Anything much beyond Dunbar’s number is too complicated to handle at optimal processing levels.
Now imagine the child who has a Facebook page and an Instagram account, who participates on Snapchat, WhatsApp, and Twitter. Throw into that mix all the mobile phone, email, and text contacts. A child who is active online, and interested in social media, could potentially have thousands of contacts.
We are not talking about an intimate group of friends. We are talking about an army. And who’s in this army? These aren’t friends in any real-world sense.
I have been working on a mathematical formula to predict the prevalence of antisocial behavior online—in hopes of designing an algorithm to identify incidences of bullying. How?
Locard’s exchange principle is the basic premise of forensic science. It dictates that every contact leaves a trace, and nowhere is this more true than online. Unlike the playground, where the mean words of a bully disappear instantly into the ether—unless there is an eyewitness—online it is just the opposite. Cyberbullying is nothing but evidence: a permanent digital record. So how did we get to the point where it became more problematic than real-world bullying? My answer is taken from The Usual Suspects, one of my favorite movies, in which Kevin Spacey delivers the immortal line “The greatest trick the devil ever pulled was convincing the world he didn’t exist.”
To me, the greatest trick social-media and telecom companies ever pulled is trying to convince us that they can do nothing about cyberbullying.
In terms of digital forensics, it is a cybercrime with big fingerprints. Using an approach that I am calling the math of cyberbullying, both victims and perpetrators can be identified.
Many of the big-data “social analytics” outfits like Brandwatch, SocialBro, or Nielsen Social use algorithms to identify or estimate much more complicated things, like a Twitter user’s age, sex, political leanings, and education level. How hard would it be to create an algorithm to identify antisocial behavior, bullying, or harassment online? My equation goes like this: d x c (i x f) = cyberbullying.
The math would be this simple:
I am bullying you = direction (d)
bitch, hate, die = content (c)
interval (i) and frequency (f) = escalation
I am actively working with a tech company in Palo Alto to apply the Aiken algorithm to online communication. To develop the c (content) database, I plan on launching a nationwide call for content. Every person who has ever received a hateful bullying message can forward it to our repository. In that way, victims of cyberbullying can become an empowering part of the solution to an ugly but eminently solvable big-data problem. We just need the collective will to address it.
The algorithm can be set to automatically detect escalation in a cyberbullying sequence, and a digital outreach can be sent to the victim: “You need to ask for help. You are being bullied.” And simultaneously an alert can be sent to parents or guardians telling them something is wrong and encouraging them to talk to their child.