Google and Storyful have launched Source, an app to help journalists fact-check online images and screenshots.
Journalists and fact-checkers face huge challenges in sorting accurate information from fast-spreading misinformation. But it’s not just about the words we read. Viral images and memes flood our feeds and chats, and often they’re out-of-context or fake.
Traditionally, journalists and fact checkers often use reverse image search to see if a viral image was real or fake news, but this is difficult because the recency bias in online search means that reprints from the past few hours or days often dominate search results. The Source app instead finds an image’s “public history"—even if it has been heavily altered since its original publication—and highlights whether it has previously shown up on whitelisted or blacklisted domains. It also automatically extracts and translates text from images, making it easier for journalists to catalogue and search them. The app is a good example of productized AI because it takes a few machine learning tasks that have been solved to the point of being accessible as simple API calls (in this case: reverse image search, optical character recognition, and machine translation) and wraps them up into an easy-to-use tool for non-expert users.
A while ago I toyed around with the idea of making a tool to extract text from screenshots of tweets, and using that to trace back whether the original tweet still exists (or if it ever did). I wanted to then hook that up to a twitter bot that you could tag to check whether a screenshot of a tweet was real or fake. I never got around to building it, partially because of restrictions on Twitter’s API, but mostly because of privacy concerns (I didn’t want to enable/encourage doxxing
). But this automatic tweet verifying does seem like it could be very useful for journalists using Source, so I’m excited to see whether Google and Storyful will add features like this to the app as they develop it further.