Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity

U.S. political discourse seems to have fissioned into discrete bubbles, each reflecting its own distorted image of the world. Many blame machine-learning algorithms that purportedly maximize "engagement"--serving up content that keeps YouTube or Facebook users watching videos or scrolling through their feeds--for radicalizing users or strengthening their partisanship. Sociologist Shoshana Zuboff15 even argues that "surveillance capitalism" uses optimized algorithmic feedback for "automated behavioral modification" at scale, writing the "music" that users then "dance" to. There is debate whether such algorithms in fact maximize engagement (their objective functions also typically contain other desiderata). More recent research3 offers an alternative explanation, suggesting that people consume this content because they want it, independent of the algorithm.