TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/9
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Article Türkiye’de Yapılmış Damgalama ile İlgili Yayınların Birliktelik Kuralına Göre Bibliyometrik Analizi(2022) Dikec, Gul; Saritas, Merve; Oban, VolkanBu çalışmada Türkiye’de yayınlanan ve ULAKBİM TR Dizin ile Türk Psikiyatri Dizininde yer alan, damgalama anahtar kelimesi içeren çalışmalar bibliyometrik yönden incelendi. Çalışma kapsamında toplam 143 çalışma değerlendirildi. İncelenen çalışmaların %80,4’ünün araştırma makalesi olduğu, sıklıkla tanımlayıcı desende yapıldığı belirlendi. Çalışmaların %65’inin toplumsal damgalama türü ile ilişkili olduğu, sıklıkla psikiyatri hemşireliği araştırma alanında yapıldığı ve en sık Psikiyatri Hemşireliği Dergisinde yayınlandığı belirlendi. Yapılan birliktelik kuralı analizine göre damgalama anahtar kelimesinin en sık ruhsal bozukluk, içselleştirilmiş damgalama, ruhsal bozukluğu olan birey ve bulaşıcı hastalık ile birlikte kullanıldığı belirlendi. İncelenen çalışmaların sıklıkla ruhsal bozukluklar ile ilişkili tanımlayıcı desende yapıldığı düşünüldüğünde, ruhsal bozukluklara yönelik damgalamanın azaltılmasını hedefleyen deneysel çalışma sayısının artırılması önerilebilir.Article Citation - WoS: 4Citation - Scopus: 5Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related To Schizophrenia(Turkiye Sinir ve Ruh Sagligi dernegi, 2023) Dikec, Gul; Oban, Volkan; Usta, Mirac BarisObjective: The aim of this study was to qualitatively examine Turkish tweets about schizophrenia in respect of stigmatization and discrimination within a one-month period and to conduct emotional analysis using artificial intelligence applications. Method: Using the keyword 'schizophrenia,' Turkish tweets were gathered from the Python Tweepy application between December 19, 2020 and January 18, 2021. Features were extracted using the Bidirectional Encoder Representations from Transformers (BERT) method and artificial neural networks and tweets were classified as positive, neutral, or negative. Approximately 5% of the tweets were qualitatively analyzed, constituting those most frequently liked and retweeted. Results: The study found that, of the total of 3406 schizophrenia-related messages shared in Turkey over a period of one-month, 2996 were original, and were then retweeted a total of 1823 times, and liked by 25,413 people. It was determined that 63.4% of the tweets shared about schizophrenia contained negative emotions, 28.7% were neutral, and 7.71% expressed positive emotions. Within the scope of the qualitative analysis, 145 tweets were examined and classified under four main themes and two sub-themes; namely, news about violent patients, insult (insulting people in interpersonal relationships, insulting people in the news), mockery, and information. Conclusion: The results of this study showed that the Turkish tweets about schizophrenia, which were emotionally analyzed using artificial intelligence were found often to contain negative emotions. It was also seen that Twitter users used the term schizophrenia, not in a medical sense but to insult and make fun of individuals, frequently shared the news that patients were victims or perpetrators of violence, and the messages shared by professional branch organizations or mental health professionals were primarily for conveying information to the public.