WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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Article Content and Quality Analysis of YouTube Videos on Therapeutic Exercises for Parkinson’s Disease(SAGE Publications Inc, 2026) Tosun, Anıl; Reyhanioglu, Duygu AktarBackground Parkinson's disease (PD) is the most common movement disorder, and patients increasingly use YouTube to obtain health-related information. Objective This study aimed to assess the content quality and informational reliability of YouTube videos on PD exercises. Methods A total of 150 English-language YouTube videos were screened using the search terms Parkinson exercises, Parkinson physiotherapy exercises, and Parkinson home exercise program. For each video, the source, upload date, number of views, likes, dislikes, and comments were recorded. The Video Power Index (VPI) was assessed using the view ratio (views/day) and like ratio (likes & times; 100 / [likes + dislikes]). The clinical quality, reliability, and educational value of PD-specific exercise videos were assessed using the Global Quality Scale (GQS), modified DISCERN (mDISCERN), and guideline-based criteria derived from the European Physiotherapy Guideline for Parkinson's Disease (PD-GEC).Results A total of 29 videos met the inclusion criteria and were analyzed. Videos explaining how and why exercises were performed demonstrated higher mDiscern and GQS scores, while providing repetition, duration, and intensity information was associated with higher GQS scores but not mDiscern (p = 0.080); no differences were observed for disease specificity, functional linkage, or safety warnings (all p > 0.05). PD-GEC scores were not significantly related to video engagement metrics. Conclusion Higher-quality videos tended to provide clear explanations of exercise rationale and dosage, while guideline-based clinical features, including PD-GEC criteria, were not associated with viewer engagement.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.Article Citation - WoS: 3Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Twitter Messages Related To Autism Spectrum Disorders(Springernature, 2023) Göksel, Pelin; Oban, Volkan; Dikec, Gul; Usta, Mirac BarisBackground: 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.