Jonas KaiserADRIAN RAUCHFLEISCH2023-04-072023-04-0720202056-3051https://scholars.lib.ntu.edu.tw/handle/123456789/629999Algorithms and especially recommendation algorithms play an important role online, most notably on YouTube. Yet, little is known about the network communities that these algorithms form. We analyzed the channel recommendations on YouTube to map the communities that the social network is creating through its algorithms and to test the network for homophily, that is, the connectedness between communities. We find that YouTube’s channel recommendation algorithm fosters the creation of highly homophilous communities in the United States (n = 13,529 channels) and in Germany (n = 8,000 channels). Factors that seem to drive YouTube’s recommendations are topics, language, and location. We highlight the issue of homophilous communities in the context of politics where YouTube’s algorithms create far-right communities in both countries.algorithms; recommendation algorithms; homophily; network analysis; YouTube; filter bubblejournal article10.1177/20563051209699142-s2.0-85096518307WOS:000593571400001https://doi.org/10.1177/205630512096991484012026