https://hdl.handle.net/20.500.12809/68 2026-04-17T11:05:01Z https://hdl.handle.net/20.500.12809/11076 Sahte Popülerliğin Sosyal Medyada Algı ve Etkileşim Üzerindeki Etkisi: Instagram Üzerinde Bir Vaka Çalışması Akın, Hasan Eren; İlkuçar, Muammer Bu çalışma, dijital medya ortamında sahte popülerlik içeriklerinin sosyal medya kullanıcılarının algı, güven ve etkileşim davranışları üzerindeki etkisini deneysel bir yaklaşımla incelenmiştir. Instagram platformunda özel olarak oluşturulan ve bot takipçilerle desteklenen bir hesap üzerinden yürütülen deneysel süreçte hem gerçek hem de sahte içeriklerin kullanıcılar üzerindeki etkileri analiz edilmiştir. Çalışma kapsamında bir ay boyunca paylaşılan 44 Reels videosu, toplamda üç milyonu aşkın görüntülenme ve yüz binlerce etkileşim almış, kullanıcı beğeni, paylaşım, yorum, kayıt etme gibi veriler nicel ve nitel olarak değerlendirilmiştir. Yapılan analize göre; kullanıcıların büyük oranda sahte popüler içeriklere duyduğu güveni, eleştirel sorgulamanın zayıflığını, içeriklerin kaynağı ve doğruluğunun araştırılmadığı, kullanıcıların kolaylıkla manipüle edilebileceği ve sosyal medya algoritmalarının manipülasyona açık olduğu gibi sonuçlara varılabilir. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/11050 A configurational approach for analyzing cultural values and performance in Global Virtual Teams Şahin, Faruk; Taras, Vas; Çetin, Fatih; Tavoletti, Ernesto; Askun, Duysal; Florea, Liviu Although there have been decades of research on the effect of cultural values on team effectiveness outcomes, knowledge of the interdependencies of team cultural values for explaining team performance remains nascent. Using a configurational qualitative approach, this study explores how cultural values combine and collectively contribute to the effectiveness of Global Virtual Teams (GVTs). We perform a fuzzy-set qualitative comparative analysis on a data set of 1847 individuals nested within 396 GVTs who participated in an international business consulting project. The results demonstrate that cultural values work together to achieve high levels of team performance rather than function independently. The results also show that different cultural value configurations could be equally effective at producing the same outcome, and that the presence of gender egalitarianism and the absence of power distance are the most important for producing the outcome. We discuss implications for practice and future research. 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/10845 Forecasting of COVID-19 Fatality in the USA: Comparison of Artificial Neural Network-Based Models Hatipoğlu, Veysel Fuat The first death caused by the novel coronavirus in the USA was declared on February 29, 2020, in the Seattle area in Washington state. Forecasting the number of deaths has great importance in terms of public psychology and strategic decisions to be taken by the government. There are several data-driven models in the literature to predict the deaths in the USA caused by COVID-19. However, most of them are based on a few variables of the data for forecasting. From this point of view, this study provides an artificial neural network (ANN)-based approach by considering 12 different variables for forecasting the cumulative deaths caused by COVID-19 in the USA. The proposed ANN structure was trained with three algorithms, namely scaled conjugate gradient algorithm, Levenberg-Marquardt algorithm and Bayesian regularization algorithm. These three forecasting models were constructed on 13 parameters such as 12 inputs and one output. The sensitivity and performance of the proposed forecasting models were analyzed and compared by using indices mean absolute error, mean absolute percentage error, correlation coefficient (R-value), sum square error, variance account for, mean square error and root-mean-square error. Results show that the forecasting model with Bayesian regularization performs better than other models for forecasting the cumulative deaths due to COVID-19 in the USA. 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/10836 What makes survival of heart failure patients? Prediction by the iterative learning approach and detailed factor analysis with the SHAP algorithm İlkuçar, Muammer; Çifci A.; Kirbaş I. Cardiovascular disease is the leading cause of global death and disability. There are many types of cardiovascular diseases. The diagnosis of heart failure, one of the cardiovascular disease types, is a challenging task and plays a significant role in guiding the treatment of patients. However, machine learning approaches can be helpful for assisting medical institutions and practitioners in predicting heart failure in the early phase. This study is the first application that analyzes the dataset containing clinical records of 299 patients with heart failure using a feedforward backpropagation neural network (NN). The aim of this study is to predict the survival of heart failure patients based on the clinical data and to identify the strongest factors influencing heart failure disease development. We adopted the Shapley additive explanations (SHAP) values, which have been used to interpret model findings. From the study, it is observed that the best and highest accuracy of 91.11% is obtained compared to previous studies and it is found that feedforward backpropagation NN performed better than the previous approaches. Also, this study revealed that time, ejection fraction (EF), serum creatinine, creatinine phosphokinase (CPK), and age are the strongest risk factors for mortality among patients suffering from heart failure. 2023-01-01T00:00:00Z