What makes content go viral on the Internet? Theories abound, and the answers given often relate to the inherent quality or catchiness of the “meme,” or information unit. But research suggests that factors intrinsic to social networks may make the process more random — and less explicable — than is often assumed.

A 2012 study from Indiana University and Northeastern University published in Scientific Reports, “Competition Among Memes in a World with Limited Attention,” investigates the “mechanisms of competition” among memes and “how they shape the spread of information.” The researchers constructed a statistical model that simulates Twitter and compared it with actual Twitter patterns from October 2010 to January 2011 — analyzing some 120 million retweets, 12.5 million users and 1.3 million hashtags — to see how closely the model represented real-world behavior. In the statistical model, the memes did not have any qualitative content; they were merely passed along within certain parameters, such as users’ capacity to pay attention to information.

The study’s findings include:

• The model simulated viral patterns similar to those that actually occurred in Twitter, suggesting that viral memes can happen without any of the usual explanations — influential user involvement; quality, appeal or cleverness; or outside world or media events driving attention to certain concepts.
• User behavior can be modeled independent of actual meme content just by looking at the structure of the network and the limits of human attention. The key mechanism appears to be that, because users have limited attention, some “memes survive at the expense of others.”
• Social networks have built-in dynamics that propel memes as they compete for attention, and this “can account for the often-reported long-tailed distributions of topic popularity and lifetime.”

The authors do not assert that “intrinsic meme appeal” has no importance in driving viral trends, but the fact that similar viral effects can occur without external impetus has important implications: “This appears as an arresting conclusion that makes information epidemics quite different from the basic modeling and conceptual framework of biological epidemics. While the intrinsic features of viruses and their adaptation to hosts are extremely relevant in determining the winning strains, in the information world the limited time and attention of human behavior are sufficient to generate a complex information landscape and define a wide range of different meme spreading patterns.”

Tags: Twitter, social media