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"Fake news" is an interesting concept, because it was originally coined to mean fly-by-night websites that looked indistinguishable from actual, legitimate members of the media, and often attempted to impersonate those members. This was a technical definition. It got co-opted (very effectively, I must admit) by pro-Trump partisans to mean "biased news," which is a much more subjective definition. Unlike the original definition, "fake news" can be applied to articles, not to sites, although echoes of the first definition are used to impugn an entire news organization for getting a single story or a single detail wrong (which happens!). By the original definition, the New York Times or CNN is never "fake news," as long as it's the actual Times and actual CNN you're looking at. By the new definition, they're "fake news" if you disagree with their editorial decisions about what's newsworthy. The original "fake news" problem, being a technical problem, can I think be legitimately addressed by technical means. It's been over 20 years since Google's founders first started tracking reputation as a way of determining worthwhile search results vs. worthless ones, and that's been extraordinarily effective. Something similar could help track whether the site you're reading is a well-known news entity or a quickly-cobbled-together WordPress. Once you know the site has been around enough to have a reputation, it's up to you to decide whether it's worth your time. And for a website impersonating a well-known one instead of presenting its own (new) brand, this becomes equivalent to the phishing problem, which is a technical problem solvable by technical solutions. The fundamental assumption, of course, is that people are interested in truth and not merely what their itching ears want to hear. But hopefully they are. |