Opening the black box of perceived quality: Predicting endorsement on a blog site

Abstract

Uncovering their readers’ perceptions is of key importance for every news media organization to find methods to improve the quality of their product. It has the potential to facilitate journalists’ work in attracting attention and gaining a loyal audience. Discovering which elements of a news story influence readers’ perceptions has been a cross-disciplinary research goal for the past years, because it can play a crucial role in news dissemination and consumption in the digital age. Drawing upon literature in the various areas such as journalism, psychology, computer science, and AI, this paper proposes a machine learning approach that explores three dimensions of article features that can help predicting the online behavior of the reader. Results show that how the story is written, the topic, and certain aspects of the author’s online reputation can affect reader endorsements and the perceived quality of an article. CCS CONCEPTS • Computing methodologies → Natural language processing; • Applied computing → Document management and text processing.