Can Community Sift Outperform Google Jigsaw’s Conversation AI in the War on Trolls?
There are some problems in the world that everyone should be working on, like creating a cure for cancer and ensuring that everyone in the world has access to clean drinking water.
On the internet, there is a growing epidemic of child exploitative content, and it is up to us as digital service providers to protect users from illegal and harmful content. Another issue that’s been spreading is online harassment — celebrities, journalists, game developers, and many others face an influx of hate speech and destructive threats on a regular basis.
Harassment is a real problem — not a novelty startup idea like ‘the Uber for emergency hairstylists.’ Cyberbullying and harassment are problems that affect people in real-life, causing them psychological damage, trauma, and sometimes even causing people to self-harm or take their own lives. Young people are particularly susceptible to this, but so are many adults. There is no disconnect between our virtual lives and our real lives in our interconnected, mesh-of-things society. Our actual reality is already augmented.
Issues such as child exploitation, hate speech, and harassment are problems we should be solving together.
We are excited to see that our friends at Alphabet (Google) are publicly joining the fray, taking proactive action against harassment. The internal incubator formerly known as Google Ideas will now be known as Jigsaw, with a mission to make people in the world safer. It’s encouraging to see that they are tackling the same problems that we are — countering extremism and protecting people from harassment and hate speech online.
Like Jigsaw, we also employ a team of engineers, scientists, researchers, and designers from around the world. And like the talented folks at Google, we also collaborate to solve the really tough problems using technology.
There are also some key differences in how we approach these problems!
Since the Two Hat Security team started by developing technology solutions for child-directed products, we have unique, rich, battle-tested experience with conversational subversion, grooming, and cyberbullying. We’re not talking about sitting on the sidelines here — we have hands-on experience protecting kids’ communities from high-risk content and behaviours.
Our CEO, Chris Priebe, helped code and develop the original safety and moderation solutions for Club Penguin, the children’s social network with over 300 million users acquired by The Walt Disney Company in 2007. Chris applied what he’s learned over the past 20 years of software development and security testing to Community Sift, our flagship product.
At Two Hat, we have an international, native-speaking team of professionals from all around the world — Italy, France, Germany, Brazil, Japan, India, and more. We combine their expertise with computer algorithms to validate their decisions, increase efficiency, and improve future results. Instead of depending on crowdsourced results (which require that users are forced to see a message
before they can report it), we focus on enabling platforms to sift out messages before they are deployed.
Google vs. Community Sift — Test Results
In a recent article published in Wired, writer Andy Greenberg put Google Jigsaw’s Conversation AI to the test. As he rightly stated in his article, “Conversation AI, meant to curb that abuse, could take down its own share of legitimate speech in the process.” This is exactly the issue we have in maintaining Community Sift — ensuring that we don’t take down legitimate free speech in the process of protecting users from hate speech.
We thought it would be interesting to run the same phrases featured in the Wired article through Community Sift to see how we’re measuring up. After all, the Google team sets a fairly high bar when it comes to quality!
From these examples, you can see that our human-reviewed language signatures provided a more nuanced classification to the messages than the artificial intelligence did. Instead of starting with artificial intelligence assigning risk, we bring conversation trends and human professionals to the forefront, then allow the A.I. to learn from their classifications.
Here’s a peak behind the scenes at some of our risk classifications.
We break apart sentences into phrase patterns, instead of just looking at the individual words or the phase on its own. Then we assign other labels to the data, such as the user’s reputation, the context of the conversation, and other variables like vertical chat to catch subversive behaviours, which is particularly important for child-directed products.
Since both of the previous messages contain a common swearword, we need to classify that to enable child-directed products to filter this out of their chat. However, in this context, the message is addressing another user directly, so it is at higher risk of escalation.
This phrase, while seemingly harmless to an adult audience, contains some risk for younger demographics, as it could be used inappropriately in some contexts.
As the Wired writer points out in his article, “Inside Google’s Internet Justice League and Its AI-Powered War on Trolls”, this phrase is often a response from troll victims to harassment behaviours. In our system, this is a lower-risk message.
The intention of our classification system is to empower platform owners to make informed and educated decisions about their content. Much like how the MPAA rates films or the ESRB rates video games, we rate user-generated content to empower informed decision-making.
Trolls vs. Regular Users
We’re going to go out on a limb here and say that every company cares about how their users are being treated. We want customers to be treated with dignity and respect.
Imagine you’re the owner of a social platform like a game or app. If your average cost of acquisition sits at around $4, then it will cost you a lot of money if a troll starts pushing people away from your platform.
Unfortunately, customers who become trolls don’t have your community’s best interests or your marketing budget in mind — they care more about getting attention… at any cost. Trolls show up on a social platform to get the attention they’re not getting elsewhere.
Identifying who these users are is the first step to helping your community, your product, and even the trolls themselves. Here at Two Hat, we like to talk about our “Troll Performance Improvement Plans” (Troll PIPs), where we identify who your top trolls are, and work on a plan to give them a chance to reform their behaviour before taking disciplinary action. After all, we don’t tolerate belligerent behaviour or harassment in the workplace, so why would we tolerate it within our online communities?
Over time, community norms set in, and it’s difficult to reshape those norms. Take 4chan, for example. While this adult-only anonymous message board has a team of “volunteer moderators and janitors”, the site is still regularly filled with trolling, flame wars, racism, grotesque images, and pornography. And while there may be many legitimate, civil conversations lurking beneath the surface of 4chan, the site has earned a reputation that likely won’t change in the eyes of the public.
Striking a balance between free speech while preventing online harassment is tricky, yet necessary. If you allow trolls to harass other users, you are inadvertently enabling someone to cause another psychological harm. However, if you suppress every message, you’re just going to annoy users who are just trying to express themselves.
We’ve spent the last four years improving and advancing our technology to help make the internet great again. It’s a fantastic compliment to have a company as amazing as Google jumping into the space we’ve been focused on for so long, where we’re helping social apps and games like Musical.ly, Dreadnought, PopJam, and ROBLOX.
Having Google join the fray shows that harassment is a big problem worth solving, and it also helps show that we have already made some tremendous strides to pave the way for them. We have had conversations with the Google team about the Riot Games’ experiments and learnings about toxic behaviours in games. Seeing them citing the same material is a great compliment, and we are honored to welcome them to the battle against abusive content online.
Back at Two Hat, we are already training the core Community Sift system on huge data sets — we’re under contract to process four billion messages a day across multiple languages in real-time. As we all continue to train artificial intelligence to recognize toxic behaviors like harassment, we can better serve the real people who are using these social products online. We can empower a freedom of choice for users to allow them to choose meaningful settings, like opting out of rape threats if they so choose. After all, we believe a woman shouldn’t have to self-censor herself, questioning whether that funny meme will result in a rape or death threat against her family. We’d much rather enable people to censor out inappropriate messages from those special kind of idiots who threaten to rape women.
While it’s a shame that we have to develop technology to curb behaviours that would be obviously inappropriate (and in some cases, illegal) in real-life, it is encouraging to know that there are so many groups taking strides to end hate speech now. From activist documentaries and pledges like The Bully Project, inspiring people to stand up against
bullying, to Alphabet/Google’s new Jigsaw division, we are on-track to start turning the negative tides in a new direction. And we are proud to be a part of such an important movement.