Abstract

Due to easy dissemination of news in social media and the Web, there has been an increasing rise of disinformation on important political issues like elections in recent years. Computational solutions for automatic bias and sensationalism detection for news articles can have tremendous impact if used in the right way. Because news is an ever-shifting domain, concept drift is an issue that must be dealt with in any real-world computational news classification system that relies on features and trained machine learning models. Yet, an empirical study of concept drift in such systems, especially popular systems released recently as open-source and used within organizations, has been lacking thus far. This short paper reports results on an empirical study specifically designed to assess concept drift, using an open-source, popular computational news classification system, on real news data crawled from the Web. We …

Date

2019

Authors

Shuo Zhang, Mayank Kejriwal

Book

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages

601-604