Detecting Media Bias in News Articles using Gaussian Bias Distributions

authored by

Wei Fan Chen, Khalid Al-Khatib, Benno Stein, Henning Wachsmuth

Abstract

Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that feature-based and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their “bias predictiveness” is unclear. In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection effectiveness. In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model. On an existing media bias dataset, we find that the frequency and positions of biased statements strongly impact article-level bias, whereas their exact sequential order is secondary. Using a standard model for sentence-level bias detection, we provide empirical evidence that article-level bias detectors that use second-order information clearly outperform those without.

External Organisation(s)

Paderborn University
Bauhaus-Universität Weimar

Type

Conference contribution

Pages

4290-4300

No. of pages

11

Publication date

11.2020

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Information Systems, Computer Science Applications, Computational Theory and Mathematics

Electronic version(s)

https://doi.org/10.48550/arXiv.2010.10649 (Access: Open)
https://doi.org/10.18653/v1/2020.findings-emnlp.383 (Access: Open)