms, emphasizing the urgent need for strategies that foster accurate, evidence-based discourse.