Social Contagion: From Virus to Viruses

In the world of Viral Marketing – as well as in the social networks analysis field – there is the tendency to assume that social contagion is a uniform phenomenon, whose dynamics are always the same for any content in any context (at least at the macro level). But are we sure that it is really enough to create a viral content and to deliver it to the adequate opinion leaders for obtaining the desired effect, without worrying about the micro level? A series of experiments seems to refute this view, suggesting that a more articulated approach is needed. According to our findings, traditional approaches based on “popularity metrics” (e.g. the number of I like) are not sufficient. Virality is a multi-faceted phenomenon, which can bring about different audience reactions depending on the characteristics of the delivered content (exactly in the same way different viruses can provoke different symptoms). Reactions, of course, must be carefully considered in planning a successful promotion campaign. Let's see in detail what are these different forms of "viral symptoms":

  • Appreciation: how much people like a given content, for example by clicking an "I like" button.
  • Spreading: how much people tend to share this content by forwarding it to other people.
  • Simple buzz: how much people tend to comment a given content.
  • White buzz: how much people tend to comment in a positive mood (e.g. “The best product I have ever bought”).
  • Black buzz: how much people tend to comment in a negative mood (e.g. “Do not buy this product, it is a rip-off”).
  • Raising discussion: the ability to induce discussion among users, rather than on the content itself.
  • Controversiality: the ability to split the audience in different parties (usually pro and against the given content).
  • Fostering elaboration: the ability to induce the audience to elaborate on the given content, by writing long comments.

To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be independently predicted according to content features. A psyco-linguistic analisys further supported this view showing that different linguistic styles bear different effects. Further details can be found in: 

Guerini, M., Strapparava, C., Ozbal, G.: Exploring text virality in social networks. In: Proceedings of ICWSM’11. 2011. (PDF)

Strapparava, C., Guerini, M., Ozbal, G.: Persuasive language and virality in social networks. In: Proceedings of ACII ’11. 2011.

PhD in Information and Communication Technologies. Personal website.

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