Digital Journalism and Deepfake: A Study on News Credibility

This study aims to examine the resilience capacity of digital journalism practices against deepfake technology, which involves fake content generated by deep learning algorithms, and its multidimensional implications for news credibility. Deepfake technology erodes traditional measures of news credibility by causing problems such as political manipulation, disinformation, and information pollution. Conducted as a case study within the qualitative research paradigm, the study identified eight news videos containing deepfake content published on YouTube and Instagram between February 18, 2022, and June 1, 2025, using criterion sampling. The empirical data of the research consists of 187 user comments collected from under these videos. The data was analyzed using descriptive content analysis within the framework of six basic sensitivity types and seven emotional tone categories. The analysis revealed that the most dominant sensitivity types were Manipulation and Perception Management Concerns and Ethical and Legal Sensitivity. Users perceive deepfakes as both a violation of individual privacy and a systemic element threatening social trust. Furthermore, Appreciation and Curiosity towards Technology sensitivity also played a significant role, showing that users admire technological progress but are aware of its potential risks. In terms of emotional tones, Fear led the way, followed by Surprise and Happiness. In conclusion, deepfakes have created a crisis of truth in digital media and demonstrated that the reliability of news is now measured not only by accuracy but also by technological control and ethical responsibility. To mitigate these risks, it is recommended that mandatory labeling/watermarking systems be introduced for AI-generated content and that intergenerational media literacy programs be developed.