Later On

A blog written for those whose interests more or less match mine.

We need an antidote for fake news

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Fake news infects and pollutes cooperation and productive discussion, and thus acts as a disease in the body politic, weakening it and making it vulnerable to attack. For a strong community and country, we must find an effective antibody to fake news. Leon Derczynski reports in Medium:

The accuracy of our media has been placed under constant question, with many claims being called out as false, or fake. Sometimes, this is done because someone doesn’t like a story or fact in the media; other times, the news really is fake.

pathology n.: the study of diseases and of the changes that they cause –Merriam

This post shows a story originating in the Middle East, about Russian soldiers clearing up bombs left in Syria by Obama’s troops. The story was related using first-hand video and personal accounts, and was picked up by major outlets. However, the truth was that this story was completely false — fabricated and framed in such a way that it looked like real news. We’ll pull on threads behind this fake news, and find just one small part of what may well be a large, international network that is feeding our Western media.

I’ve been working on rumors, fake news and so on for a long time. In 2012, we proposed a multinational project on truth in online media, Pheme, which was funded with €4.3M by the European Commission. The project lasted a little over three years, finishing Spring 2017. We spent the time building definitions of fake news, models of rumors, and tools for picking up untrue claims (a tough project!). We even ran an evaluation where teams from all over the world tried to pick out fake stories from real ones, RumorEval. This was perfect timing, with 2016 being the explosive year that it was and “fake news” becoming a hot term just as our project reached its peak.

In the project, we take in huge amounts of data from the web, streaming in various social media sites, news sites, and so on. These need to be linked and grouped so that our journalists (from can readily digest content and see what claims and stories are emerging. This grouping and linking is tough, and to understand how to do it, you have to look at the data really close up. And when you do that, you start to see recurring patterns and behaviors in the tweets and stories on the web.

Let’s start with one story. This is, as it goes, a fairly neutral bit of data; it’s video footage from the destroyed Syrian city of Aleppo, of soldiers in Russian uniforms operating a piece of equipment in the streets. The caption tells us that these are Russian soldiers, using a bomb-disposal device to make the streets safer. This footage is captured and relayed by Ruptly TV, a German-based media organisation, on January 4, 2017. . . .

Continue reading. There’s a lot more. The story evolves, but by artificial selection rather than natural selection (cf. breeding of animals in captivity vs. animals breeding in the wild).

The problem in using AI as the clean-up agent is, I imagine, that it is extremely difficult to distinguish fake news and advertising.

Written by LeisureGuy

11 June 2017 at 3:59 pm

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