How a news site uses social network mapping to reduce polarization

Aemula is a new kind of news media platform that’s trying to tackle a big problem: the fact that the structure of our news media leads to various outcomes that amplify toxic polarization. Instead of the usual “engagement = more exposure” logic, Aemula flips the incentives. You read an article, then you tap a simple Support or Disagree button — and those signals build a living map of Aemula’s community: a 3D social network graph showing how readers, writers, and articles relate (without slapping on ill-defined partisan labels like 'left' and 'right' - labels that often unintentionally amplify us-vs-them, team-based thinking). Aemula creator Don Templeman and I discuss: Why left/right-type labels can be a misleading way to understand beliefs or categorize content; How Aemula uses social network analysis to map out relationships and ideological groupings in an objective, data-driven way; How Aemula’s social network can help define a sort of ideological center, and how promoting content from the widely supported regions of the network can help reduce polarization; How the blockchain aspect of Aemula makes it self-governing and therefore infinitely scalable ; How Aemula’s approach could matter even more in an AI world, where chatbots and LLMs need better sources than “Reddit + Wikipedia”. If you’ve ever felt like the incentives of the media ecosystem seem destined to drive us further apart — I think you’ll appreciate learning about Aemula's paradigm-shifting approach to the news.

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