O. Author Credentials

• Linked: a network scientist who is also the Robert Gray Dodge Professor of Network Science at Northeastern University

• Viral Marketing and Social Networks: Doctor of Philosophy in Business Administration, Florida Atlantic University

• You Are Here: Assistant Professor in the Department of Communication and Rhetorical Studies at Syracuse University

• “The Accidental Topology of Digital Culture” and Virality: Reader in Digital Cultures and Communication, University of East London.

• “Drama Goes Viral”: Author Keith A. Quesenberry is a marketing professor and researcher at Messiah College, while Michael K. Coolsen is a professor of marketing at Shippensburg University.

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I. As the development of online platforms enables more and more people to unleash and promote their creativity, knowing the backend story of viral ads should be one vital part of people’s Internet literacy.

 

A. Backend stories help people know how to promote

Viral marketing creates more loyal customer base than traditional marketing (Petrescu).

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B. Backend stories help people filter out misleading information:

• Conceptual lens

• the design of the internet has its root in “liberalism,” and more specifically, “neoliberalism” (Phillips and Milner, ch. 2). In other words, the freedom of information serves as the foundation for recent Internet development

• Liberalism: 

  • “Support for or advocacy of individual rights, civil liberties, and reform tending towards individual freedom, democracy, or social equality” (Oxford English Dictionary). 

  • Express and emphasize “individual freedoms like free speech, a free press, property rights, and civil liberties” (Phillips and Milner, ch. 2)

• Neoliberalism

  • Has an emphasis on free-market capitalism (alongside the liberalism definition) (You Are Here).

• Pure focus on freedom cause “negative freedom” in return:

  • The lack of censorship enables false information to flow on the web (disinformation, misinformation, and malformation) (Phillips and Milner, ch. 2). 

  • The ultimate freedom of speech led to the absence of democracy, fairness, and equity on the web (Barabasi, ch. 5). For example, web pages that are referenced by more external pages are more likely to be linked again (ch. 7). 

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II. Virility occurs when the “Möbius strip of virality” gets activated for an online video, meaning that it needs to be seen first. Its qualities induce sharing, enabling others to see it.  

 

A. Defining “virality”: 

Some information or the awareness of something “propagates” in a virus-like way, spreading quickly and achieving wider reach (Oxford English Dictionary).

 

B. Networks: knowing how everything around us connects; multiple models (world wide web is a typical exam of a network)

• historical lens

• Erdös-Rényi model: random network model (Barabasi, ch. 2)

  • Everything is connected 

  • A network is made up of linkages between nodes, and any nodes will be connected through multiple linkages to any other nodes on the same network

  • The hypothesis of nineteen degrees of separation states that every web page is, on average, only nineteen clicks away (Barabasi, ch. 3). 

    • This hypothesis suggests that the web is a small world — imagine being able to follow the links from a scientific database to a funny video on YouTube within nineteen clicks. 

    • But this hypothesis is problematic since a database is very less likely to refer to funny videos (it needs some trials and errors to deduce that exact nineteen clicks). —> instead, follow the clues to find more relevant links.

• Watts & Strogatz: cluster model —> network is not random (Barabasi, ch. 5)

  • There are a few densely connected nodes in the network. (Think about social media. It would be helpful in Arc 3)

• The “80 - 20” law of networks (power law) (Barabasi, ch. 6)

  • Based on the cluster model, it states that most nodes in a network only have a few links, while a few nodes have a lot more links. These highly linked nodes are called “hubs.”

  • The article “The Accidental Topology of Digital Culture: How the Network Becomes Viral” also presents the idea that network growth is “skewed,” with hubs growing faster (Sampson). 

• Networks controlled by “growth” and “preferential attachment” (Barabasi, ch. 7)

  • “Growth”: the network is growing with more nodes constantly added to it (think about the Internet). 

  • “Preferential attachments”: nodes existing for longer are more likely to get new links since nodes with more links are likely to be connected again (I disagree. This cannot explain how new, viral content gets way more links).

  • A counterargument of “preferential attachment” is from Viral Marketing and Social Networks: “a person … decides to spread it to members of his or her social network” (Petrescu 1)

• Fitness of nodes (fitness/competitive environment model) (Barabasi, ch. 8)

  • Introduces the competitive environment of nodes

  • The fit-get-rich situation: the nodes that fit more (e.g., meeting the need of more users) gets more links. The second fittest get almost as many links as the fittest. 

    • In addition to the simple fit-get-rich model, the network is also unstable, and the structure will be influenced by events and accidents (like viral content) (Sampson). 

  • The winner-gets-all situation: the fittest node lures away almost all links, leaving nothing for other nodes. It becomes a lonely hub. 

  • “a far-from-random topology that exhibits a decentralized, clustered and highly vulnerable pattern of complex connectivity” (Sampson). 

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C. Explaining the Möbius strip of virality

• The Möbius strip of virality describes the two major and recurring steps when an ad goes viral: being seen by a person and that person share it so more people can see it. 

• More audience finds an ad through sharing with other viewers (the ad extend its reach) (Quesenberry and Coolsen).

• After watching and interpreting the relatable qualities of an ad, content viewers will decide to spread it to chosen members on their social network (Petrescu 1).

• “Clusters” in social networks usually represent close friends with stronger ties. However, clusters are exposed to similar information. In order to get new information, one has to rely more on weak ties to other clusters or nodes (Barabasi, ch. 4). This is, in my opinion, very true since the reach of an ad exactly happens when it is shared across many weak ties to reach other groups of people.

• The measurement of visibility of a weblink: more incoming links = more visible web page (which could have ad videos on them) (Barabasi, ch. 5)

 

D. Resource to be analyzed more and useful for Arc 2 & 3: 

Sampson, Tony D. Virality: Contagion Theory in the Age of Networks. University of Minnesota Press, 2012.

• Provides an extremely in-depth view of the mechanism of how things go viral in a network setting.

• A more modern version of Barabasi’s relatively older Linked. 

• But concept-rich and complex, e.g., “rhizomatic,” “deterritorialisation,” and “Deleuzeguattarian.”