SUN’IY INTELLEKT ASOSIDA FAKE NEWS VA YOLG‘ON MA’LUMOTLARNI ANIQLASH TEXNOLOGIYALARINING SAMARADORLIGI

Авторы

  • Qarshiboyev Vosid Vaxob o'g'li Автор

Аннотация

Mazkur maqolada sun’iy intellekt texnologiyalari asosida fake news va yolg‘on ma’lumotlarni aniqlash usullarining samaradorligi tahlil qilinadi. Internet va ijtimoiy tarmoqlarning rivojlanishi natijasida noto‘g‘ri ma’lumotlarning tez tarqalishi global muammoga aylanganligi sababli, ularni avtomatik aniqlash tizimlariga ehtiyoj ortib bormoqda. Tadqiqot davomida mashinaviy o‘qitish, chuqur o‘rganish, tabiiy tilni qayta ishlash hamda neyron tarmoqlar asosida ishlab chiqilgan texnologiyalar o‘rganildi. Shuningdek, Facebook, Twitter va boshqa platformalarda qo‘llanilgan algoritmlar samaradorligi ko‘rib chiqildi. Tadqiqot natijalari shuni ko‘rsatadiki, sun’iy intellekt tizimlari yolg‘on ma’lumotlarni aniqlashda yuqori aniqlik darajasiga ega bo‘lsa-da, ma’lumotlar sifati, til xususiyatlari va algoritmik xatoliklar kabi muammolar hanuz mavjud. Maqolada zamonaviy texnologiyalarning afzalliklari va cheklovlari ilmiy manbalar asosida yoritilgan.

Библиографические ссылки

Allcott H., Gentzkow M. Social Media and Fake News in the 2016 Election. — Journal of Economic Perspectives, 2017. — Vol. 31, No. 2. — P. 211–236.

Wardle C., Derakhshan H. Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making. — Council of Europe Report, 2017. — P. 5–18.

Lazer D. et al. The Science of Fake News. — Science, 2018. — Vol. 359, No. 6380. — P. 1094–1096.

Vosoughi S., Roy D., Aral S. The Spread of True and False News Online. — Science, 2018. — Vol. 359, No. 6380. — P. 1146–1151.

Shu K., Sliva A., Wang S., Tang J., Liu H. Fake News Detection on Social Media: A Data Mining Perspective. — SIGKDD Explorations, 2017. — Vol. 19, No. 1. — P. 22–36.

Goodfellow I., Bengio Y., Courville A. Deep Learning. — MIT Press, 2016. — P. 321–350.

Kotsiantis S. Supervised Machine Learning: A Review of Classification Techniques. — Informatica, 2007. — Vol. 31. — P. 249–268.

Goldberg Y. Neural Network Methods for Natural Language Processing. — Morgan & Claypool Publishers, 2017. — P. 45–88.

Devlin J., Chang M., Lee K., Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. — NAACL Proceedings, 2019. — P. 4171–4186.

Powers D. Evaluation: From Precision, Recall and F-measure to ROC. — Journal of Machine Learning Technologies, 2011. — Vol. 2, No. 1. — P. 37–63.

Kaliyar R., Goswami A., Narang P. FakeBERT: Fake News Detection in Social Media with Transformer-Based Deep Neural Networks. — Complex & Intelligent Systems, 2021. — Vol. 7. — P. 1077–1087.

Meta Transparency Report. Community Standards Enforcement Report. — Meta Platforms Inc., 2023. — P. 14–27.

World Health Organization. Managing the COVID-19 Infodemic. — WHO Report, 2020. — P. 1–12.

Google Transparency Report. YouTube Community Guidelines Enforcement. — Google LLC, 2023. — P. 8–19.

Barocas S., Selbst A. Big Data’s Disparate Impact. — California Law Review, 2016. — Vol. 104, No. 3. — P. 671–732.

Chesney R., Citron D. Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security. — California Law Review, 2019. — Vol. 107. — P. 1753–1819.

Floridi L., Cowls J. A Unified Framework of Five Principles for AI in Society. — Harvard Data Science Review, 2019. — Vol. 1, No. 1. — P. 1–15.

Опубликован

2026-05-25

Как цитировать

Qarshiboyev , V. (2026). SUN’IY INTELLEKT ASOSIDA FAKE NEWS VA YOLG‘ON MA’LUMOTLARNI ANIQLASH TEXNOLOGIYALARINING SAMARADORLIGI. Центральноазиатский журнал междисциплинарных исследований и менеджмента, 3(5, PART 2), 90-94. https://www.in-academy.uz/index.php/CAJMRMS/article/view/50152
Innovative Academy RSC
Article metrics Views and PDF downloads
3 Views
0 Downloads