A Cross-Cultural/Lingual Natural Language Processing Analysis of Disability Awareness on Social Media

This paper examines cross-lingual and cross-cultural variations in disability discourse on social media, specifically on X (formerly Twitter). Despite the growing presence of disability-related discussions online, empirical evidence remains limited regarding the perception, accessibility, and advocacy for people with disabilities in non-English-speaking countries of the Global South. Utilizing Natural Language Processing (NLP), this research analyzes three years of Arabic and English disability-related posts, incorporating two hashtags, #peoplewithdisabilities and #disability. The study investigates posting behaviors, engagement patterns, sentiment, and empathy-driven linguistic differences, while also assessing the cultural factors shaping these narratives. The findings show that there are notable linguistic and thematic differences between the posts in English and Arabic across different continents. They also highlight how these relate to disability theories and offer insights for policy makers and advocates on how to effectively study, track, and design for disability awareness in the global south.