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April 28, 2017, 12:54 p.m.
LINK: medium.com  ➚   |   Posted by: Joseph Lichterman   |   April 28, 2017

In the months since the U.S. presidential election, there have been many proposed solutions for how platforms such as Facebook and Google can deal with misinformation and fake news. This week another possible solution emerged — this time from someone with significant experience with how platforms deal with news.

On Thursday, Google News creator Krishna Bharat published a Medium post that outlined a way that platforms could use a combination of algorithms and human editors to cut off certain stories as they begin to gain traction through shares, searches, and other types of engagement. He describes this phenomenon as a wave.

Given the scale of social media, Bharat wrote that it makes most sense for the platforms to address waves that display attributes associated with fake news once they’ve hit a certain threshold:

To make this concrete: Let us say that a social media platform has decided that it wants to fully address fake news by the time it gets 10,000 shares. To achieve this they may want to have the wave flagged at 1,000 shares, so that human evaluators have time to study it and respond. For search, you would count queries and clicks rather than shares and the thresholds could be higher, but the overall logic is the same.

To prove how this system could work, Bharat gave the example of the infamous false story that Pope Francis endorsed Donald Trump for president. Bharat says that the system could trigger editors when waves reach a certain scale. Ultimately, the algorithm would learn from the editors’ decisions and improve over time:

To do this at scale, an algorithm would look at all recent articles (from known and obscure sources) that have been getting some play in the last 6–12 hours on a particular social network or search engine. To limit the scope we could require a match with some trigger terms (e.g., names of politicians, controversial topics) or news categories (e.g., politics, crime, immigration). This would reduce the set to around 10,000 articles. These articles can be analyzed and grouped into story buckets, based on common traits — significant keywords, dates, quotes, phrases, etc. None of this is technically complex. Computer Scientists have been doing this for decades and call this “document clustering.”

Articles that land in a given story bucket would be talking about the same story. This technique has been used successfully in Google News and Bing News, to group articles by story and to compare publishing activity between stories. If two different sources mention “pope” and “Trump” and some variant of the term “endorsed” within a short time window then their articles will end up in the same bucket. This essentially helps us capture the full coverage of a story, across various news sources. Add in the social context, i.e., the posts that refer to these articles, and you have the full wave. Most importantly this allows us to figure out comprehensively which sources and authors are propagating this news and which are not.

While Bharat’s solution might be a sensible way to address the problem of misinformation on the platforms, there’s another question about whether Facebook and Google will want to follow suit. Despite its white paper this week on how propaganda has thrived on the platform, Facebook has been reluctant to use human editors after its controversy last year.

“The biggest challenge to stopping fake news is not technical,” Bharat wrote. “It is operational willingness.”

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