The Changing Landscape of Automated Content Moderation in 2019

Is 2019 the year that content moderation goes mainstream? We think so.

Things have changed a lot since 1990 when Tim Berners-Lee invented the World Wide Web. A few short years later, the world started to surf the information highway – and we’ve barely stopped to catch our collective breath since.

Learn about the past, present, and future of online content moderation in an upcoming webinar

The internet has given us many wonderful things over the last 30 years – access to all of recorded history, an instant global connection that bypasses country, religious, and racial lines, Grumpy Cat – but it’s also had unprecedented and largely unexpected consequences.

Rampant online harassment, an alarming rise in child sexual abuse imagery, urgent user reports that go unheard – it’s all adding up. Now that well over half of Earth’s population is online (4 billion people as of January 2018), we’re finally starting to see an appetite to clean up the internet and create safe spaces for all users.

The change started two years ago.

Mark Zuckerberg’s 2017 manifesto hinted at what was to come:

“There are billions of posts, comments, and messages across our services each day, and since it’s impossible to review all of them, we review content once it is reported to us. There have been terribly tragic events — like suicides, some live streamed — that perhaps could have been prevented if someone had realized what was happening and reported them sooner. There are cases of bullying and harassment every day, that our team must be alerted to before we can help out. These stories show we must find a way to do more.”

In 2018, the industry finally realized that it was time to find solutions to the problems outlined in Facebook’s manifesto. The question was no longer, “Should we moderate content on our platforms?” and instead became, “How can we better moderate content on our platforms?”

Play button on a film stripLearn how you can leverage the latest advances in content moderation in an upcoming webinar

The good news is that in 2019, we have access to the tools, technology, and years of best practices to make the dream of a safer internet a reality. At Two Hat, we’ve been working behind the scenes for nearly seven years now (alongside some of the biggest games and social networks in the industry) to create technology to auto-moderate content so accurately that we’re on the path to “invisible AI” – filters that are so good you don’t even know they’re in the background.

On February 20th, we invite you to join us for a very special webinar, “Invisible AI: The Future of Content Moderation”. Two Hat CEO and founder Chris Priebe will share his groundbreaking vision of artificial intelligence in this new age of chat, image, and video moderation.

In it, he’ll discuss the past, present, and future of content moderation, expanding on why the industry shifted its attitude towards moderation in 2018, with a special focus on the trends of 2019.

He’ll also share exclusive, advance details about:

We hope you can make it. Give us 30 minutes of your time, and we’ll give you all the information you need to make 2019 the year of content moderation.

PS: Another reason you don’t want to miss this – the first 25 attendees will receive a free gift! ; )


Read about Two Hat’s big announcements:

Two Hat Is Changing the Landscape of Content Moderation With New Image Recognition Technology

Two Hat Leads the Charge in the Fight Against Child Sexual Abuse Images on the Internet

Two Hat Releases New Artificial Intelligence to Moderate and Triage User-Generated Reports in Real Time

 

The Future of Image Moderation: Why We’re Creating Invisible AI (Part Two)

Yesterday, we announced that Two Hat has acquired image moderation service ImageVision. With the addition of ImageVision’s technology to our existing image recognition tech stack, we’ve boosted our filter accuracy — and are determined to push image moderation to the next level.

Today, Two Hat CEO and founder Chris Priebe discusses why ImageVision was the ideal choice for a technology acquisition— and how he hopes to change the landscape of image moderation in 2019.

We were approached by ImageVision over a year ago. Their founder Steven White has a powerful story that led him to found the company (it’s his to tell so I won’t share). His story resonated with me and my own journey of why I founded Two Hat. He spent over 10 years perfecting his art. He had clients with Facebook, Yahoo, Flickr, and Apple. That is 10 years of experience and over $10 million in investment to solve the problems of accurately detecting pornographic images.

Of course 10 years ago we all did things differently. Neural networks weren’t popular yet. Back then, you would look at how much skin tone was in an image. You looked at angles and curves and how they relate to each other. ImageVision made 185 of these hand-coded features.

Later they moved on to neural networks but ImageVision did something amazing. They took their manually coded features and fed both them and the pixels into the neural network. And they got a result different from what everyone else was doing at the time.

Now here is the reality — there is no way I’m going to hire people to write nearly 200 manually coded features in this modern age. And yet the problem of child sexual abuse imagery is so important that we need to throw every resource we can at it. It’s not good enough to only prevent 90% of exploitation — we need all the resources we can get.

Like describing an elephant

So we did a study. We asked, “What would happen if we took several image detectors and mixed them together? Would they give a better answer than any alone?”

It’s like the story of several blind men describing an elephant. One describes a tail, another a trunk, another a leg. They each think they know what an elephant looks like, but until they start listening to each other they’ll never actually “see” the real elephant. Likewise in AI, some systems are good at finding one kind of problem and another at another problem. What if we trained another model (called an ensemble) to figure out when each of them is right?

For our study, we took 30,000 pornographic images and 55,000 clean images. We used ImageVision images since they are full of really hard ones to find; the kind of images you might actually see in real life and not just a lab experiment. The big cloud providers found between 89-98% of pornographic images out of all 30k images, while the precision rate was around 95-98% for all of them (precision refers to the proportion of positive identifications that are correct).

We were excited that our current system found most of the images, but we wanted to do better.

For the CEASE.ai project, we had to create a bunch of weak learners to find CSAM. Detecting CSAM is such a huge problem that we needed to throw everything we could at it. So we ensembled the weak learners all together to see what would happen — and we got another 1% of accuracy, which is huge because the gap from 97% to 100% is the hardest to close.

But how do you close the last 2%? This is where millions of dollars and decades of experience are critical. This is where we must acquire and merge every trick in the book. When we took ImageVision’s work and merged it with our own, we squeezed out another 1%. And that’s why we bought them.

We’re working on a white paper where we’ll present our findings in further detail. Stay tuned for that soon.

The final result

So if we bought ImageVision, not only would we gain 10 years of experience, multiple patents, and over $10 million in technology, but we would be the best NSFW detector in the industry. And if we added that into our CSAM detector (along with age detection, face detection, body part detection, and abuse detection) then we could push that accuracy even closer and hopefully save more kids from the horrors of abuse. Spending money to solve this problem was a no-brainer for us.

Today, we’re on the path to making AI invisible.


Learn more about Priebe’s groundbreaking vision of artificial intelligence in an on-demand webinar. He shares more details about the acquisition, CEASE.ai, and the content moderation trends that will dominate 2019. Register to watch the webinar here.

Further reading:

Part One of The Future of Image Moderation: Why We’re Creating Invisible AI
Official ImageVision acquisition announcement
Learn about CSAM detection with CEASE.ai on our site

The Future of Image Moderation: Why We’re Creating Invisible AI (Part One)

In December and early January, we teased exciting Two Hat news coming your way in the new year. Today, we’re pleased to share our first announcement of 2019 — we have officially acquired ImageVision, an image recognition and visual search company. With the addition of ImageVision’s groundbreaking technology, we are now poised to provide the most accurate NSFW image moderation service in the industry.

We asked Two Hat CEO and founder Chris Priebe to discuss the ambitious technology goals that led to the acquisition. Here is part one of that discussion:

The future of AI is all about quality. Right now the study of images is still young. Anyone can download TensorFlow or PyTorch, feed it a few thousand images and get a model that gets things right 80-90% of the time. People are excited about that because it seems magical – “They fed a bunch of images into a box and it gave an answer that surprisingly right most of the time!” But even if you get 90% right, you are still getting 10% wrong.

Think of it this way: If you do 10 million images a day that is a million mistakes. A million times someone tried to upload a picture that was innocent and meaningful to them and they had to wait for a human to review it. That is one million images humans need to review. We call those false positives.

Worse than false positives are false negatives, where someone uploads an NSFW (not safe for work) picture or video and it isn’t detected. Hopefully, it was a mature adult who saw it. Even if it was an adult, they weren’t expecting to see adult content, so their trust in the site is in jeopardy. They’re probably less likely to encourage a friend to join them on the site or app.

Worse if it was a child who saw it. Worst of all if it is a graphic depiction of a child being abused.

Protecting children is the goal

That last point is closest to our heart. A few years ago we realized that what really keeps our clients awake at night is the possibility someone will upload child sexual abuse material (CSAM; also known as child exploitive imagery, or CEI, and formerly called child pornography) to their platform. We began a long journey to solve that problem. It began with a hackathon where we gathered some of the largest social networks in the world with international law enforcement and academia all in the same room and attempted to build a solution together.

So AI must mature. We need to get beyond a magical box that’s “good enough” and push it until AI becomes invisible. What do I mean by invisible? For us, that means you don’t even notice that there is a filter because it gets it right every time.

Today, everyone is basically doing the same thing, like what I described earlier — label some NSFW images and throw them at the black box. Some of us are opening up the black box and changing the network design to hotrod the engine, but for the most part it’s a world of “good enough”.

Invisible AI

But in the future, “good enough” will no longer be tolerated. The bar of expectation will rise and people will expect it to just work. From that, we expect companies to hyper-specialize. Models will be trained that do one thing really, really well. Instead of a single model that answers all questions, instead, there will be groups of hyper-specialists with a final arbiter over them deciding how to best blend all their opinions together to make AI invisible.

We want to be at the top of the list for those models. We want to be the best at detecting child abuse, bullying, sextortion, grooming, and racism. We are already top of the market in several of those fields and trusted by many of the largest games and social sharing platforms. But we can do more.

Solving the biggest problems on the internet

That’s why we’ve turned our attention to acquiring. These problems are too big, too important to have a “not built here, not interested” attitude. If someone else has created a model that brings new experience to our answers, then we owe it our future to embrace every advantage we can get.

Success for me means that one day my children will take for granted all the hard work we’re doing today. That our technology will be invisible.

In part two, Chris discusses why ImageVision was the ideal choice for a technology acquisition— and how he hopes to change the landscape of image moderation in 2019.

Sneak peek:

“It’s like the story of several blind men describing an elephant. One describes a tail, another a trunk, another a leg. They each think they know what an elephant looks like, but until they start listening to each other they’ll never actually “see” the real elephant. Likewise in AI, some systems are good at finding one kind of problem and another at another problem. Could we train another model (called an ensemble) to figure out when each of them is right?”

 

Read the official ImageVision acquisition announcement
Learn about CSAM detection with CEASE.ai on our site

New Research Suggests Sentiment Analysis is Critical in Content Moderation

At Two Hat, research is the foundation of everything we do. We love to ask big questions and seek even bigger answers. And thanks to a generous grant from Mitacs, we’ve partnered with leading Canadian universities to conduct research into the subjects that we’re most passionate about — from protecting children by detecting child sexual abuse material to developing new and innovative advances in chat moderation.

Most recently, Université Laval student researcher Éloi Brassard-Gourdeau and professor Richard Khoury asked the question “What is the most accurate and effective way to detect toxic (also known as disruptive) behavior in online communities?” Specifically, their hypothesis was:

“While modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder.”

They wanted to see if sentiment analysis was more effective than keyword detection when identifying disruptive content like abuse and hate speech in online communities.

Definitions of sentiment analysis, toxicity, subversion, and keywords in content moderationIn Impact of Sentiment Detection to Recognize Toxic and Subversive Online Comments, Brassard-Gourdeau and Khoury analyzed over a million online comments using one Reddit and two Wikipedia datasets. The results show that sentiment information helps improve toxicity detection in all cases. In other words, the general sentiment of a comment — whether it’s positive or negative — is a more effective measure of toxicity than just keyword analysis.

But the real boost came when they used sentiment analysis on subversive language; that is, when users attempted to mask sentiment using L337 5p33k, deliberate misspellings, and word substitutions. According to the study, “The introduction of subversion leads to an important drop in the accuracy of toxicity detection in the network that uses the text alone… using sentiment information improved toxicity detection by as much as 3%.

You may be asking yourself, why does this matter? With chat moderation becoming more common in games and social apps, more users will find creative ways to subvert filters. Even the smartest content moderation tools on the market (like Two Hat’s Community Sift, which uses a unique AI called Unnatural Language Processing to detect complex manipulations), will find it increasingly difficult to flag disruptive content. As an industry, it’s time we started looking for innovative solutions to a problem that will only get harder in time.

In addition to asking big questions and seeking even bigger answers, we have several foundational philosophies at Two Hat that inform our technology. We believe that computers should do computer work and humans should do human work, and that an ensemble approach is key to exceptional AI.

This study validates our assumption that using multiple data points and multiple models in automated moderation algorithms are critical in boosting accuracy and ensuring a better user experience.

“We are in an exciting time in AI and content moderation,” says Two Hat CEO and founder Chris Priebe. “I am so proud of our students and the hard work they are doing. Every term they are pushing the boundaries of what is possible. Together, we are unlocking more and more pieces to the recipe that will one day make an Internet where people can share without fear of harassment or abuse.

To learn more, check out the full paper here.

Keep watching this space for more cutting-edge research. And stay tuned for major product updates and product launches from Two Hat in 2019!