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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Rethinking news trust in post-truth Turkey: immediacy as the imagined affordance of television and search engines(SAGE PUBLICATIONS INC, 2024) Department of Media and Visual Arts; Department of Media and Visual Arts; Çamurdan, Suncem Koçer; Ünal, Nazlı Özkan; College of Social Sciences and HumanitiesIn today's post-truth world, news users grapple with the tension between growing distrust in news institutions and the need for "true" information. Based on a mixed-methods study conducted in Turkey, this paper examines strategies developed by news users to establish trust in media tools in the context of the COVID-19 pandemic and populist polarization. We first collected data with a nationally representative survey (N = 1089). Then, 30 media users filled out media diaries for 1 week. We interviewed diary participants at the end of the week. We also conducted a four-week-long participant observation in three locations. Based on this data, we argue that users build trust in news stories by attributing a sense of immediacy to specific media, namely television and search engines. This immediacy arises from people's desire to scrutinize the accuracy of news stories in Turkey's highly polarized media environment. We term this ascribed meaning of transparency the imagined affordance of immediacy, asserting that immediacy is crucial for forming trust in the post-truth era. Contrary to suggestions that news trust is diminishing in the post-truth era, our paper highlights citizens' creative strategies to reestablish trust in contemporary news media.Publication Metadata only Exploring users interested in 3D food printing and their attitudes: case of the employees of a kitchen appliance company(Taylor and Francis inc, 2022) N/A; N/A; Department of Sociology; Department of Media and Visual Arts; Department of Sociology; Department of Media and Visual Arts; Kocaman, Yağmur; Mert, Aslı Ermiş; Özcan, Oğuzhan; PhD Student; Faculty Member; Faculty Member; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 125323D Food Printing (3DFP) technology is expected to enter homes in the near future as a kitchen appliance. on the other hand, 3DFP is perceived as a non-domestic technology by potential users and domestic users' attitudes and everyday habits received less attention in previous 3DFP research. Exploring their perspective is needed to reflect their daily kitchen dynamics on the design process and discover possible new benefits situated in the home kitchen. on this basis, this study focuses on finding potential 3DFP users and explores their attitudes towards using 3DFP technology in their home kitchens through a two-stage study: First, we prioritized potential users based on their relationship with food through a questionnaire and found six factors that positively affect their attitude towards 3DFP: cooking every day, ordering food less than once a month, eating out at least a couple of times a month, having a mini oven, A multicooker, or a kettle, liking to try new foods, thinking that cooking is a fun activity. Second, we conducted semi-structured interviews with seven participants to discuss the possible benefits and drawbacks of 3DFP technology for their daily lives in the kitchen. Results revealed two new benefits that 3DFP at home may provide: risk-free cooking and cooking for self-improvement. We discuss the potential implications of these two benefits for design and HCI research focusing on how to facilitate automation and pleasurable aspects of cooking into future 3DFP devices.Publication Metadata only Machine learning-enabled optimization of extrusion-based 3D printing(Academic Press Inc Elsevier Science, 2022) N/A; Department of Media and Visual Arts; Department of Mechanical Engineering; Department of Media and Visual Arts; Department of Mechanical Engineering; Dabbagh, Sajjad Rahmani; Özcan, Oğuzhan; Taşoğlu, Savaş; PhD Stud; ent; Faculty Member; Faculty Member; Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Sciences and Engineering; College of Social Sciences and Humanities; College of Engineering; N/A; 12532; 291971Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.