The phenomenon of memes
Why you’ll share this story: The new science of memes
How did the Bee Movie come to be an Internet sensation? What about the inundation of Harambe, Arthur, Doge or Forever Alone memes? How did such memes become successfully viral over so many others? Further, how is the dissemination of memes translated and accelerated through social networks? The fate of a meme seems to be a roll of the dice, but it is quite the contrary; it turns out that there is a science behind the phenomenon of memes. The short answer to all of the above questions lies within the fascinating study of networks.
Memes and the science behind their strangely addictive visual content are surprisingly complex. The article asserts that, “most previous research on how things go viral has sought to map the social interconnections of those who are sharing content”. Thus, analyzing networks through which memes traverse can help us predict and assess various qualities regarding a meme such as its virality and its potential reach. Memes exhibit the behavior of “social contagion” as information spreads via a series of cascading effects throughout a network. Unlike biological contagion in which pathogens reach individuals through physical or airborne contact, the social contagion behind memes is voluntary and based on peer-to-peer sharing. In fact, it is amusing that an evolutionary biologist named Richard Dawkins is responsible for coining the term “meme”, comparing memes to genes. Just as genes carry genetic information through generations, memes also continuously reproduce, mutate and spread ideas across groups of people. With the invention of the Internet, this information can reach millions of people in a matter of seconds. Since all this data rests inside the Internet, scientists have been able to extract data from the study of memes; consequently they can model the behavior of their life cycles and audiences, producing data that is both impressive and potentially powerful. For example, from this data scientists were able to conclude that “memes that were ‘more competitive’ than others – that is, whose rise in popularity tended to correlate with the fall in popularity of other memes – were more likely to succeed overall” and that “clusters of memes tend to do well together”. There is some game theory involved in this; creators must be strategic in how and where they choose to spread their memes.
How can we apply the data science behind memes to other aspects of society? Perhaps this information may motivate the ways in which influencers such as celebrities, Internet icons or political leaders, spread their ideas in order to reach as many people as possible. Our understanding will only be further exemplified in future years by the mass amount of data produced from social media platforms.
Above graph shows how a specific Obamacare meme spread and mutated over time. Image: Adamar et al