The formation of autism advocacy organisations led by family members of autistic individuals led to intense criticism from some parts of the autistic community. In response to what was perceived as a misrepresentation of their interests, autistic individuals formed autistic self-advocacy groups, adopting the philosophy that autism advocacy should be led 'by' autistic people 'for' autistic people. However, recent claims that self-advocacy organisations represent only a narrow subset of the autistic community have prompted renewed debate surrounding the role of organisations in autism advocacy. While many individuals and groups have outlined their views, the debate has yet to be studied through computational means. In this study, we apply machine learning and natural language processing techniques to a large-scale collection of Tweets from organisations and individuals in autism advocacy. We conduct a specification curve analysis on the similarity of language across organisations and individuals, and find evidence to support claims of partial representation relevant to both self-advocacy groups and organisations led by non-autistic people. In introducing a novel approach to studying the long-standing conflict between different groups in the autism advocacy community, we hope to provide both organisations and individuals with new tools to help ground discussions of representation in empirical insight.