Browsing by Author "Oban, Volkan"

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Citation Count: 0
Bibliometric Analysis of Publications on Stigmatization in Psychiatric Nursing Literature
(Kare Publ, 2024) Dikeç, Gül; Saritas, Merve; Oban, Volkan; Hemşirelik Bölümü
Objectives: In the past two decades, the number of publications on stigma has increased in the literature. This study aimed to conduct a bibliometric analysis of publications related to stigmatization in the psychiatric nursing literature. Methods: In this study, a search was performed on the PubMed database on September 11, 2022, with the Medical Searching Terms "(Stigmatization [Title OR Abstract] OR Social Stigma [Title OR Abstract]) OR (Stigma [Title OR Abstract] OR Stereotyping [Title OR Abstract] OR Discrimination [Title OR Abstract]) AND (Psychiatric Nursing [Title OR Abstract] OR Nursing [Title OR Abstract])." Between 1990 and 2022, 10,571 studies published in English, available in full text, and published in journals indexed with SCI, SSCI, and ESCI were found. Results: The number of published articles reached the highest number in 2020, with an increase of 4.05 times in 30 years; it was determined that 92.8% of the publications were of the descriptive study, and Happell was the most productive author in this field. Frequently, articles were published in the Journal of Psychiatric and Mental Health Nursing (n=762), Journal of Psychosocial Nursing and Mental Health Services (n=550), International Journal of Mental Health Nursing (n=480), Issues in Mental Health Nursing (n=445), and Journal of Advanced Nursing (n=429). It was determined that the top five most frequently repeated keywords were humans, female, psychiatric nursing, male, and adult, respectively. Conclusion: The findings obtained from this study can provide information about the number of publications, research types, researchers, and institutions, as well as give ideas for new research strategies in psychiatric nursing literature. Establishing cooperation between institutions and authors can guide psychiatric nurses in creating projects to reduce stigma.
Citation Count: 1
Qualitative and Artificial Intelligence-Based Sentiment Analyses of Anti-Lgbti Plus Hate Speech on Twitter in Turkey
(Taylor & Francis inc, 2023) Dikeç, Gül; Oban, Volkan; Dikec, Gul; Hemşirelik Bölümü
The aim of this study was to evaluate hate speech in Turkish LGBTI+-related tweets during a one-month period of artificial intelligence-based sentiment analyses. Turkish tweets related to LGBTI+, were retrieved using Python library Tweepy and were evaluated by sentiment analysis. The researchers then performed a qualitative analysis of the most frequently liked and retweeted tweets (n = 556). Sentiment analysis revealed that 69.5% of tweets were negative, 23.3% were neutral, and 7.2% were positive. The qualitative analysis was grouped under seven themes: LGBTI+ Club; Terrorism and Terrorist Organization Membership; Perversion, Illness, Immorality; Presence in History; Religious References; Insults; and Humiliation. The results of this study show that anti-LGBTI+ hate speech in Turkey is significant in terms of both quality and quantity. As LGBTI+ individuals are at risk for excess mental distress and disorders, it is important to understand the risks and other factors that ameliorate stress and contribute to mental health in social media.
Citation Count: 2
Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related To Schizophrenia
(Turkiye Sinir ve Ruh Sagligi dernegi, 2023) Dikeç, Gül; Oban, Volkan; Usta, Mirac Baris; Hemşirelik Bölümü
Objective: 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.
Citation Count: 1
Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Twitter Messages Related To Autism Spectrum Disorders
(Springernature, 2023) Dikeç, Gül; Oban, Volkan; Dikec, Gul; Usta, Mirac Baris; Hemşirelik Bölümü
Background: The aim of our study was to conduct an emotional analysis of Turkish Twitter messages related to autism spectrum disorders (ASD). Methods: An emotion analysis was performed using quantitative and qualitative analysis methods on Turkish Twitter messages shared between November 2021 and January 2022 that contained the words "autism" and "autistic." Results: It was found that 81.5% of the 13,042 messages that constituted the sample of this study contained neutral emotions. The most frequently used words in Twitter messages were autism, a, universe, strong, patience, warriors, and happy. The qualitative analysis revealed three main themes. These themes were: "experiences," "informing society and awareness," and "humiliation." Conclusion: In this study, it was found that Turkish Twitter messages related to autism, which were analyzed using artificial intelligence-based emotion analysis, often contained neutral emotions. While the content of these messages, often shared by parents, was related to experiences, and the messages shared by pediatric psychiatrists and rehabilitation center employees were informative in nature, it was determined that the word "autism" was used to insult, which is outside of its medical meaning.