Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylizedframework to study the effects of ranking algorithms on opinion dynamics. We consider rankings thatdepend on popularity and on personalization. We find that popularity driven rankings can enhanceasymptotic learning while personalized ones can both inhibit or enhance it, depending on whether individuals have common or private value preferences. We also find that ranking algorithms can contributetowards the diffusion of misinformation (e.g., fake news ), since lower ex-ante accuracy of content ofminority websites can actually increase their overall traffic share.