AI Catches Fake News By Gauging The Accuracy Of Its Source

The staff used Media Bias/Truth Test’s evaluation of 2,000-plus news outlets to train the algorithm to search for the telltale linguistic cues of sites that push fake or heavily distorted information tales. These organizations are inclined to rely on hyperbolic, emotion-driven language like “crisis actor,” “demonic” and “false flag,” as an example. A left-biased site might discuss “fairness” and “reciprocity,” whereas a conservative site may mention “authority” and “sanctity.” The machine studying method could even draw links between a site’s authenticity and its Wikipedia page (longer is usually extra reliable) or net addresses (complicated ones are more likely to contain fake news).

We visited all three capital cities within the Caucasus area – Yerevan in Armenia, Baku in Azerbaijan, and Tbilisi in Georgia – in 2019 to uncover the main emerging technology startups within the region. We discovered a budding tech startup ecosystem pushed by a robust entrepreneurial spirit, much wanted, since VC funding is missing.

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