Stigma towards persons with disability has been highlighted as one predicting factors for overdiagnosis and more negative prognoses. The extent to which disability equality policies and practices are adopted in any given Southeast Asia societies is dependent upon the ability to capture in a real-time the extremely rapid and massive context of current societal dynamics. The mode of communication, with the support of increasingly affordable digital devices, brings communities into a new form of interaction in social media. As a source of data, the patterns derived from these social media are extremely valuable, especially to support decision-making processes. Data-driven policies are capable of suppressing errors because the target is a measurable goal. Characteristics of specific digital data requires a special approach given the huge volume of data (big data). Related to this research, the applied platform is social listening. Big data processing is conducted through the (1) data mining stage (Twitter API) using six technical terms i.e., “disabilitas” (disability), “difabel” (different ability), “cacat fisik” (physical disability), “cacat genetik” (genetic disability), “gangguan mental” (mental disorder), “gangguan jiwa” (soul illness), “penyandang cacat” (persons with disability), “retardasi mental” (mental retardation), and “skizofrenia” (schizophrenia), (2) data processing, (3) pattern evaluation, and (4) data visualization (Tableau and Gephi software). Findings from this study is expected to overcome the limitations of the tradition of stigma measurement with surveys and interviews that are vulnerable to the tendency of the subject to respond normatively.
Keywords: physical disability; mental disability; stigma; big data; social listening