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Öğe A Computational Content Analysis on Representation in Mental Health News Media with Global Perspectives(Springer Science and Business Media Deutschland GmbH, 2023) Dursun, A.D.Media representations can provide important symbolic resources in the construction of perception and agendas in diverse domains particularly concerning public health and social awareness. In view of this standpoint, dominant media frames are said to be powerful in defining social problems and shaping public discourses. The majority of the relevant literature demonstrates that representations of people with mental disorder are often negative, individuals being depicted in a passive role. A grammatical analysis of language use in press reveals different perspectives regarding the assignment of a more or less prominent role. The use of active and passive verb voice is one constituent of such analysis. Framing process constitutes another mode of analysis of news media content, which is stated to be dynamic in the concept of communication. Accordingly, this study aims to provide insights into the representation of individuals that experience mental health problems. For this purpose, newspaper stories are analyzed by examining the volume and content of a sample of selected news stories on mental illness in Türkiye for a time period of six years. Six national daily newspapers make up the sample of the study which provides a content analysis on the news media dataset. The analysis results demonstrate statistical findings on the depiction of people with mental disorder. Based on the findings and considering the sensitivity of the issue, it can be noted that more emphasis can be placed on humanizing mental illness by indicating that mental disorder is real, common and possible to be treated. In addition, coverage of other success stories with positive frames can be an alternative way to balance the multitude of news stories that link mental illness with violence or other negative elements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe Multifractal complexity analysis-based dynamic media text categorization models by natural language processing with BERT(Elsevier, 2022) Karaca, Y.; Zhang, Y.-D.; Wang, S.-H.; Dursun, A.D.Fractals, being essentially mathematical constructs, are forms that embody the fundamental features of dynamism, self-organization, self-similarity and complexity. The lexical items and parts of sentences are comprehended as the constituents of schemata with a particular pattern made up of interacting elements. Among the most well-known means used to detect and analyze self-repeating patterns are multifractal methods which have numerous applications in many areas including computational linguistics. The predominance of properties like self-similarity, irregularity and vagueness in texts add more to the challenge of clear and accurate meaning conveyance. The ever-increasing amount of text data in different categories also contribute to the inherent complexity due to having properties like being unstructured, noisy and nonstandard. To address this challenge and complexity, this study has aimed at ensuring regularity and self-similarity within the digital-based complex media texts, which comprise the dataset, by multifractal methods (multifractal Bayesian, multifractal regularization and multifractal wavelet shrinkage) and attaining accurate classification and categorization of the words within texts in the dataset by Bidirectional Encoder Representations from Transformers (BERT), as the Natural Language Processing (NLP) method. The related steps of our integrative proposed method are as follows: firstly, regularity enhancement was attained by applying the multifractal methods (multifractal Bayesian, multifractal regularization and multifractal wavelet shrinkage) to the text dataset. Thus, the new datasets were generated, respectively, by obtaining the significant, self-similar and regular attributes. Subsequently, BERT, as the NLP method, was employed to the text dataset as well as to the three new datasets obtained for the classification purposes. In this way, accurate word detection within the text for the category classification was ensured for the analyses. The analysis results for the text dataset and the new datasets were compared by BERT and the most optimal result could be achieved by multifractal Bayesian method. Through this integrated scheme, we have enunciated the significance of the behavioral patterns of fractal while setting forth the distinctive quality of BERT owing to its capability of classification accuracy and adaptiveness into integrated methodologies. © 2022 Elsevier Inc. All rights reserved.