Measuring the Diversity of Shared News and the Effect of Recommendation Algorithms

We present a graph- and information-theoretical approach to the problem of the measurement of the diversity in the context of consumption, sharing, and recommendation of news media. We use these proposed metrics to analyze the diversity of news sources shared on Twitter during the 2017 French Presidential election campaign. We then explore the diversity of recommendations produced by different popular and ubiquitous recommendation algorithms when used to propose news sources to online users.