  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.
