Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

Digital journalism has faced a dramatic change and media companies are challenged to use data science algorithms be more competitive in Big Data era. While this is relatively new area of study the landscape, machine learning artificial intelligence increased substantially over last few years. In particular, adoption models for personalization recommendation attracted attention several publishers. Following trend, paper presents research literature analysis on role Science (DS) Journalism (DJ). Specifically, aim present critical review, synthetizing main application areas DS DJ, highlighting gaps, challenges, opportunities future studies. Through systematic review integrating bibliometric search, text mining, qualitative discussion, relevant was identified extensively analyzed. The reveals an increasing methods with almost 47% being published three An hierarchical clustering highlighted six domains focused event extraction, online comment analysis, systems, automated journalism, exploratory along some approaches. Future directions comprise developing improve engagement features, exploring algorithms, testing solutions, improving paywall mechanisms.