The Intelligent News Content Distribution System Based on Convolutional Neural Networks

Background: With the advent of the digital age, news content distribution has undergone a revolution. While traditional news distribution relies on subjective judgements of editors and search behaviour of users, intelligent news content distribution systems based on Convolutional Neural Networks (CNNs) use machine learning techniques to achieve automated and personalised news recommendations. In this paper, we present a novel framework that exploits the powerful feature extraction capabilities of CNNs to deeply analyse news text and accurately capture user interests. Methods: The system analyses the users historical reading patterns, combines the features of the news content, and uses CNN models to classify and score the news, ultimately recommending the most relevant and appealing news to the user. Experimental results show that the system significantly improves user satisfaction and news engagement. Results: The study found that factors such as level of graphic violence in news stories, the emotional slant of news headlines, the political stance of news sources, and the accuracy of factual reporting have a significant impact on user attention on the Reddit platform. In addition, the traffic rating of the news site and the number of cross-posts had a significant positive effect on the number of user comments. Conclusion: This study provides new perspectives in the field of digital news distribution and helps media practitioners better understand how to optimize content to increase user engagement. The findings suggest that the intrinsic mechanisms of social media platforms, such as community rules and platform characteristics, have a significant impact on user engagement, and future work should pay more attention to how these mechanisms shape user behaviour. On a technical level, news recommendation systems are expected to become smarter and more personalised as artificial intelligence and machine learning technologies continue to evolve.