Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized framework to study the effects of ranking algorithms on opinion dynamics. We consider rankings that depend on popularity and on personalization. We find that popularity driven rankings can enhance...

This paper provides a simple theoretical framework to evaluate the effect of key parameters of ranking algorithms, namely popularity and personalization parameters, on measures of platform engagement, misinformation and polarization. The results show that an increase in the weight assigned to...

This paper provides a model of the market for news where profit-maximizing media outlets choose their editors from a population of rational citizens. The analysis identifies a novel mechanism of media bias: the bias in a media outlet's news reports is the result of the slanted endogenous...