AI and Deep Learning in Data Journalism: Advancing Pay-Per View Strategies through Intelligent Insights and Audience Engagement

The integration of Artificial Intelligence (AI) and Deep Learning into data journalism has transformed the way the media organizations analyze, interpret, and distribute news content. The emergence of digital journalism through subscription-based and pay-per-view (PPV) models has created a pressing need for intelligent systems that boost revenue through increased audience engagement. This study examines the role of AI-powered data analytics, deep learning algorithms, and natural language processing (NLP) in advancing pay-per-view strategies through predictive insights, personalized content recommendations, and dynamic pricing mechanisms. This research looks at how media organizations can effectively customize their content distribution by applying machine learning models, audience sentiment analysis, and real-time behavioral tracking to increase reader retention and revenue. The influence of deep learning-enabled recommendation systems on content accessibility and user engagement is analyzed, ensuring that paywalled content is offered to the right audience at the right moment. The study also examines the functionality of AI-driven predictive analytics in estimating subscription trends, user preferences, and pricing strategies that assure the balance between profitability and accessibility. The findings demonstrate that the combination of AI and deep learning significantly boosts the performance of pay-per-view models, enables media houses to do well at Publisher's adaptation of business models, personalization of news experiences, and automation of the content curation process. Ethical issues are also highlighted as significant challenges in AI-driven journalism, including algorithmic bias, privacy concerns, and transparency. The study concludes by suggesting a combined AI-human editorial framework that guarantees that data-driven journalism is ethical, trustworthy, and financially viable in the fast-changing digital media landscape.