The Cause of Viral Internet Content
Nearly everybody is very much aware of the insanity that stems from how quickly certain information can travel through the complicated mazes that make up the Internet. It is undeniable that since the popularization of the Internet, the speed of the spreading of news, videos, and general social media content has increased immeasurably. While these things are well-known and generally agreed upon by most people, the reason why only certain things spread to vast amounts of people while others are only viewed and shared by a small group is not quite as well known.
In Jonah Berger’s article, “The DNA of Viral Content,” he discusses the science behind the sharing of information on the Internet. Berger gives a sneak peak to six important components to a piece of media that will go viral, which he goes into detail about in his book Contagious: Why Things Catch On. It turns out that, after thorough analysis, six components have been proven to consistently be the reasons behind the viral spreading of information on the Internet. The two examples Berger provides in the article are social currency and triggers. He points out that sharing a piece of information that makes a person look good or reflects positively back on them increases that person’s likelihood to share the information. Berger calls the measure of this positive reflection social currency. Triggers, on the other hand, are things that are already in the media that cause people to think about and share more about something else and are also key to how many people share a certain link, video, etc. The other four components are emotion, public, practical value, and stories.
Relating this to class, these different components would be easy to create a numerical scale for that measures how much of each component a certain piece of information has. If each component was then added together in some way, the total likelihood to go viral could be calculated in a way. This in turn could be used to predict how big of a network could be created representing people who have seen a certain video, link, etc. Say we only look at Facebook as a way to spread this piece of information. If one person sees it and we come up with a function to measure how often the person shares things based on activity and combine this with the total likelihood for this piece of information to go viral, we can come up with a grand score. If this score is above a certain number, we could then draw an edge between this person and all of their Facebook friends (different nodes on a network that we are creating). This would continue throughout the network of Facebook as more and more people share the piece of information based on the grand score. Taking advantage of the scientific basis of the different components of what makes something go viral, we could potentially predict what a network would look like where all nodes are people who have been exposed to this piece of information and the edges are connecting the sharer and the receiver of the shared piece of information. This created network could then help us predict exactly how viral a piece of information would be and we would have succeeded at using a network to predict human behavior.
http://www.theguardian.com/media/2014/sep/15/the-dna-of-viral-content