Online News Media Website Ranking Using User Generated Content

TL;DR

This paper proposes a novel framework for ranking online news websites using user-generated content, evaluating completeness, diversity, and speed to improve news retrieval and recommendation tasks.

Contribution

It introduces a new multi-view ranking framework based on user-generated content, emphasizing real-time, unbiased, and low-cost assessment of news websites.

Findings

BBC ranks highest in news completeness and speed.

NYTimes shows the greatest diversity of covered events.

The framework effectively differentiates news website performance.

Abstract

News media websites are important online resources that have drawn great attention of text mining researchers. The main aim of this study is to propose a framework for ranking online news websites from different viewpoints. The ranking of news websites is useful information, which can benefit many news-related tasks such as news retrieval and news recommendation. In the proposed framework, the ranking of news websites is obtained by calculating three measures introduced in the paper and based on user-generated content. Each proposed measure is concerned with the performance of news websites from a particular viewpoint including the completeness of news reports, the diversity of events being covered by the website and its speed. The use of user-generated content in this framework, as a partly-unbiased, real-time and low cost content on the web distinguishes the proposed news website ranking framework from the literature. The results obtained for three prominent news websites, BBC, CNN, NYTimes, show that BBC has the best performance in terms of news completeness and speed, and NYTimes has the best diversity in comparison with the other two websites.