[2015 Spring, Complex System Seminar] Go Viral!

Social Influence

1. Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market ([1])

  • Method
    • Authors created an artificial music market and recruited 14,341 participants (mostly teenagers) and provide them unknown musics from unknown bands. After listening the songs they chose, they are asked to rate the quality of the songs and to decide whether to download or not.
    • Participants are assigned into two groups randomly.
    • Independent: Only names of the bands and their songs are provided as an information.
    • Social Influence: Not only above information, but also download counts of each song by others are known. This social influence group is separated into 8 subgroups, in which each subgroup is evolved independently each other.
    • This experiment are operated two times, which are different in the visualizing way of download counts.
    • In experiment 1, the download counts of 48 songs are shown in 16*3 grid, in a random order.
    • In experiment 2, the counts are shown in one column in a descending order.
  • Result
    • Gini coefficient of Exp. 2 > Gini coefficient of Exp. 1 > Gini coefficient of Independent Group
    • Unpredictability of Exp. 2 > Unpredictability of Exp. 1 > Unpredictability of Independent Group (Unpredictability: avg. difference in market share of a song in 8 different worlds)
    • Market share in Exp 1. looks linearly correlated with market share in Independent group, while that in Exp 2. looks exponentially correlated with that in Independent group.
  • Personal opinion
    • If it traces the dynamic processes to become top-ranked for some most popular songs, it would be also interesting. (Finding phase transition moments and conditions.)

2. Complex Contagions and the Weakness of Long Ties ([2])

  • Related works
    • Two different meanings of tie strength according to Granovetter
    • relational: strong tie means close friend, family, while weak tie means an acqaintance.
    • structural: strong tie means having higher ability to facilitate diffusion, cohesion, and integration of its social network by linking others.
    • Granovetter’s insight is that a weak tie in relation can be a strong tie in structure by doing a job as shortcuts in small-world network.
    • Threshold model in contagion process (by Granovetter and Schelling)
    • Mechanisms of Complex Contagion
    1. Strategic complementarity: When the (social or economic) cost for adoptation decreases as the number of adopted people around increases.
    2. Credibility: Some innovations (or information) become reliable enough to adopt when my credible neighbors already adopted them.
    3. Legitimacy: The number of close friends who participated matters to recognize the event or social movement legitimate.
    4. Emotional Contagion

3. A 61-million-person experiment in social influence and political mobilization ([3])

  • Question: Can political behaviour spread through an online social network?
  • Effect of message to encourage voting
    3_1

    • Dividing all Facebook users over 18 years in the US into 3 groups: social message group, informational message group, and control group
    • Social message group (N = 60,055,176) vs. Informational message group (N = 611,044): Different in whether show the profile pictures of 6 friends in a message.
    • Not only using self-reported voting (“I Voted” in the message), they also used the examination of public voting records.
  • Effect of strong ties
    3_2

    • Validating that Facebook friends with more interactions are likely to be closer friends.
    • Then, based on this interaction counts, they compared the effect on voting behavior measured in 3 different ways, depending on the closeness.
    • Followings are their explanation.
    • “Figure 2 shows that the observed per-friend treatment effects increase as tie-strength increases. All of the observed treatment effects fall outside the null distribution for expressed vote (Fig. 2b), suggesting that they are significantly different from chance outcomes. For validated vote (Fig. 2c), the observed treatment effect is near zero for weak ties, but it spikes upwards and falls outside the null distribution for the top two deciles. This suggests that strong ties are important for the spread of real-world voting behaviour. Finally, the treatment effect for polling place search gradually increases (Fig. 2d), with several of the effects falling outside the 95% confidence interval of the null distribution.”
    • However, if you see the graph, the mean changes in probability to vote look same and only the variances become larger as the amount of interaction is bigger. (It might be because the number of close friends are much smaller than that of just frieds?)

4. Structural diversity in social contagion ([4])

4_1

  • Contagion of joining Facebook through friends’ invitation e-mails
  • Data: corpus of 54 million invitation e-mails
  • Question: “How does an individual’s probability of accepting an invitation depend on the structure of his or her contact neighborhood?”
  • Result: Acceptance probability is related to the number of connected components in the contact neighborhood (existing users who have an e-mail account of a user in common). That is, more components, higher probability to accept

[1] – M.J. Salganik et al., Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market, Science 311, 854 (2006).
[2] – D. Centola and M. Macy, Complex Contagions and the Weakness of Long Ties, AJS 113, 702 (2007).
[3] – R.M. Bond et al., A 61-million-person experiment in social influence and political mobilization, Nature 489, 295 (2012).
[4] – Ugander, Johan, et al. “Structural diversity in social contagion.” Proceedings of the National Academy of Sciences (2012): 201116502.

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[2015 Spring, Complex System Seminar] Go Viral!

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