Filterworld: How Algorithms Flattened Culture

by Kyle Chayka

After the 2016 election of Donald Trump, the American public became slightly more aware of how we were being manipulated by algorithmic feeds. Democrats couldn’t understand how anyone had voted for Trump, given that their Facebook and Twitter feeds didn’t promote as many posts from the other side of the political spectrum, creating one of Eli Pariser’s filter bubbles, a digital echo chamber. Online, they lived in an illusion of total agreement that Trump was ridiculous. At the same time, his supporters were surrounded by content that reinforced their own views—another form of homogeneity. Recommender systems had sorted the audiences into two neat categories that didn’t need to overlap, whereas in a human-edited newspaper or television news program, some mutual exposure might have been more likely. More likely but not guaranteed: traditional media can be biased into homogeneity just as well, as in The New York Times’s unwillingness to cover the possibility of a Trump win.