Down Sendromuna Yönelik Türkçe X İletilerinin Yapay Zekâya Dayalı Duygu Çözümlemesi ve Niteliksel Olarak İncelenmesi

Öz

Amaç: Bu çalışmada Down sendromuna yönelik Türkçe X iletilerinin yapay zekâya dayalı duygu çözümlemesi ve niteliksel olarak incelenmesi amaçlanmıştır. Yöntem: Çalışmanın tasarımı karma yöntemdir. Ölçüt örnekleme tekniği kullanılan araştırmanın örneklemini “Down sendromu” anahtar kelimesiyle taranan 73.840 gönderi oluşturmuştur. Veriler Tweepy Paketi kullanılarak elde edilmiş olup BERT yöntemi kullanılarak özellik çıkarımı gerçekleştirilmiştir. Duygu analizi yapay sinir ağlarıyla sınıflandırılmıştır. En fazla beğeni ve paylaşım alan 738 gönderi Colaizzi’nin fenomenolojik analiz basamaklarına göre analiz edilmiştir. Bulgular: Down sendromu ile ilgili paylaşılan Türkçe iletilerin %30.23’ünün olumsuz, %39.87’sinin nötr ve %29.90’ının olumlu duygular içerdiği belirlenmiştir. Down sendromuna yönelik Türkçe X iletilerinden elde edilen bulgular “Sosyal Damgalama”, “Farkındalık ve Destek”, “Medyada Temsiliyet” ve “Sosyal Medyada Destekleyici ve Dayanışmacı Paylaşımlar” olmak üzere dört tema altında toplanmıştır. Sonuç: Bu çalışmada yapay zekâ temelli duygu çözümlemesi ile incelenen Down sendromuyla ilgili Türkçe iletilerin yaklaşık üçte birinin olumsuz duygular içerdiği saptanmıştır. Ayrıca, Down sendromu, genetik bir bozukluk yerine hastalık olarak algılanmakta ve bireyler "mutlu" ya da "melek" gibi kalıplaşmış kimliklerle tanımlanmaktadır. "Down sendromlu" ifadesi hakaret amaçlı kullanılmakta, bu bireyler ayrımcılığa uğramaktadır. Medyada gündelik yaşama katılımları olağanüstü gösterilirken, sosyal medyada farkındalık ve destek içeriği bulunmakta, aileler deneyim paylaşmaktadır. Sivil toplum kuruluşları Down sendromunu genetik bir farklılık olarak vurgulamakta, başarı öyküleri toplumsal katılımı desteklemektedir. Ayrıca, sosyal medyada oluşturulan dayanışma ağı hak taleplerini görünür kılmaktadır.

Anahtar Kelimeler

• Down sendromu
• doğal dil işleme
• makine öğrenmesi
• sosyal medya
• sosyal damgalama

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

Abstract

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.

Keywords

• Down syndrome
• natural language processing
• machine learning
• social media
• social stigmatization

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