Agoracom Blog

How #Facebook is using #AI to combat #COVID19 misinformation and detect ‘hateful memes’ – SPONSOR: Datametrex AI Limited $

Posted by AGORACOM-JC at 5:46 PM on Tuesday, May 12th, 2020

SPONSOR: Datametrex AI Limited (TSX-V: DM) A revenue generating small cap A.I. company that NATO and Canadian Defence are using to fight fake news & social media threats. The company is working with US Government agencies on Covid19 and Coronavirus fake news and disinformation. The company also obtained the rights to import and sell COVID-19 test kits from South Korea – Click here for more info.

How Facebook is using AI to combat COVID-19 misinformation and detect ‘hateful memes’

By Nick Statt

Facebook on Monday released a new report detailing how it uses a combination of artificial intelligence and human fact-checkers and moderators to enforce its community standards. The report — called the Community Standards Enforcement Report, which usually encompasses data and findings from the prior three to six months — has a large focus on AI this time around.

That’s because Facebook is relying more on the technology to help moderate its platform during the COVID-19 pandemic, which is preventing the company from using its usual third-party moderator firms because those firms’ employees are not allowed to access sensitive Facebook data from home computers.

That said, Facebook says the data it’s compiled so far doesn’t contain any larger trends in its enforcement or in offending behavior on its platform because the pandemic hit so late in its reporting period. “This report includes data only through March 2020 so it does not reflect the full impact of the changes we made during the pandemic,” writes Guy Rosen, the company’s vice president of integrity, in a blog post. “We anticipate we’ll see the impact of those changes in our next report, and possibly beyond, and we will be transparent about them.’

Given the state of the world, Facebook’s report does contain new information about how the company is specifically combating coronavirus-related misinformation and other forms of platform abuse, like price gouging on Facebook Marketplace, using its AI tools. Facebook put warning labels on 50 million coronavirus-related posts last month

“During the month of April, we put warning labels on about 50 million posts related to COVID-19 on Facebook, based on around 7,500 articles by our independent fact-checking partners,” the company said in a separate blog post, penned by a group of its research scientists and software engineers, about its ongoing COVID-19 misinformation efforts published today. “Since March 1st, we’ve removed more than 2.5 million pieces of content for the sale of masks, hand sanitizers, surface disinfecting wipes and COVID-19 test kits. But these are difficult challenges, and our tools are far from perfect. Furthermore, the adversarial nature of these challenges means the work will never be done.”

Facebook says its labels are working: 95 percent of the time, someone who is warned that a piece of content contains misinformation will decide not to view it anyway. But producing those labels across its enormous platform is proving to be a challenge. For one, Facebook is discovering that a fair amount of misinformation as well as hate speech is now showing up in images and videos, not just text or article links.

“We have found that a substantial percentage of hate speech on Facebook globally occurs in photos or videos,” the company says in a separate hate speech-specific blog post about its recent moderation findings and research. “As with other content, hate speech also can be multimodal: A meme might use text and image together to attack a particular group of people, for example.”

This is a tougher challenge for AI to tackle, the company admits. Not only do AI-trained models have a harder time parsing a meme image or a video due to complexities like wordplay and language differences, but that software must also then be trained to find duplicates or only marginally modified versions of that content as it spreads across Facebook. But this is precisely what Facebook says it’s achieved with what it calls SimSearchNet, a multiyear effort across many divisions within the company to train an AI model how to recognize both copies of the original image and those that are near-duplicates and have perhaps one word in the line of text changed. “We have found that a substantial percentage of hate speech on Facebook globally occurs in photos or videos.”

“Once independent fact-checkers have determined that an image contains misleading or false claims about coronavirus, SimSearchNet, as part of our end-to-end image indexing and matching system, is able to recognize near-duplicate matches so we can apply warning labels,” the company says. “This system runs on every image uploaded to Instagram and Facebook and checks against task-specific human-curated databases. This accounts for billions of images being checked per day, including against databases set up to detect COVID-19 misinformation.”

Facebook uses the example of a misleading image modeled after a broadcast news graphic with a line of overlaid text reading, “COVID-19 is found in toilet paper.” The image is from a known peddler of fake news called Now8News, and the graphic has since been debunked by Snopes and other fact-checking organizations. But Facebook says it had to train its AI to differentiate between the original image and a modified one that says, “COVID-19 isn’t found in toilet paper.”

The goal is to help reduce the spread of duplicate images while also not inadvertently labeling genuine posts or those that don’t meet the bar for misinformation. This is a big problem on Facebook where many politically motivated pages and organizations or those that simply feed off partisan outrage will take photographs, screenshots, and other images and alter them to change their meaning. An AI model that knows the difference and can label one as misinformation and the other as genuine is a meaningful step forward, especially when it can then do the same to any duplicate or near-duplicate content in the future without roping in non-offending images in the process.


Tags: , , , , ,

Comments are closed.