An internal Facebook report found that the social media platform’s algorithms – the rules its computers follow in deciding the content that you see – enabled disinformation campaigns based in Eastern Europe to reach nearly half of all Americans in the run-up to the 2020 presidential election, according to a report in Technology Review.
The campaigns produced the most popular pages for Christian and Black American content, and overall reached 140 million U.S. users per month. Seventy-five percent of the people exposed to the content hadn’t followed any of the pages. People saw the content because Facebook’s content-recommendation system put it into their news feeds.
Social media platforms rely heavily on people’s behavior to decide on the content that you see. In particular, they watch for content that people respond to or “engage” with by liking, commenting and sharing. Troll farms, organizations that spread provocative content, exploit this by copying high-engagement content and posting it as their own.
As a computer scientist who studies the ways large numbers of people interact using technology, I understand the logic of using the wisdom of the crowds in these algorithms. I also see substantial pitfalls in how the social media companies do so in practice.
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