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Subject: new media

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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Fair-News: Digital Journalism Model To Prevent Information Pollution and Manipulation
    (Tech Science Press, 2023) Takan, Savas; Ergun, Duygu; Katipoglu, Gokmen
    As digital data circulation increases, information pollution and manipulation in journalism have become more prevalent. In this study, a new digital journalism model is designed to contribute to the solution of the main current problems, such as information pollution, manipulation, and account-ability in digital journalism. The model uses blockchain technology due to its transparency, immutability, and traceability. However, it is tough to provide the mechanisms necessary for journalism, such as updating one piece of information, instantly updating all other information affected by the updated information, establishing logical relationships between news, making quick comparisons, sorting and indexing news, and keeping the changing informa-tion about the news in the system, with the blockchain data structure. For this reason, in our study, we have developed a new data structure that provides both the immutability, transparency and traceability properties of the blockchain and can support the communication mechanisms necessary for journalism. The functionality of our proposed data structure is demonstrated in terms of communication mechanisms such as mutability, context, consistency, and reliability through example scenarios. Additionally, our data structure is compared with the data structure of blockchain technology in terms of time, space, and maintenance costs. Accordingly, while the model size increases linearly in blockchain, the model's size remains approximately constant since the structure we developed is data-independent. In this way, maintenance costs are reduced. Since our model also has an indexing mechanism, it reduces the linear time search complexity to logarithmic time. As a result, the data structure we developed is found to have higher performance than blockchain in the journalism concept. In future studies, it is planned to test all aspects of the model with a pilot application, eliminate its shortcomings, and develop a holistic approach to the root causes of the problems in the journalism focus.
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    Review
    Citation - WoS: 1
    Citation - Scopus: 2
    Bias in human data: A feedback from social sciences
    (Wiley Periodicals, inc, 2023) Takan, Savas; Ergun, Duygu; Yaman, Sinem Getir; Kilincceker, Onur
    The fairness of human-related software has become critical with its widespread use in our daily lives, where life-changing decisions are made. However, with the use of these systems, many erroneous results emerged. Technologies have started to be developed to tackle unexpected results. As for the solution to the issue, companies generally focus on algorithm-oriented errors. The utilized solutions usually only work in some algorithms. Because the cause of the problem is not just the algorithm; it is also the data itself. For instance, deep learning cannot establish the cause-effect relationship quickly. In addition, the boundaries between statistical or heuristic algorithms are unclear. The algorithm's fairness may vary depending on the data related to context. From this point of view, our article focuses on how the data should be, which is not a matter of statistics. In this direction, the picture in question has been revealed through a scenario specific to "vulnerable and disadvantaged" groups, which is one of the most fundamental problems today. With the joint contribution of computer science and social sciences, it aims to predict the possible social dangers that may arise from artificial intelligence algorithms using the clues obtained in this study. To highlight the potential social and mass problems caused by data, Gerbner's "cultivation theory" is reinterpreted. To this end, we conduct an experimental evaluation on popular algorithms and their data sets, such as Word2Vec, GloVe, and ELMO. The article stresses the importance of a holistic approach combining the algorithm, data, and an interdisciplinary assessment.This article is categorized under:Algorithmic Development > Statistics