Casual video games are played by millions, and the Electronic Software Association (ESA) offers research showing that casual and social games are among the most frequently played games today (by 47% of the population playing games). Social games are those that are played within social networking sites, such as Facebook, or as applications on mobile devices that connect to the user’s social network. Many social games can also be considered casual games, those that are easy to play as well as simple to pick up, play for a while in short bursts, then put down.
Casual social games like Fruit Ninja, Candy Crush Soda Saga, and Plants vs. Zombies are popular even amongst those who might not normally label themselves “gamers.” However, players of these games frequently fail to consider the convergence of networked game play and game algorithms that mine both our own data as well as the data of individuals in our network. Already we see our gameplay options being shaped by this surveillance of casual game play, and these algorithms bring up compelling issues regarding user privacy and data mining that digital humanities scholars are well poised to address.
Online social networks thrive on the constellations of strong and weak ties that make up our personal networks of friends, family members, casual acquaintances, work buddies, friends of friends, and so on. As Jesper Juul (2010) has explored in his book A Casual Revolution: Reinventing Video Games and Their Players, because of their connections to these massive webs of strong and weak ties, socially networked games are embedded within highly complex networks. And as I have explored elsewhere, casual game players who play these socially networked games leave digital traces of their actions in their wake, “their game play activities … visible to corporate entities … as well as the player’s own social networks.”
Surveillance, of course, is not unique to casual social games. From security cameras at schools and stores to facial recognition software, keystroke loggers, and license-plate tracking programs, the United States has long used technological tools to gather information about its citizens. However, the intersection of social networks and casual gameplay—a space where players generate massive amounts of data by the minute—is particularly intriguing for digital humanities scholars. For example, Runge, Gao, Garcin, and Faltings(2014) analyze when casual game players of Diamond Dash and Monster World quit playing the game; this moment in time, often called “customer churn” or attrition rate, can be predicted via gaming algorithms. Furthermore, in games where customer churn can be predicated via algorithmic analysis, players can then be retained with targeted email and social media campaigns or notifications as well as the implementation of free in-game currency or similar benefits. In other words, if the game algorithms predict that you as a player are likely to leave soon (and these predictions may be based on elements like time and date of last login, accuracy rates of your gameplay, the country you’re from, the number of other players in your network who are active, etc.) then the game may “reward” you with free elements like in-game currency, extra lives, or a new level to hook you in and keep you actively playing. Meanwhile, the player is frequently unaware that these games have been logging all the facets of their game play activities and that the algorithms are busy breaking down patterns and making predictions about the “stickiness” of the game play.
Greater awareness of this constant surveillance in casual social games is the first step in influencing humanities research into such algorithms. We should be paying greater attentionto casual games, an area of study too easily dismissed as frivolous and unacademic; however, the seeming simplicity of these games belies the importance of the algorithmic analysis occurring by the second underneath the surface.