WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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Article Social Media and Financial Decisions: The Influence of Socio-Demographics and Financial Literacy(John Wiley and Sons Inc, 2026) Altinbas, H.This study investigates the predictors of individuals' reliance on social media for financial decision-making within the context of Türkiye's high-inflation environment and the associated surge in retail investor participation. Data were collected via an online survey utilizing the OECD's financial literacy toolkit. The results indicate that gender, family structure, high-risk asset preferences, and financial literacy predict social media usage for financial information. Specifically, males, individuals who invest in stocks or cryptocurrencies, and those with higher financial literacy demonstrate a greater propensity to access financial information on social media; conversely, households with children exhibit lower reliance on social media information. © 2026 American Association of Family and Consumer Sciences.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.Article Citation - WoS: 3Data Mining the City: User Demands Through Social Media(Konya Technical Univ, Fac Architecture & design, 2021) Cakir, Hulya Soydas; Levent, Vecdi EmrePurpose Information technologies are commonly used in architectural and urban design. The use of these technologies providing support at every stage of the design opens up different perspectives for designers and users. The aim of the study is to obtain user demands for green spaces of a specific district by mining data through social media and to detect the actual green spaces of the same district using applications developed for this purpose. User demands for design decisions and applications of green spaces and the current situation of the study area are evaluated. Design/Methodology/Approach The research is firstly realized through social media, and data obtained from Twitter is analysed in order to evaluate user demands for parks and green spaces of Atasehir district in Istanbul City. Secondly, all green areas in the same district are detected by using digital maps. Two applications are specifically designed for this research; Tweet Grabber is used for user sentiment analysis on social media and Map Grabber is processed for extraction of green spaces via maps. The total area of the green spaces is compared with the desired area of open and green spaces per user. Findings The user demands and thoughts obtained in the study about the green spaces of the district are compatible with the actual situation of green spaces. It is observed that the users are mostly dissatisfied with the adequacy of green spaces. Designers, politicians, municipalities and all stakeholders can benefit from the obtained user expectations and feedback. Interpreting user demands by mining data through social media enables user participation in design decisions. This research method can be supportive and adaptive in related issues of design for the cities, enabling user participation in architectural and urban design. Research Limitations/Implications Parks and green spaces of Atasehir district of Istanbul are taken as a case study. Twitter is chosen for mining of data in social media based on parameters such as keywords and location. Social/Practical Implications The impact and support of users in design decisions can be clearly demonstrated by advanced information technologies. Mining data through social media and developed applications will contribute to design decisions and policies for architectural and urban spaces. Originality/Value Tweet Grabber and Map Grabber applications are developed for this research in order to get text based and image based data. The research includes a unique case study for mining data through social media on a specific design issue and target location.