It’s great to see a big player like Facebook take on a challenging subject in such a big way. They understand that to create a safe and thriving community, it’s always better to be proactive than to do nothing. Facebook is demonstrating its commitment to creating a safe, supportive, and inclusive community with these new tools. We expect to see more and more features like this in the months to come.
Suicide is one of the biggest issues facing social networks today. The internet is full of self-injury and suicidal language, images, and videos. If we want to build communities where users feel safe and find a place they can call home, then we’re also responsible for ensuring that at-risk users are given help and support when they need it most.
Facebook has over 1.86 billion monthly active users, so they have access to data and resources that other companies can only dream of. Every community deserves to be protected from dangerous content. Is there anything smaller companies can do to keep their users safe?
After years in the industry studying high-risk, dangerous content we have unique insight into this issue.
There are a few things we’ve learned about self-injury and suicidal language:
Using AI to build an automation workflow is crucial. Suicide happens in real time, so we can’t afford mistakes or reactions after-the-fact. If you can identify suicidal language as it happens, you can also use automation to push messages of hope, provide suicide and crisis hotline numbers, and suggest other mental health resources. With their new features, Facebook has taken a huge, bold step in this direction.
Suicidal language is complex. If you want to identify suicidal language, you need a system that recognizes nuance, looks for hidden (unnatural) meaning and understands context and user reputation. There is a huge difference between a user saying “I am going to kill myself” versus “You should go kill yourself.” One is a cry for help, and the other is bullying. So it’s vital that your system learns the difference because they require two very different responses.
Think about all the different ways someone could spell the word “suicide.” Does your system read l337 5p34k? What if “suicide” is hidden inside a string of random letters?
Chris Priebe, CEO and founder of Two Hat Security (creator of Community Sift) wrote a response to Mark’s initial manifesto. In it he wrote:
When it comes to cyber-bullying, hate-speech, and suicide the stakes are too high for the current state of art in NLP [Natural Language Processing].
At Two Hat Security, we’ve spent five years building a unique expert system that learns new rules through machine learning, aided by human intelligence. We use an automated feedback loop with trending phrases to update rules and respond in real-time. We call this approach Unnatural Language Processing (uNLP).
When it comes to suicide and other high-risk topics, we aren’t satisfied with traditional AI algorithms that are only 90-95% accurate. We believe in continual improvement. When lives are at stake, you don’t get to rest on your laurels.
Suicide is connected to bullying and harassment. If you want to keep your community safe, you have to deal with all high-risk content. Community guidelines are great, but you need cutting-edge technology to back them up.
We’ve identified a behavioral flow that shows a direct link between cyberbullying/harassment and self-injury/suicide. When users are bullied, they are more likely to turn to suicidal thoughts and self-injuring behavior. It’s important that you filter cyberbullying in your product to prevent vulnerable users from getting caught in a vicious cycle.
While Facebook is doing its part, we want to ensure that all communities have the tools to protect their most vulnerable users. If you’re concerned about high-risk content in your community, we can help. Our content filter and moderation engine Community Sift is highly tuned to identify sensitive content like suicide and self-injury language.
We believe that everyone should be able to share online without being worried about harassed or threatened. Our goal has always been to remove bullying and other high-risk content from the internet. A big part of that goal involves helping online communities keep their most vulnerable users safe and supported. Suicide is such a sensitive and meaningful issue, so we want to extend our gratitude to Mark and all of the product managers at Facebook for taking a stand.
Here’s to hoping that more social networks will follow.
For many women, logging onto social media is inherently dangerous. Online communities are notoriously hostile towards women, with women in the public eye—journalists, bloggers, and performers—often facing the worst abuse. But abuse is not just the province of the famous. Nearly every woman who has ever expressed an opinion online has had these experiences: Rape threats. Death threats. Harassment. Sometimes, even their children are targeted.
In the last few years, we’ve seen many well-documented cases of ongoing, targeted harassment of women online. Lindy West. Anita Sarkeesian. Leslie Jones. These women were once famous for their talent and success. Now their names are synonymous with online abuse of the worst kind.
And today we add a new woman to the list: Allie Rose-Marie Leost. An animator for EA Labs, her social media accounts were targeted this weekend in a campaign of online harassment. A blog post misidentified her as the lead animator for Mass Effect: Andromeda, and blamed her for the main character’s awkward facial animations. Turns out, Leost never even worked on Mass Effect: Andromeda. And yet she was forced to spend a weekend defending herself against baseless, crude, and sexually violent attacks from strangers.
Clearly, social media has a problem, and it’s not going away anytime soon. And it’s been happening for years.
Young women, those 18-24, experience certain severe types of harassment at disproportionately high levels: 26% of these young women have been stalked online, and 25% were the target of online sexual harassment.
We don’t want to discount the harassment and abuse that men experience online, in particular in gaming communities. This issue affects all genders. However, there is an additional level of violence and vitriol directed at women. And it almost always includes threats of sexual violence. Women are also more likely to be doxxed, the practice of sharing someone else’s personal information online without their consent.
So, what can social networks do to provide safer spaces for women?
First, they need to make clear in their community guidelines that harassment, abuse, and threats are unacceptable —regardless of whether they’re directed at a man or a woman. For too long social networks have adopted a “free speech at all costs” approach to community building. If open communities want to flourish, they have to define where free speech ends, and accountability begins.
Then, social networks need to employ moderation strategies that:
Prevent abuse in real time. Social networks cannot only depend on moderators or users to find and remove harassment as it happens. Not only does that put undue stress on the community to police itself, it also ignores the fundamental problem—when a woman receives a rape threat, the damage is already done, regardless of how quickly it’s removed from her feed.
The best option is to stop abuse in real time, which means finding the right content filter. Text classification is faster and more accurate than it’s ever been, thanks to recent advances in artificial intelligence, machine learning, and Natural Language Processing (NLP).
Our expert system uses a cutting-edge blend of human ingenuity and automation to identify and filter the worst content in real time. People make the rules, and the system implements them.
When it comes to dangerous content like abuse and rape threats, we decided that traditional NLP wasn’t accurate enough. Community Sift uses Unnatural Language Processing (uNLP) to find the hidden, “unnatural” meaning. Any system can identify the word “rape,” but a determined user will always find a way around the obvious. The system also needs to identify the l337 5p34k version of r4p3, the backwards variant, and the threat hidden in a string of random text.
Take action on bad actors in real time. It’s critical that community guidelines are reinforced. Most people will change their behavior once they know it’s unacceptable. And if they don’t, social networks can take more severe action, including temporary or permanent bans. Again, automation is critical here. Companies can use the same content filter tool to automatically warn, mute, or suspend accounts as soon as they post abusive content.
Encourage users to report offensive content. Content filters are great at finding the worst stuff and allowing the best. Automation does the easy work. But there will always be content in between that requires human review. It’s essential that social networks provide accessible, user-friendly reporting tools for objectionable content. Reported content should be funnelled into prioritised queues based on content type. Moderators can then review the most potentially dangerous content and take appropriate action.
Social networks will probably never stop users from attempting to harass women with rape or death threats. It’s built into our culture, although we can hope for a change in the future. But they can do something right now—leverage the latest, smartest technology to identify abusive language in real time.
The numbers indicate that cyberbullying and harassment are huge problems for young people on social media. A 2016 report from the Cyberbullying Research Center indicates that 33.8% of students between 12 and 17 were victims of cyberbullying in their lifetime. Conversely, 11.5% of students between 12 and 17 indicated that they had engaged in cyberbullying in their lifetime.
Cyberbullying is different from “traditional” bullying in that it happens 24/7. For victims, there is no escape. It’s not confined to school or the playground. Kids and teens connect through social media, so for many, there is no option to simply go offline.
Even more troubling is the connection between cyberbullying and child exploitation. At Two Hat Security, we’ve identified a cycle in which child predators groom young victims, who are tricked into taking explicit photos which are then shared online; this leads to bullying and harassment from peers and strangers. Finally, the victim suffers from depression, engages in self-harm, and sometimes — tragically — commits suicide. It’s a heartbreaking cycle.
Cyberbullying and online harassment are profoundly dangerous and alarming behaviors with real, often severe and sometimes fatal, consequences for victims.
Social media platforms have options, though. AI-based text and image filters like Community Sift are the first lines of defense against cyberbullying. Purposeful, focused moderation of User Generated Content (UGC) is the next step. And finally, education and honest, open discussions about the effects of cyberbullying on real victims is crucial. The more we talk about it, the more comfortable victims will feel speaking out about their experiences.
It’s not hard to understand why more and more media companies are inclined to turn off comments. If you’ve spent any time reading the comments section on many websites, you’re bound to run into hate speech, vitriol, and abuse. It can be overwhelming and highly unpleasant. But the thing is, even though it feels like they’re everywhere, hate speech, vitriol, and abuse are only present in a tiny percentage of comments. Do the math, and you find that thoughtful, reasonable comments are the norm. Unfortunately, toxic voices almost always drown out healthy voices.
But it doesn’t have to be that way.
The path of least resistance is tempting. It’s easy to turn off comments — it’s a quick fix, and it always works. But there is a hidden cost. When companies remove comments, they send a powerful message to their best users: Your voice doesn’t matter. After all, users who post comments are engaged, they’re interested, and they’re active. If they feel compelled to leave a comment, they will probably also feel compelled to return, read more articles, and leave more comments. Shouldn’t media companies cater to those users, instead of the minority?
Traditionally, most companies approach comment moderation in one of two ways, both of which are ineffective and inefficient:
Pre-moderation. Costly and time-consuming, pre-moderating everything requires a large team of moderators. As companies scale up, it can become impossible to review every comment before it’s posted.
Crowdsourcing. A band-aid solution that doesn’t address the bigger problem. When companies depend on users to report the worst content, they force their best users to become de facto moderators. Engaged and enthusiastic users shouldn’t have to see hate speech and harassment. They should be protected from it.
My company Two Hat Security has been training and tuning AI since 2012 using multiple unique data sets, including comments sections, online games, and social networks. In our experience, proactive moderation uses a blend of AI-powered automation, human review, real-time user feedback, and crowdsourcing.
It’s a balancing act that combines what computers do best (finding harmful content and taking action on users in real-time) and what humans do best (reviewing and reporting complex content). Skim the dangerous content — things like hate speech, harassment, and rape threats — off the top using a finely-tuned filter that identifies and removes it in real-time. That way no one has to see the worst comments. You can even customize the system to warn users when they’re about to post dangerous content. Then, your (much smaller and more efficient) team of moderators can review reported comments, and even monitor comments as they’re posted for anything objectionable that slips through the cracks.
Comments section don’t have to be the darkest places on the internet. Media companies have a choice — they can continue to let the angriest, loudest, and most hateful voices drown out the majority, or they can give their best users a platform for discussion and debate.
There are a few key steps you can take to build a community that encourages high-quality comments:
Know your community. What kind of articles will you be publishing? Will you cover controversial topics that are likely to elicit passionate responses? What demographic are you targeting? Once you know who will be posting (and what topics they will be posting about), you can start to…
Think long and hard about community guidelines. If you know your community, you can create guidelines to protect. Be clear about your policies. If you allow profanity but not bullying, define bullying for your audience. If you allow racial slurs within the context of a historical article but not when they’re directed at another user, make sure it’s explained in your policy guide.
Build a comprehensive moderation strategy. Visit the comments section of most websites, and you’re bound to walk away with a skewed — and highly unpleasant — view of humanity. Toxic voices will always drown out healthy voices. But it doesn’t have to be that way. If you’re using a blend of smart computer automation and human review to moderate comments, you can build a process that works for your unique community.
Engage with your best users. Who doesn’t appreciate a good shout-out? Encouraging high-quality content can go a long way towards fostering a healthy community. Give your moderators the time to upvote, call out, or comment on quality posts. If you’ve done the first three steps, your moderators will have time to do what people do best, and what computers will likely never do—interact with users and engage them emotionally.
This is by no means an exhaustive list. Your community will grow and change over time, so you may have to adjust your policies as your audience changes. You will probably make mistakes and have to course-correct your moderation strategy. But if you start with a solid baseline, you will serve your audience well.
Moderation is a delicate art. It can take some real finesse to get it right. Every community is different and requires different techniques. But there are a few guiding principles that work for just about every product, from social networks to online games to forums.
Something to consider as you build your moderation strategy:
You have the power to shape the community.
Words have real consequences.
They may seem unconnected, but they’re profoundly linked. When creating a set of community guidelines and deciding how you will communicate and support them, you’re acknowledging that your community deserves the best experience possible, free of abuse, threats, and harassment. There is an old assumption that trolls and toxicity are inevitable by-products of the great social experiment that is the Internet, but that doesn’t have to be true. With the right techniques—and technology—you can build a healthy, thriving community.
First, it’s crucial that you set your community guidelines and display them in an area of your app or website that is readily available.
Know exactly where you stand on topics like profanity and sexting. It’s easy to take a stand on the really bad stuff like rape threats and hate speech. The trickier part is deciding where you draw the line with less dangerous subjects like swearing. Again, the age and demographic of your community will play into this. What is your community’s resilience level? Young audiences will likely need stricter policies, while mature audiences might be able to handle a more permissive atmosphere.
Ensure that your moderation team has an extensive policy guide to refer to. This will help avoid misunderstandings and errors when taking actions on user’s accounts. If your moderators don’t know your guidelines, how can you expect the community to follow them?
Then, decide how you are going to moderate content. Your best option is to leverage software that combines AI (Artificial Intelligence) with HI (Human Intelligence). Machine learning has taken AI to a new level in the last few years, so it just makes sense to take advantage of recent advances in technology. But you always need human moderators as well. The complex algorithms powering AI are excellent at some things, like identifying high-risk content (hate speech bullying, abuse, and threats). Humans are uniquely suited to more subtle tasks, like reviewing nuanced content and reaching out to users who have posted cries for help.
Many companies decide to build content moderation software in-house, but it can be expensive, complex, and time-consuming to design and maintain. Luckily, there are existing moderation tools on the market.
Full disclosure: My company Two Hat Security makes two AI-powered content moderation tools that were built to identify and remove high-risk content. Sift Ninja is ideal for startups and new products that are just establishing an audience.Community Sift is an enterprise-level solution for bigger products.
Once you’ve chosen a tool that meets your needs, you can build out the appropriate workflows for your moderators.
Start with these basic techniques:
Automatically filter content that doesn’t meet your guidelines. Why force your users to see content that you don’t allow? With AI-powered automation, you can filter the riskiest content in real time.
Automatically escalate dangerous content (excessive bullying, cries for help, and grooming) to queues for your moderators to review.
Automatically take action on users based on their behavior. Warn, mute, or ban users who don’t follow the guidelines. It’s not about punishment—Riot Games found that users who are given immediate feedback are far less likely to re-offend:
Give users a tool to report objectionable content. Moderators can then review the content and determine if further action is required.
Building community is the fun part of launching a new social product. What kind of community do you want? Once you know the answer, you can get started. Draft your community guidelines, know how you will reinforce them, and invest in a moderation system that uses a blend of artificial and human intelligence. And once the hard stuff is out of the way—have fun, and enjoy the ride. : )
Yesterday, we tested our theory that toxicity can put a dent in your profits. We used our two fictional games AI Warzone and Trials of Serathian as an A/B test, and ran their theoretical financials through our mathematical formula to see how they performed.
And what were the results? The AI Warzone community flourished. With a little help from a powerful moderation strategy, they curbed toxicity and kept the trolls at bay. The community was healthy, and users stuck around.
Trials of Serathian paid the cost of doing nothing. As toxicity spread, user churn went up, and the company had to spend more and more on advertising to attract new users just to meet their growth target.
Today, we move from the hypothetical to the real. Do traditional techniques like crowdsourcing and muting actually work? Are there more effective strategies? And what does it mean to engineer a healthy community?
Charles Kettering famously said that “A problem well stated is a problem half-solved”; so let’s start by defining a word that gets used a lot in the industry, but can mean very different things to different people: trolls.
The crux of the video is that trolling can be silly and ultimately harmless — like (most) pranks — or it can be malicious and abusive, especially when combined with anonymity.
When we talk about trolls, we refer to users who maliciously and persistently seek to ruin other users’ experiences.
Trolls are persistent. Their goal is to hurt the community. And unfortunately, traditional moderation techniques have inadvertently created a culture where trolls are empowered to become the loudest voices in the room.
Strategies That Aren’t Working
Many social networks and gaming companies— including Trials of Serathian —take a traditional approach to moderation. It follows a simple pattern: depend on your users to report everything, give users the power to mute, and let the trolls control the conversation.
Let’s take a look at each strategy to see where it falls short.
Crowdsourcing — depending on users to report toxic chat — is the most common moderation technique in the industry. As we’ll discover later, crowdsourcing is a valuable tool in your moderation arsenal. But it can’t be your only tool.
Let’s get real — chat happens in real time. So by relying on users to report abusive chat, aren’t you in effect allowing that abuse to continue? The damage is already done by the time the abusive player is finally banned, or the chat is removed. It’s already affected its intended victim.
Imagine if you approached software bugs the same way. You have QA testers for a reason — to find the big bugs. Would you release a game that was plagued with bugs? Would you expect your users to do the heavy lifting? Of course not.
Community is no different. There will always be bugs in our software, just as there will always be users who have a bad day, say something to get a rise out of a rival, or just plain forget the guidelines. Just like there will always be users who want to watch the world burn — the ones we call trolls. If you find and remove trolls without depending on the community to do it for you, you go a long way towards creating a healthier atmosphere.
You earn your audience’s trust — and by extension their loyalty — pretty quickly when you ship a solid, polished product. That’s as true of community as it is of gameplay.
If you’ve already decided that you won’t tolerate harassment, abuse, and hate speech in your community, why let it happen in the first place?
Muting Annoying Players
Muting is similar to crowdsourcing. Again, you’ve put all of the responsibility on your users to police abuse. In a healthy community, only about 1% of users are true trolls — players who are determined to upset the status quo and hurt the community. When left unmoderated, that number can rise to as much as 20%.
That means that the vast majority of users are impacted by the behavior of the few. So why would you ask good players to press mute every time they encounter toxic behavior? It’s a band-aid solution and doesn’t address the root of the problem.
It’s important that users have tools to report and mute other players. But they cannot be the only line of defense in the war on toxicity. It has to start with you.
Letting The Trolls Win
We’ve heard this argument a lot. “Why would I get rid of trolls? They’re our best users!” If trolls make up only 1% of your user base, why are you catering to a tiny minority?
Good users — the kind who spend money and spread the word among their friends — don’t put up with trolls. They leave, and they don’t come back.
Simon Fraser University’s Reddit study proved that a rise in toxicity always results in slower community growth. Remember our formula in yesterday’s post? The more users you lose, the more you need to acquire, and the smaller your profits.
Trust us — there is a better way.
Strategies That Work
Our fictional game AI Warzone took a new approach to community. They proactively moderated chat with the intention to shape a thriving, safe, and healthy community using cutting-edge techniques and the latest in artificial and human intelligence.
The following four strategies worked for AI Warzone — and luckily, they work in the real world too.
Knowing Community Resilience
One of the hardest things to achieve in games is balance. Developers spend tremendous amounts of time, money, and resources ensuring that no one dominant strategy defines gameplay. Both Trials of Serathian and AI Warzone spent a hefty chunk of development time preventing imbalance in their games.
The same concept can be applied to community dynamics. In products where tension and conflict are built into gameplay, doesn’t it make sense to ensure that your community isn’t constantly at each other’s throats? Some tension is good, but a community that is always at war can hardly sustain itself.
It all comes down to resilience — how much negativity can a community take before it collapses?
Without moderation, players in battle games like AI Warzone and Trials of Serathian are naturally inclined to acts — and words — of aggression. Unfortunately, that’s also true of social networks, comment sections, and forums.
The first step to building an effective moderation strategy is determining your community’s unique resilience level. Dividing content into quadrants can help:
High Risk, High Frequency
High Risk, Low Frequency
Low Risk, High Frequency
Low Risk, Low Frequency
Younger communities will always have a lower threshold for high-risk chat. That means stricter community guidelines with a low tolerance for swearing, bullying, and other potentially dangerous activity.
The older the community gets, the stronger its resilience. An adult audience might be fine with swearing, as long as it isn’t directed at other users.
Once you know what your community can handle, it’s time to look closely at your userbase.
Dividing Users Based on Behavior
It’s tempting to think of users as just a collection of usernames and avatars, devoid of personality or human quirks. But the truth is that your community is made up of individuals, all with different behavior patterns.
You can divide this complex community into four categories based on behavior.
Let’s take a closer look at each risk group:
Boundary testers: High risk, low frequency offenders. These players will log in and instantly see what they can get away with. They don’t start out as trolls — but they will upset your community balance if you let them get away with it.
Trolls: High risk, high frequency offenders. As we’ve discussed, these players represent a real threat to your community’s health. They exist only to harass good players and drive them away.
Average users/don’t worry: Low risk, low frequency offenders. These players usually follow community guidelines, but they have a bad day now and then. They might take their mood out on the rest of the community, mostly in a high-stress situation.
Spammers: Low risk, high frequency offenders. Annoying and tenacious, but they pose a minor threat to the community.
Once you’ve divided your users into four groups, you can start figuring out how best to deal with them.
Taking Action Based on Behavior
Each of the four user groups should be treated differently. Spammers aren’t trolls. And players who drop an f-bomb during a heated argument aren’t as dangerous as players who frequently harass new users.
Filter and Ban Trolls
Your best option is to deal with trolls swiftly and surely. Filter their abusive chat, and ban their accounts if they don’t stop. Set up escalation queues for potentially dangerous content like rape threats, excessive bullying, and threats, then let your moderation team review them and take action.
Warn Boundary Testers
A combination of artificial intelligence and human intelligence works great for these users. Set up computer automation to warn and/or mute them in real time. If you show them that you’re serious about community guidelines early on, they are unlikely to re-offend.
Crowdsource Average Users
Crowdsourcing is ideal for this group. Content here is low risk and low frequency, so if a few users see it, it’s unlikely that the community will be harmed. Well-trained moderators can review reported content and take action on users if necessary.
There are a couple of options here. You can mute spammers and let them know they’ve been muted. Or, for a bit of fun try a stealth ban. Let them post away, blissfully unaware that no one in the room can see what they’re saying.
Combining Artificial and Human Intelligence
The final winning strategy? Artificial intelligence (AI) and computer automation are smarter, more advanced, and more powerful than they’ve ever been. Combine that with well-trained and thoughtful human teams, and you have the opportunity to bring moderation and community health to the next level.
A great real world example of this is Twitch. In December 2016 they introduced a new tool called AutoMod.
It allows individual streamers to select a unique resilience level for their own channel. On a scale of 1–4, streamers set their tolerance level for hate speech, bullying, sexual language, and profanity. AutoMod reviews and labels each message for the above topics. Based on the streamer’s chosen tolerance level, AutoMod holds the message back for moderators to review, then approve or reject.
Reactions to AutoMod were resoundingly positive:
Positive user responses and great press? We hope the industry is watching.
The Cost of Doing Nothing
So, what have Trials of Serathian and AI Warzone taught us? First, we really, really need someone to make these games. Like seriously. We’ll wait…
We learned that toxicity increases user churn, that traditional moderation techniques don’t work, and that community resilience is essential. We learned that trolls can impact profits in surprising ways.
In the end, there are three costs of doing nothing:
Financial. Money matters.
Brand. Reputation matters.
Community. People matter.
Our fictional friends at AI Warzone found a way to keep the trolls away — and keep profits up. They carefully considered how to achieve community balance, and how to build resilience. They constructed a moderation strategy that divided users into four distinct groups and dealt with each group differently. They consistently reinforced community guidelines in real-time. And in the process, they proved to their community that a troll-free environment doesn’t diminish tension or competition. Quite the opposite — it keeps it alive and thriving.
Any community can use the four moderation strategies outlined here, whether it’s an online game, social sharing app, or comments section, and regardless of demographic. And as we’ve seen with Twitch’s AutoMod, communities are welcoming these strategies with open arms and open minds.
One final thought:
Think of toxicity as a computer virus. We know that online games and social networks attract trolls. And we know that if we go online without virus protection, we’re going to get a virus. It’s the nature of social products, and the reality of the internet. Would you deliberately put a virus on your computer, knowing what’s out there? Of course not. You would do everything in your power to protect your computer from infection.
By the same token, shouldn’t you do everything in your power to protect your community from infection?
Today we test our theory: when social products do nothing about toxicity, they lose money. Using AI Warzone and Trials of Serathian (two totally-made-up-but-awesome online games) as examples, we’ll run their theoretical financials through our mathematical formula to see how they perform.
Remember — despite being slightly different games, AI Warzone and Trials of Serathian have similar communities. They’re both competitive MMOs, are targeted to a 13+ audience, and are predominantly male.
But they differ in one key way. Our post-apocalyptic robot battle game AI Warzone proactively moderates the community, and our epic Medieval fantasy Trials of Serathian does nothing.
Let’s take a look at the math.
The Math of Toxicity
In 2014, Jeffrey Lin from Riot Games presented a stat at GDC that turned the gaming world on its head. According to their research, users who experience toxicity are 320% more likely to quit. That’s huge. To put that number in further perspective, consider this statistic from a 2015 study:
52% of MMORPG players reported that they had been cyber-victimized, and 35% said they had committed cyberbullying themselves.
A majority of players have experienced toxicity. And a surprising amount of them admit to engaging in toxic behavior.
We’ll take those numbers as our starting point. Now, let’s add a few key facts — based on real data — about our two fictional games to fill in the blanks:
Each community has 1 million users
Each community generates $13.51 in revenue from each user
The base monthly churn rate for an MMO is 5%, regardless of moderation
Even with a proactive moderation strategy in place, we expect AI Warzone users to experience about 10% toxicity. It’s a complex battle game where tension is built into the game mechanic, so there will be conflict. Users in Trials of Serathian — our community that does nothing to mitigate that tension— experience a much higher rate of toxicity, at 30%.
Using a weighted average, we’ll raise AI Warzone’s churn rate from 5% to 6.6%. And we’ll raise Trials of Serathian to 9.8%.
Taking all of these numbers into account, we can calculate the cost of doing nothing using a fairly simple formula, where U is total users, and U¹ is next month’s total users:
U¹ = U — (U * Loss Rate) + Acquired through Advertising
Using our formula to calculate user churn and acquisition costs, let’s watch what happens in their first quarter.
Increased User Churn = Increased Acquisition Costs
In their first quarter, AI Warzone loses 218,460 users. And to meet their 10% growth rate target, they spend $1,527,498 to acquire more.
Trials of Serathian, however, loses 324,380 users (remember, their toxicity rate is much higher). And they have to spend $1,821,956 to acquire more users to meet the same growth target.
Let’s imagine that AI Warzone spends an additional $60,000 in that first quarter on moderation costs. Even with the added costs, they’ve still saved $234,457 in profits.
That’s a lot. Not enough to break a company, but enough to make executives nervous.
Let’s check back in at the end of the year.
The Seven Million Dollar Difference
We gathered a few key stats from our two communities.
When Trials of Serathian does nothing, their EOY results are:
Churn rate: 9.8%
User Attrition: -8,672,738
Total Profits (after acquisition costs): $39,784,858
And when AI Warzone proactively moderates, their EOY results are:
Churn rate: 6.6%
User Attrition: -5,840,824
Total Profits (after acquisition costs): $47,177,580
AI Warzone deals with toxicity in real time and loses fewer users in the process — by nearly 3 million. They can devote more of their advertising budget to acquiring new users, and their userbase grows exponentially. The end result? They collect $7,392,722 more in profits than Trials of Serathian, who does nothing.
And what does AI Warzone do with $7 million more in revenue? Well, they develop and ship new features, fix bugs, and even start working on their next game. AI Warzone: Aftermath, anyone?
These communities don’t actually exist, of course. And there are a multitude of factors that can effect userbase growth and churn rate. But it’s telling, nonetheless.
And there are real-world examples, too.
Sticks and Stones
Remember the human cost that we talked about earlier? Money matters — but so do people.
Twitter is a vital platform for sharing new ideas and forging connections around the globe. Crucially, it’s a place where activists and grassroots organizers can assemble and connect with like-minded citizens to incite real political change. The Arab Spring in 2011 and the Women’s March in January of this year are only two examples out of thousands.
But it’s become known for the kind of abuse that Lily Allen experienced recently — and for failing to deal with it adequately. Twitter is starting to do something — over the last two years, they’ve released new features that make it easier to report and block abusive accounts. And earlier this week even more new features were introduced. The question is, how long can a community go without doing something before the consequences catch up to them?
Twitter’s user base is dwindling, and their stock is plummeting, in large part due to their inability to address toxicity. Can they turn it around? We hope so. And we have some ideas about how they can do it (stay tuned for tomorrow’s post).
What Reddit Teaches us About Toxicity and Churn
Reddit is another real-world example of the cost of doing nothing.
In collaboration with Simon Fraser University, we provided the technology to conduct an independent study of 180 subreddits, using a public Reddit data set. In their academic paper “The Impact of Toxic Language on the Health of Reddit Communities,” SFU analyzes the link between toxicity and community growth.
They found a correlation between an increase in toxic posts and a decrease in community growth. Here is just one example:
It’s a comprehensive study and well worth your time. You can download the whitepaper here.
Using our formula, we can predict how a proactive moderation strategy can impact your bottom line. And using our two fictional games as a model, we can see how a real-world community might be affected by toxicity.
AI Warzone chose to engineer a healthy community — and Trials of Serathian chose to do nothing.
But what does it mean to “engineer a healthy community”? And what strategies can you leverage in the real world to shape a troll-free community?
In tomorrow’s post, we examine the moderation techniques that AI Warzone used to succeed.
What happens when two games with similar communities take two very different approaches to chat?
It’s dark. The faint green glow of a computer screen lights your field of vision. You swipe left, right, up, down, tracing the outline of a floating brain, refining a neural network, making connections. Now, an LED counter flashes red to your right, counting down from ten. You hear clanking machinery and grinding cogs in the distance. To your left, a new screen appears: a scrap yard, miles of twisted, rusty metal. The metal begins to move, slowly. It shakes itself like a wet dog. The counter is closer to zero. Urgent voices, behind, below, above you:
“DON’T MESS IT UP!”
“LET’S DO THIS!”
“YOU GOT THIS!”
Welcome to AI Warzone, a highly immersive, choice-driven game in which players create machines that slowly gain self-awareness, based on user’s key moral decisions. Set in 3030, machines battle each other in the industrial ruins of Earth. You create and join factions with other users that can help or hinder their progress, leading to — as we see above — a tense atmosphere rife with competition. A complex game with a steep learning curve, AI Warzone is not for the faint of heart.
Now, imagine this:
You stand atop a great rocky crag, looking down on a small village consisting of a few thatch-roofed cottages. A motley crew stands behind you; several slope-browed goblins, the towering figure of a hooded female Mage, and two small dragons outfitted with rough-hewn leather saddles.
You hold a gleaming silver sword in your hand. A group of black-robed men and women, accompanied by trolls and Mages, approach the village, some on dragon-back, others atop snarling wolves. Some of them shout, their voices ringing across the bleak landscape. Almost time, you whisper, lifting your broadsword in the air and swinging it, so it shines in the pale sun. Almost time…
“FUCK YOU FAGGOT,” you hear from far below.
“kill yurself,” a goblin behind you says.
“Show us yr tits!” yells one of the black-robed warriors in the village.
“Oh fuck this,” says the hooded female Mage. She disappears abruptly.
This is life in Trials of Serathian, an MMO set in the Medieval world of Haean. Users can play on the Dawn or Dusk side. On the Dawn side, they can choose to be descendants of the famed warrior Serathian, Sun Mages, or goblins; on the Dusk side, they can play as descendants of the infamous warrior Lord Warelind, Moon Mages, or trolls. Dawn and Dusk clans battle for the ultimate goal — control of Haean.
Two Communities, Two Approaches to Chat
Spoiler alert: AI Warzone and Trials of Serathian aren’t real games. We cobbled together elements from existing games to create two typical gaming communities.
Like most products with social components, both AI Warzone and Trials of Serathian struggle with trolls. And not the mythical, Tolkien-esque kind — the humans-behaving-badly-online kind.
In both games, players create intense bonds with their clan or faction, since they are dependent on fellow players to complete challenges. When players make mistakes, both games have seen incidents of ongoing harassment in retaliation. Challenges are complex, and new users are subject to intense harassment if they don’t catch on immediately.
Second spoiler alert: Only one of these games avoids excessive user churn. Only one of these games has to spend more and more out of their advertising budget to attract new users. And only one of these games nurtures a healthy, growing community that is willing to follow the creators — that’s you — to their next game. The difference? One of these games took steps to deal with toxicity, and the other did nothing.