Yeni Medya Ortamında Yapay Zekânın Gazetecilik İçeriği Üzerindeki Etkisi

Yıl 2025, Sayı: “MASS COMMUNICATION" SPECIAL ISSUE, 1 - 11

Sabina Izzatli

Öz

Bu çalışma, Yapay Zekâ’nın (YZ) değişen yeni medya ortamında gazetecilik içeriğini nasıl dönüştürdüğünü incelemektedir. Dijital teknolojiler haberin üretilme, dağıtılma ve tüketilme biçimlerini yeniden tanımlarken, YZ modern gazetecilikte kritik bir araç olarak öne çıkmaktadır. Otomatik haber yazımı ve veri analitiğinden, kişiselleştirilmiş içerik sunumu ve izleyici etkileşimine kadar birçok alanda YZ, haber odası pratiklerini ve editoryal karar alma süreçlerini yeniden şekillendirmektedir.
Makale, medya kuruluşlarının doğal dil üretimi, makine öğrenimi ve algoritmik öneri sistemleri gibi YZ araçlarını nasıl kullandığını inceleyerek, verimliliği ve izleyici erişimini artırma çabalarını ele almaktadır. Ayrıca, büyük veri kümelerinin işlenerek eğilimlerin ortaya çıkarıldığı ve araştırmacı gazeteciliğe katkı sağladığı veri gazeteciliğinde YZ’nin artan rolüne dikkat çekilmektedir. Hız, ölçeklenebilirlik ve kişiselleştirme gibi avantajlara rağmen, çalışmada algoritmik önyargılar, editoryal şeffaflığın azalması ve YZ üretimi içeriklerin özgünlüğüne dair etik kaygılar gibi olası sakıncalar da değerlendirilmektedir. Vaka analizleri ve güncel eğilimler ışığında bu araştırma, gazetecilik süreçlerinde insan denetiminin sürdürülmesinin önemini vurgulamaktadır.
Sonuç olarak, bu çalışma dijital toplumda gazeteciliğin geleceğine dair devam eden tartışmalara katkı sunmakta; YZ’nin yeteneklerinden yararlanırken gazetecilik etiği, güvenilirlik ve demokratik değerlerden ödün verilmemesi gerektiğini savunmaktadır.

Anahtar Kelimeler

Yapay Zeka , Gazetecilik , Yeni Medya , Algoritmik Önyargı , Haber Otomasyonu

The Impact of Artificial Intelligence on Journalistic Content in The New Media Environment

Öz

This research focuses on the issue of Artificial Intelligence (AI) in journalism and its implications for the digital media ecosystem. AI has become an indispensable asset in contemporary journalism as digital paradigms shift the processes of news production, distribution, and consumption. This article aims to address the manner in which media companies apply AI, including Natural Language Processing (NLP), deep learning, and recommendation algorithms, to improve productivity and expand audience size. It also explores the growing significance of AI in data-driven journalism, where vast amounts of data are processed to discover patterns and validate investigative stories.
Through case studies and analysis of current trends, this research emphasizes the importance of maintaining human oversight in journalistic workflows. It argues that while AI can augment journalism, it must be guided by professional standards, ethical principles, and a commitment to public interest.
Ultimately, this study contributes to ongoing discussions about the future of journalism in a digital society. It calls for balanced integration of AI in newsrooms' policy by leveraging its capabilities without compromising journalistic integrity, credibility, and democratic values.

Anahtar Kelimeler

Artificial Intelligence , Journalism , New Media , Algorithmic Bias , News Automation

Kaynakça

• Carlson, M. (2018). Automated journalism: A critical inquiry into emerging algorithms and news production in the digital era. Digital Journalism, 6(3), pp. 354–367. https://doi.org/10.1080/21670811.2017.1343642
• Chesney, R., & Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), pp. 1753–1819.
• Culver, K. B., & Whipple, A. (2020). AI in journalism: Ethical challenges and policy considerations. Journalism & Mass Communication Quarterly, 97(1), pp. 220–239. https://doi.org/10.1177/1077699019872243
• Diakopoulos, N. (2019). Automating the news: How algorithms are rewriting the media. Harvard University Press.
• European Union. (2018). General Data Protection Regulation (GDPR). Official Journal of the European Union.
• Reuters Institute for the Study of Journalism. (2024). Digital News Report 2024. https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2024
• Reuters Institute for the Study of Journalism. (2024). Public attitudes towards the use of AI and journalism. https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2024/public-attitudes-towards-use-ai-and-journalism
• Faggella, D. (2018, July 31). News organization leverages AI to generate automated narratives from big data. Emerj Artificial Intelligence Research. https://emerj.com/news-organization-leverages-ai-generate-automated-narratives-big-data/
• Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), pp. 298–320. https://doi.org/10.1093/poq/nfw006
• Gorwa, R. (2020). The platform governance triangle: Conceptualizing the informal regulation of online content. Internet Policy Review, 9(4). https://doi.org/10.14763/2020.4.1531
• Gray, J., Bounegru, L., & Chambers, L. (2012). The data journalism handbook: How journalists can use data to improve the news. O'Reilly Media.
• Graves, L. (2018). Understanding the promise and limits of automated fact-checking. Reuters Institute for the Study of Journalism.
• Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., ... & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), pp. 1094–1096. https://doi.org/10.1126/science.aao2998
• Lewis, S. C., & Westlund, O. (2015). Actors, actants, audiences, and activities in cross-media news work. Digital Journalism, 3(1), 19–37. https://doi.org/10.1080/21670811.2014.999668
• Liu, Y., & Sundar, S. S. (2018). Should machines express sympathy and empathy? Experiments with a health advice chatbot. Cyberpsychology, Behavior, and Social Networking, 21(10), pp. 625–636. https://doi.org/10.1089/cyber.2018.0250
• Marconi, F. (2020). Newsmakers: Artificial intelligence and the future of journalism. Columbia University Press.
• Mitova, M., van Drunen, M., Helberger, N., & Moeller, J. (2024). User control and transparency in news recommender systems: A five-country study. Digital Journalism, Advance online publication. https://doi.org/10.1177/14648849231222099
• Napoli, P. M. (2014). Automated media: An institutional theory perspective on algorithmic media production and consumption. Communication Theory, 24(3), pp. 340–360. https://doi.org/10.1111/comt.12039
• Napoli, P. M. (2019). Navigating algorithmic news: Artificial intelligence and the changing dynamics of news consumption. Digital Journalism, 7(8), pp. 1039–1050. https://doi.org/10.1080/21670811.2019.1639489
• Napoli, P. M. (2019). Social media and the public interest: Media regulation in the disinformation age. Columbia University Press.
• Nieman Lab. (2024, June 16). People don’t trust the news media to use generative AI responsibly, Reuters finds. https://www.niemanlab.org/2024/06/people-dont-trust-the-news-media-to-use-generative-ai-responsibly-reuters-finds/
• Obermayer, B., & Obermaier, F. (2016). The Panama Papers: Breaking the story of how the rich and powerful hide their money. Oneworld Publications.
• Pariser, E. (2011). The filter bubble: What the internet is hiding from you. Penguin Press.
• Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), pp. 22–36. https://doi.org/10.1145/3137597.3137600
• Sunstein, C. R. (2017). #Republic: Divided democracy in the age of social media. Princeton University Press.
• Tandoc, E. C., Jr., & Maitra, J. (2022). AI and the future of journalism: Risks and opportunities. Journalism Studies, 23(4), pp. 521–537. https://doi.org/10.1080/1461670X.2021.1974203
• Thomson Reuters Foundation. (2025). Journalism in the AI Era: Opportunities and Challenges in the Global South. https://www.trust.org/wp-content/uploads/2025/01/TRF-Insights-Journalism-in-the-AI-Era.pdf
• Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13, pp. 203–218.
• Wang, W. Y., Su, H., & Kambhampati, S. (2020). Explainable fact-checking. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), pp. 9540–9547. https://doi.org/10.1609/aaai.v34i05.6282
• Ward, S. J. A. (2010). Ethics and the media: An introduction (2nd ed.). Cambridge University Press.