Artificial Intelligence-Based Sentiment Analysis and Qualitative Analysis of Turkish X Posts about Down syndrome

Objective: This study aims to analyze Turkish X messages about Down syndrome with AI-based sentiment analysis and qualitative analysis. Method: The design of the study was mixed method. Using the criterion sampling technique, the sample of the study consists of 73,840 posts searched with the hashtag “Down syndrome”. Data extracted using Tweepy Package and feature extraction was performed using BERT method. Sentiment analysis was classified with artificial neural networks. The 738 posts with the most likes and shares analyzed according to Colaizzi's phenomenological analysis steps. Results: Among the Turkish messages about Down syndrome, 30.23% were negative, 39.87% were neutral and 29.90% were positive. The findings obtained from Turkish X messages about Down syndrome were grouped under four themes: ‘Social Stigmatisation’, ‘Awareness and Support’, ‘Representation in Media’ and ‘Supportive and Solidarity-Oriented Posts on Social Media’. Conclusion: Approximately one-third of the Turkish messages about Down syndrome analyzed with artificial intelligence-based sentiment analysis contain negative sentiments. In addition, Down syndrome perceived as a disease rather than a genetic disorder and individuals identified with stereotypical identities such as “happy” or “angel”. The term “Down syndrome” continues to be used as an insult and individuals discriminate against people with Down syndrome. While their participation in daily life portrayed as extraordinary in the media, awareness and support content appears on social media and families share stories about their experiences. Non-governmental organizations emphasize that Down syndrome is a genetic difference and success stories support social participation. In addition, the social media solidarity network makes the demands for rights visible.