Summary of Why Good People Turn Bad Online

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Picture your ancestors. Living in small communities, they interacted face-to-face with the same people every day. Social consequences were immediate. Bad behavior could lead to ostracism from the community and the loss of long-term survival advantages. It paid to be agreeable, so humans evolved to be agreeable. Now contrast that scenario with today’s online environment. Where one led to cooperation, the other has led to trolling and tribalism. getAbstract recommends writer and broadcaster Gaia Vince’s overview of how algorithms prompt the worst in human behavior to all Internet users.

In this summary, you will learn

  • Why algorithms select for emotional content and create online echo-chambers,
  • How the online environment and natural human instincts interact to produce online abuse, and
  • Which interventions have proven successful in limiting online abuse.

About the Author

Gaia Vince is the author of Adventures in the Anthropocene: A Journey to the Heart of the Planet We Made.



Researchers have used “public goods games” to observe how people behave in situations that test cooperation. These games reveal that most people’s immediate instinct is to cooperate for the greater good. Though humans seem to have evolved for cooperation, the online environment lacks characteristics that favor these traits. Anonymity and physical distance mean that people are unlikely to face negative repercussions for their online behavior. Meanwhile, algorithms prioritize content that increases engagement, and moral outrage is a significant driver of that engagement. Studies show that acting on moral outrage stimulates the brain’s reward centers and – while it might feel good to tell others that they’re wrong – it’s even better with an audience cheering from the sidelines. Mix this with the echo-chambers produced by algorithms, and the Internet becomes a recipe for trolls. Women and people of color receive the brunt of the abuse. Eventually, harassed groups are less likely to participate in online discussions, reducing online diversity.

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