Learning Unbiased News Article Representations: A Knowledge-Infused Approach

TL;DR

This paper introduces a knowledge-infused deep learning model that leverages external data to produce unbiased representations of news articles, effectively predicting political leaning even for unseen publishers, thus reducing bias and improving accuracy.

Contribution

The work presents a novel knowledge-infused deep learning approach that mitigates algorithmic bias and generalizes better to unseen news sources for political leaning prediction.

Findings

Achieves up to 73% accuracy in predicting political leaning.

Outperforms baseline methods in unbiasedness and generalization.

Effectively reduces publisher bias in news article representations.

Abstract

Quantification of the political leaning of online news articles can aid in understanding the dynamics of political ideology in social groups and measures to mitigating them. However, predicting the accurate political leaning of a news article with machine learning models is a challenging task. This is due to (i) the political ideology of a news article is defined by several factors, and (ii) the innate nature of existing learning models to be biased with the political bias of the news publisher during the model training. There is only a limited number of methods to study the political leaning of news articles which also do not consider the algorithmic political bias which lowers the generalization of machine learning models to predict the political leaning of news articles published by any new news publishers. In this work, we propose a knowledge-infused deep learning model that utilizes relatively reliable external data resources to learn unbiased representations of news articles using their global and local contexts. We evaluate the proposed model by setting the data in such a way that news domains or news publishers in the test set are completely unseen during the training phase. With this setup we show that the proposed model mitigates algorithmic political bias and outperforms baseline methods to predict the political leaning of news articles with up to 73% accuracy.