The first day it was publicly announced it was linked on Reddit, and it ended up getting 1,600 visitors who collectively viewed  twenty-eight thousand pages.   By the end of the night, though, the system that had been used to manage it  had been overcome by a malicious script and the flow of the experience had been destroyed.  Over the next weekend my friend Aidan and I rebuilt a tighter  system using a database. It meant that we could limit the speed that people posted at, but that meant we could also tell who posted them. From there we decided that we could also let people vote on which posts they liked.

While the site lost the initial momentum from the exploit, the improved system captured people’s attention. In the four months it has been online, it has had 3,600 visits, but shown 55,000 pages and the average visitor stays on the site for five minutes. By comparison the average visit on a website is seven seconds – it’s hard to overstate how distracted the average internet user is.

The data that I needed to collect to make the system work also makes for intriguing analysis. With individual ratings you can see which posts are the most popular and which are the most contentious. You can compare the score distribution of all the posts with the subset containing a specific keywords. At present the corpus of data is relatively small so it’s hard to determine clear trends. I’m hoping to expand the system to other places to see how they vary. It’s going to be interesting!

A lot has happened to the social game space in the last year or so. Shortly after EA’s purchase of Playfish in 2009, a lot more developers began working on Facebook titles, and a number of new shops opened up to specifically target the platform. From the player’s perspective, there have been developments too – people are starting to scoff a little less at the idea of playing a game on Facebook as a legitimate activity, players have become increasingly comfortable with parting with money for the experience, and the more dubious methods for extracting revenue have largely fallen by the wayside.

What about the games?

These developments have more to do with the business imperative for games on Facebook, though, and less to do with what’s happening in the games themselves. On that front, things aren’t moving quite as fast. The bulk of the new games are generally functional copies of older ones, and even the more novel designs still feature a lot of plain token-passing and grind for the sake of grind. In particular, very few games are making use of the fact that they’re connected to an enormously valuable data source: The social network.

Making more out of the network

Social networks typically have “a high degree of partial overlap”. A person has an average of about 150 friends, and might share 75 or so with their closest friend, 60 with the next-closest, and so on. By looking at these measures of overlap, as well as indicators like wall-to-wall messaging, we can build a picture of who these friends are and what a user’s relationship to them is. A game could exploit this by encouraging cooperation (or rivalry) with your closest friends, or encourage you to get back in contact with people you haven’t spoken to recently. Finding clumps of densely-connected people probably means you have a ‘clique’ that might enjoy operating as a group in the game, and so group quests can be offered to them to encourage that participation.

Good for business

A better understanding of the of social connections – and quality of those connections – is going to be better for business, too. If a person has a higher friend count than the average friend count of their friends, they are generally a more influential person. If a ‘hub’ user has good things to say about your game, that praise will go out to more people. In fact, if you want to get really sneaky, you can bias the game in favour of these users, giving them more free stuff and making things slightly easier for them. It’s effectively making the game ‘cheaper’ for them in recognition of the fact that they’re likely to increase the Average Revenue Per-User (ARPU) through encouraging others to play. From a marketing standpoint, they are essentially micro-advertisers rather than sources of revenue. This tactic is hardly new – the promotion strategy for the first commercially-available toothpicks consisted of giving free packs to influential students at Harvard University, with a special request that the toothpicks be used as conspicuously as possible. The field of Viral Marketing calls these people Alpha users, though discussion seems to focus mainly on finding these users in the first place, rather than the tailoring of an adaptive system to bias alpha users in favour of it.

Other types of users

Another user of particular interest to social gaming is one who spends money on playing the game. These users are referred to as “Whales”, a term borrowed from the casino industry. In casinos, these people can often spend millions of dollars in a single visit. In social games, however, the figures are much more modest – a “Whale” is generally someone who spends around $20 per month. Often, though, it seems that players with money would be happy to spend a lot more. It would be possible to reveal progressively more extravagant purchases for a “Whale” to pick up, where the emphasis could shift from gameplay to collection and memorabilia. Examples could include a figurine of their in-game character, large-format schematics of a player’s farm/city/restaurant/etc, books that chronicle their progression through the game or simply an alternate electronic view of their game-worlds. As the top end of the available purchases climbs higher, the number of purchasers required to justify investment in them should hopefully decrease, since one purchase at a hundred dollars yields as much return as twenty purchases at five dollars.

While the discussion of these strategies here might seem cold, the role of the game designer is to pick methods for engaging the player, or game mechanics, that are appropriate both in the context of the underlying theme and in combination with one another. If used in the right context, a socially adaptive model can lead to a more personalized experience for individual players. It can expose certain mechanics to people who are likely to appreciate them, and keep those mechanics out of way the for people who won’t. Everybody wins!

[UPDATE] It turns out that Zynga does allow users to spend a lot more than the $20/month figure, but it seems like people only have a good reason to do so in their Poker game. Since poker requires players to engage in oneupmanship in the form of ever-increasing bets, it’s not really making use of an adaptive model for determining the best way to make use of a user. Still, it’s very interesting to know that someone is offering players the opportunity to spend as much as they want – and that some players are doing just that!

The first two accusations are reasonable – there are a lot of stupid comments out there, and they are very repetitive.  They are so hated by some that someone has developed a Firefox Add-on that will strip comments out of the page before you can even be offended by them.  On the other hand, though, people are very stupid and repetitive.  There’s often a surprising honesty to the statements that people make on youtube. It is suggested that the monumental scale of the youtube ‘community’ means that individuals are effectively anonymous, which liberates users from any self-censorship that would occur from fear of shame or other punishment.  It’s not all honesty, since people are more likely to be offensive if they know they can get away with it, but it’s a refreshingly different place to look for views and opinions in.

So while they may be stupid, they’re still worth looking at.  The issue of quantity is more of a practical issue, though.  For example, I keep tabs on  a video called “The Most Pathetic Baby Panda Ever”. It has almost 5 million views and 15,500 comments.   Even if you only dedicated 5 seconds to each comment, you have to spend over 21 hours studying them. The Evolution Of Dance has almost 250,000 comments -   almost two weeks’ worth of study.  While they’re interesting, they’re not that interesting.

Conveniently, though, this is exactly the kind of thing that the study of data mining is supposed to deal with.  In addition, the fact that the comments appear to be stupid (or simple,) and highly repetitive works in our favour. The first question I would try to ask of these comments is “what’s a typical comment?”  and toward that end, I have created the Youtuber.

Most of these factoids are interesting only to myself, sometimes not even that. Being able to dig into my financial transactions gives me the opportunity to do a number of things, though – I can construct an “average” day. If I place my expenditures on a city map, I can draw my route over a day (or a week, or a month), see when I buy my morning coffee, and the average radius of my lunchtime wanderings. I can even use it to retrace my steps, and find the name of that great Japanese restaurant I went to in Auckland last year.

You might still think that these are unimportant, but this ignores two issues. First, we don’t know what’s insignificant until we can see it. There may be important trends in my buying habits – if I buy a coffee in the morning, I may end up working late because I forget to go home. Second, it’s important to somebody – it might not be front-page news if I go to a restaurant, but it’s nice to have the name on hand so I can tell a friend whether to give it a miss or not.

Most of our interaction with computers (as digital cameras , cellphones or point-of-sale devices) is being recorded. The sum total of this recorded data is referred to as our digital footprint. While many people find the presence – or even the idea - of this record threatening, I think it gives us an opportunity to answer important questions about who we are, and what we do.

There are at least 80 million videos on Youtube [wikipedia], and if the average length is at least one minute, then it would take about 160 years to watch them all (with no waiting for loading).

There are at least 1.5 million CCTV cameras deployed in public places in the Greater London area [also wikipedia], meaning that if a person could watch 16 cameras at once to monitor them, you’d still need almost 100,000 people, or around one out of seventy living in London employed to watch over the other 69. (If we broke it into reasonable chunks, you’d want 4 full-time shifts of people, pulling the number up to 400k and the ratio down to about 1:18!

Project Gutenberg, wonderfully, has over 100,000 books on offer for download. If we assume that the average book is 100,000 words (a novel is generally supposed to be about 230,000 or so; the English translation of Les Miserables is 650,000) then it would take 90 years (of constant reading at a pace of 240wpm) to get through it all.

There are probably hundreds (or thousands, or millions) more examples of where the quantity of information we have at our disposal is so far beyond our perceivable reach that it becomes effectively unusable. This is why we need to find a better way to work with large quantities of information – a way of “Data Mining”, if you will pardon the phrase.

It’s true, datamining is becoming big business, and all of the big IT companies are doing it – but when was the last time that you did it? Unless you work at one of those big IT companies, probably never.

That’s something I want to change.  One of the programs I’ve been making recently, the Youtuber, lets individuals do some basic analysis on one of the most frivolous data sources out there – Youtube comments.  It is my hope if we make better tools for data analysis for the everyday user, they will learn something new and interesting about the world.

Not to mention what we might find when we turn this analysis back on ourselves! But that’s another story.