Analysis of Bias in Gathering Information Between User Attributes in News Application

TL;DR

This study investigates how user demographics influence news consumption behaviors and biases using action logs, revealing correlations and biased keywords linked to demographic attributes, thus providing insights into confirmation bias in web news services.

Contribution

The paper introduces a novel method to analyze demographic-based biases in news consumption using action logs, highlighting correlations and biased keywords without relying on questionnaires.

Findings

Strong correlation in browsing tendencies between user attributes.

Identification of biased keywords associated with demographic attributes.

Effective detection of biased keywords through regression analysis.

Abstract

In the process of information gathering on the web, confirmation bias is known to exist, exemplified in phenomena such as echo chambers and filter bubbles. Our purpose is to reveal how people consume news and discuss these phenomena. In web services, we are able to use action logs of a service to investigate these phenomena. However, many existing studies about these phenomena are conducted via questionnaires, and there are few studies using action logs. In this paper, we attempt to discover biases of information gathering due to differences in user demographic attributes, such as age and gender, from the behavior log of the news distribution service. First, we summarized the actions in the service for each user attribute and showed the difference of user behavior depending on the attributes. Next, the degree of correlation between the attributes was measured using the correlation coefficient, and a strong correlation was found to exist in the browsing tendency of the news articles between the attributes. Then, the bias of keywords between attributes was discovered, keywords with bias in behavior among the attributes were found using parameters of regression analysis. Since these discovered keywords are almost explainable by big news, our proposed method is effective in detecting biased keywords.