B-NEWS

In the rapidly evolving landscape of news publishing, the ability to predict the types of news content that will resonate with readers is becoming increasingly important. To this end, news publishers are turning to predictive systems that use artificial intelligence and machine learning algorithms to analyze large volumes of data and identify patterns that can be used to predict what news stories are likely to be successful. 

One specific challenge faced by news publisher in Switzerland is the need to republish articles from other regions (e.g., a bilingual news media company publishing German news on a French online news site), which requires content assessment, translation and contextualization by a journalist. To address this challenge, our goal in this project is to develop a machine intelligence-based predictive system for news republishing, the purpose of which will be twofold: firstly, we intend to build news data visualization platform in this system, which will provide journalists with an easy-to-use interface for viewing published news articles and their statistical feedback. It will help journalists stay up-to-date with the latest news and trends. Secondly, this system aims to design a predictive model that can help newsrooms to choose articles from other regions to republish on a daily basis. The goal is to provide decision support based on audience figures from the originating region and trends from the target region, such as online popular news and hot topics on social networks, to ensure that the republished content will be of interest to the target audience.

By leveraging the power of predictive analytics, news publishers can improve the efficiency and effectiveness of their newsrooms, increase engagement with their readers, and ultimately generate more revenue from their content. The development of a predictive system for news publishing represents an exciting opportunity for news publishers to stay ahead of the curve and deliver high-quality content that resonates with their audience.