Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection

Abstract

News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

“…This dataset is collected gradually from the MovieLens website, a noncommercial web-based movie recommender system, and randomly selected. The dataset includes ratings of users on movies on a star scale in the interval [1,5]. Among the different datasets in different sizes provided on this website, we used in this project the ml-100 K dataset, which includes 100-thousand records of ratings.…”

“…The problem arises when people face too many options, which can cause an overload of information, leading to a difficult decision-making process. To overcome this problem, Recommender Systems (RSs) are used to provide users with personalized item recommendations [1][2][3][4][5].…”