The Japanese Society for Artificial Intelligence

In the era of social media, we are often exposed to unintentionally biased information due to filter bubbles.
Such biased information amplifies opinion fragmentation and political polarization.
To cope with this problem, we analyze the bias of the news media and help people understand the news correctly.
The existing survey on the bias is conducted by expert analysis and crowdsourced evaluation to the media.
In this study, by using a topic model for Twitter comments on news, the distance between news media is calculated from hierarchical clustering's estimated probability of the topic.
The fundamental idea is to measure the bias by analyzing the content of the topic and the news media's similarity.
When we applied this method to tweets about the Science Council of Japan issue, we found that the results for mainstream media such as Asahi Shinbun were roughly consistent with other studies on political bias. We found that it is possible to capture bias in media that have not been surveyed before.