6.1 Structural Virality

(Goel et al. 2015)

\(S = f(Q) + E\)

where

  • Q = skill

  • E = luck

  • S = successful viral process

Measure fraction of variance remaining after conditioning on Q

\[ F = \frac{E(var(S|Q))}{var(S)} = 1 - R^2 \]

Hence, if

  • \(R^2 \to 1\), you can succeed in the world we created based on “pure skill”

  • \(R^2 \to 0\) you can’t succeed in the world we created (because it’s based on luck)

The more variations in the initial seed, the more likely there is a larger variation in the prediction, which means lower R-squared

They show there is a theoretical limit to the predictive power of predicting cascade event due to its intrinsic dynamic properties.

6.1.1 Network Structure and position

(Ansari et al. 2018)

  • Network management on social network

  • Networking activities and ego network structure on an actor’s online success

  • Related literature:

    • Seeding viral marketing campaigns:

    • Contagion and product diffusion: network position and network structure

    • How do network structures impact online success?

  • Method: stochastic actor-based models

  • Ego Network measures

    • Ego network density

    • Degree centrality

    • Betweenness centrality

    • Closeness centrality (not applicable to ego network)

  • Endogeneity

    • Time fixed effects

    • Control function: observed time-varying and firm-specific instrumental variables

    • Instrumental Variables are those associated with the networking activities: independent US social platform on which those focal European firms also main their profile pages. (activities in the US is correlated with activities in Europe on social media, but not directly to the success of European market).

6.1.2 Seeding Strategies

Hinz et al. (2011)

  • Despite analytical models and simulations that support seeding strategies don’t work, this study finds evidence in small-scale field experiments and real campaign that seeding to well-connected people can lead to 8 times more successful because they are more active in using their greater reach (even though they don’t have more influence on their peers than less connected people)

(Chae et al. 2017)

  • seeding strategy can help increase online WOM, and decrease conversation about competing products (in the same category) but also focal brand products in other categories.

  • data: cosmetics and beauty products in Korea.

References

Ansari, Asim, Florian Stahl, Mark Heitmann, and Lucas Bremer. 2018. “Building a Social Network for Success.” Journal of Marketing Research 55 (3): 321–38. https://doi.org/10.1509/jmr.12.0417.
Chae, Inyoung, Andrew T. Stephen, Yakov Bart, and Dai Yao. 2017. “Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns.” Marketing Science 36 (1): 89–104. https://doi.org/10.1287/mksc.2016.1001.
Goel, Sharad, Ashton Anderson, Jake Hofman, and Duncan J. Watts. 2015. “The Structural Virality of Online Diffusion.” Management Science, July, 150722112809007. https://doi.org/10.1287/mnsc.2015.2158.
Hinz, Oliver, Bernd Skiera, Christian Barrot, and Jan U. Becker. 2011. “Seeding Strategies for Viral Marketing: An Empirical Comparison.” Journal of Marketing 75 (6): 55–71. https://doi.org/10.1509/jm.10.0088.