Over the last few months I have been working on my thesis for my master’s program at Columbia. I figured it was about time to share just a little bit of my research. Sure. its not your everyday blog post, but hey- it’s still got value. Read on nerdites, read on!
In genetics a meme, comparable as an mental representation of a physical gene, is defined as “an idea, belief, pattern of behavior” which is “hosted” in the mind of one or more parties. A meme can replicate itself as necessary to travel from mind to mind; therefore what would otherwise be interpreted as an individual manipulating or influencing another is seen, according to memetics, is a meme reproducing itself (Lesovec et al, 2009). As with genetics, a meme’s virility, that is, its ability to be easily spread from one individual to another, maybe be a result of its benefiting the host. (Brodie, 1996). Social media provides an interesting application of memetics. Historically, memetics ignores the truth of ideas and focuses on the spread of mental thought. As we have discussed earlier, this is often the case with information dissemination on the social web: ideas (or news) comes first, the truth second.
While the meme concept does seem to explain virility of thought, it fails to explain why certain thoughts are stickier than others. Additionally, memetics fails to leverage the strength of the host as a possible indicator in the ability for a meme to travel more successfully. Theodore Vail’s research on telephones in 1908 led to a nebulas definition of what is now called the “network effect” (Ussi, 1996), that is to say the effect that one user of a good or service has on the value of that product to other people. Although network effect focuses mainly on product and technology adoption, it is easily adapted to define thought adoption as well. Vail’s research serves as a nice companion to memetics as it introduces the variable of network strength, in the potential mass adoption of a thought. This combination of memetic and network theory might rightly be called the “social network effect,” or the effect one person’s publicly-expressed thought has on the value or adoption of that thought by another person. Given the nature of the channel, the social network effect drives both virility of thought (memetics) and leverages community for thought dispersion (network effect).
In today’s “social” world, several core SN sites make up the majority of the average American’s online community networks, namely: Facebook, Twitter, MySpace, and LinkedIn (dBizMBA, 2010). In addition to foundation there are myriad other sites that provide niche communities with a way to interact. Sometimes these interactions center on a common passion: Dogster connects dog lovers from around the world, while Flixter provides a forum for moviegoers to share reviews. Others social community sites serve a specific utility: PlanCast allows users to share plans and activities, while FourSquare provides a GPS check-in platform where users are rewarded for updating their location in real time. Regardless of site focus or content, once an individual joins a social network they are first prompted to identify themselves. Once an online identity has been created, the second step of any SN is to connect that identity with others within the network. These relationships may be labeled in a variety of different ways, the most popular being “friends” (Facebook) and “followers” (Twitter). Additionally, connections can be one way- in which a user can elect to view another’s updates and posts without necessarily sharing their own content. To protect privacy, connections can also be bi-directional, where both parties must accept the “relationship” before content can be shared between the two. These digital associations knit together to create a web of content dispersion popularly called the “social graph” (Zuckerberg, 2007).
Through these SNS connections, users are often exposed to content to which they would otherwise not be privy. User A may share a story, which User B reads and re-posts, sharing it with all of User B’s friends and followers, including User C. Now User C is exposed to User A’s content, with whom he has no relationship. If User C likes User A’s content enough, he or she may elect to created a connection with User A, becoming User A’s friend or follower. Thus a bond is created between two complete strangers solely based on the quality of shared content. In this environment, content dispersion can provide a means through which strangers can begin to build bonds with each other due to ideological alignment exposed by their mutual social interactions (Haythornthwaite, 2005; Boyd, 2007). Thus, one’s circle or influence online far exceeds, in network size, that of previous generations. Connections between users within the social graph can be unpredictably varied, and information quickly and easily travels across a myriad of social properties causing proliferation of thought (Boyd, 2007). Leveraging the increased number of connections, regardless of their strength, means information travels at a much faster rate.
27.3 million tweets (Watters, 2010; Hird, 2010) and 500 million pieces of content on Facebook (Facebook, 2010) are shared daily. Outside of these active updates, a great number of users participate in this content dispersion passively: rather than sharing content they absorb what is shared by others. Currently SNs and blogs consume nearly 25% of people’s time online (one in every four-and-half minutes online). The average visitor spends 66% more time on these sites than they did a year ago—6 hours in April 2010 versus 3 hours and 31 minutes last year (Nielsen, 2010). As a result, information travels at surprisingly quick speed through the social graph, with the most shocking of news taking only minutes to disseminate (Vieweg et al, 2010; Starbird et al 2010).
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