Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/7
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Article Tween Jargon "mean Girls" and Beauty Bullying;(Cyprus International University, 2021) Güzel, E.The destructive emotions imposed on women by patriarchal ideology, such as dependence on appearance, vanity, narcissism, and competitiveness are also capturing girls; children under the age of 13 sharing adult-looking posts on Instagram under 2 million "tween" tags although it is illegal; cyberbullying and the words used in social media and the interaction of phrases with spoken language are the main problems of tween. In this study, grounded theory and in-depth interviews are used as a qualitative research method, beauty bullying is defined with the movie Mean Girls, and the "tween jargon" that can be clearly seen in social media is reflected in the spoken language. For example, words and abbreviations used by children such as tag, "break my scale", "send ss/dm", "bro/sis", "feno", "efso" are also used in daily language. As a result, the expression of tween jargon with the way of life of these children determines the increase of beauty cyberbullying, competition and language degeneration of this new girl culture. In addition, it is predicted that this study will contribute not only to the corruption of language but also literature in terms of childhood degeneration. © 2021 Cyprus International University. All rights reserved.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.