Automatic Estimation of News Values Reflecting Importance and Closeness of News Events

Evgenia Belyaeva, Aljaž Košmerlj, Dunja Mladenić, Gregor Leban


This paper addresses a problem of automatic estimation of three journalistic news values, more specifically frequency, threshold and proximity, by applying various text mining methods. Although theoretical frameworks already exist in social sciences that identify if an event is newsworthy, these manual techniques require enormous amount of time and domain knowledge. Thus, we illustrate how text mining can assist journalistic work by finding news values of different international publishers across the world. Our experiments on both general collection of articles and a collection of news articles from different publishers about Apple’s launch of new iPhone 6 and Apple Watch confirm that some journalists still follow some of the well-known journalistic values. Furthermore, we acknowledge that news values are often orthodox and outdated, and no longer apply to all publishers. We also outline possible future implications of our approach to work on interaction between text mining and journalistic domains. 

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