Authors : Dr. Abuelainin Hussain

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/3akvjrfz

Scribd : https://tinyurl.com/27wkpstk

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL927

Abstract : This study explores the impact of Artificial Intelligence (AI) on digital media, focusing on content creation, recommendation systems, and user engagement. A comprehensive literature review was conducted, synthesizing existing studies and scholarly articles on the subject. A mixed-methods approach was employed, involving in-depth discussions with industry professionals and a survey administered to digital media platform users. The findings revealed that AI has significantly transformed content creation, with AI-generated content being encountered by 78% of users. Most users found the content to be relevant and of good quality; however, concerns about authenticity and biases were raised. AI-driven recommendation systems were prevalent, with 62% of users utilizing them. The majority found the recommended content to be useful and relevant. Trust levels varied, with 48% expressing moderate to high trust. Transparency and explainability were emphasized by 81% of users. The study concludes by providing recommendations for enhancing authenticity, addressing biases, increasing user education, and ensuring ethical considerations in AI applications in digital media. These findings contribute to our understanding of the implications of AI in digital media.

Keywords : Artificial Intelligence, Digital Media, Content Creation, Recommendation Systems, User Engagement.

References :

1. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., . . . Amodei, D. (2020). Language Models are Few-Shot Learners. ArXiv. /abs/2005.14165.
2. de Fine Licht, K., de Fine Licht, J. Artificial intelligence, transparency, and public decision-making. AI & Soc 35, 917–926 (2020).
3. Ekstrand, M. D., Fleder, M., Ludwig, M., & Riedl, J. (2020). Challenging Misinformation: Exploring Agents and Roles in an AI-supported News Game. In Proceedings of the 31st ACM Conference on Hypertext and Social Media (HT’20), 205-214.
4. Hassan, N., Dar, A., Qadir, A., Imran, M., & Nawaz, R. (2020). Fake news detection: A deep learning approach. Information Processing & Management, 57(2), 102025.
5. Klöckner, K., Nemec-Begluk, S., & Heidenreich, S. (2021). AI storytelling and algorithms in creative industries. Technological Forecasting and Social Change, 168, 120786.
6. Li, W., & Huang, J. (2019). Generating personalized tour recommendations: A knowledge-enhanced reinforcement learning approach. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1440-1454.
7. O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books.
8. Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web (pp. 111-112).
9. Tang, J., Wang, K., Zhang, A. X., Yan, M., & Chua, T. S. (2015). Learning sentiment-specific word embedding for Twitter sentiment classification. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (pp. 1533-1542).
10. Wang, Q., Zhang, J., Wang, J., & Tian, L. (2020). A survey on emotion recognition using physiological signals as mobile healthcare applications. Mobile Networks and Applications, 25(4), 1433-1449.
11. Yang, D., & Lee, W. S. (2019). "I am so happy!" Sentiment-aware hybrid recommender system for social media platforms. Journal of Information Science, 45(4), 447-462.
12. Yu, K., Hu, W., Zhang, J., Zhang, C., & Sun, Y. (2020). Ai-human synergy system: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(4), 1-25.
13. Zhang, Y., Du, L., Feng, Y., & Wang, M. (2018). Towards personalized online chatbots: A comprehensive survey. Sensors, 18(6), 1862.