In the rapidly evolving news publishing landscape, 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 amounts of data and identify patterns that can be used to predict which news stories are likely to be successful.

A particular challenge for news publishers in Switzerland with national coverage is the need to republish articles from other regions (for example, republishing a story in German on a French-language news site), which requires content assessment, translation and contextualization by a journalist. To address this challenge, the goal of this project, carried out by the Media Innovation Lab at the HES-SO Valais-Wallis in partnership with Blick | fr, is to develop a machine intelligence-based predictive system for news republishing that will provide the newsroom with a real-time visual dashboard to help journalists select articles from other regions to republish on a daily basis. This will involve providing decision support based on audience metrics from the source region and trends from the target region, such as popular news stories and trending topics on social media, to ensure that republished content is of interest to the target audience.

Ultimately, the use of predictive systems is a promising way for publishers to proactively produce compelling, audience-centric content and maintain a competitive edge in today’s landscape.