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.

Understanding the link between audience engagement metrics and the perceived quality of online news using machine learning

“…This work primarily discusses the prediction task of the perceived quality of articles published on the blogging platform Medium.com. It considerably extends a preliminary version of this work in [61] and [62] by introducing additional features (e.g., from [65]) and including a framework with three dimensions, namely, author, content and context (see [47]). The best predictor consistently is the number of followers, which is not surprising: if more people see it, more people will clap.…”