Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society. Any form of media can contain overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such form of imbalanced news coverage can be exposed. The research presented in this paper addresses not only the automatic detection of bias but goes one step further in that it explores how political bias and unfairness are manifested linguistically. In this regard we utilize a new corpus of 6964 news articles with labels derived from this http URL and develop a neural model for bias assessment. By analyzing this model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.

[1]  Preslav Nakov,et al.  Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media , 2019, NAACL.

[2]  Frank Bentley,et al.  Understanding Online News Behaviors , 2019, CHI.

[3]  Tim Groseclose,et al.  A Measure of Media Bias , 2005 .

[4]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[5]  P Deepak,et al.  Topic-Specific Sentiment Analysis Can Help Identify Political Ideology , 2018, WASSA@EMNLP.

[6]  Benno Stein,et al.  Learning to Flip the Bias of News Headlines , 2018, INLG.

[7]  Philip Resnik,et al.  Political Ideology Detection Using Recursive Neural Networks , 2014, ACL.

[8]  Jun Zhao,et al.  Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions , 2017, AAAI.

[9]  Xiaojun Wan,et al.  Attention-based LSTM Network for Cross-Lingual Sentiment Classification , 2016, EMNLP.

[10]  Preslav Nakov,et al.  Predicting Factuality of Reporting and Bias of News Media Sources , 2018, EMNLP.

[11]  Eunsol Choi,et al.  Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking , 2017, EMNLP.

[12]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[13]  Horst Po¨ttker News and its communicative quality: the inverted pyramid—when and why did it appear? , 2003 .

[14]  Ryan L. Boyd,et al.  The Development and Psychometric Properties of LIWC2015 , 2015 .

[15]  Steven Skiena,et al.  Multi-view Models for Political Ideology Detection of News Articles , 2018, EMNLP.

[16]  Wei-Fan Chen,et al.  Detecting Media Bias in News Articles using Gaussian Bias Distributions , 2020, FINDINGS.

[17]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.