Check out our new paper about belief & contagion dynamics led by Rachith Aiyappa!
We show that both simple and complex contagion dynamics can emerge from a model of belief interaction network.
In other words, simple/complex contagions may be just two sides of the underlying belief dynamics.
Paper: https://www.science.org/doi/full/10.1126/sciadv.adh4439
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Why is it so hard to change the minds of "the other side"?
There are lots of cognitive mechanisms, but we focus on the interaction between beliefs and belief systems. Once you're deep into a belief system, every belief reinforces each other.
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We (w Nathaniel Rodriguez & Johan Bollen) introduced a simple model of belief interaction network in 2016: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165910
In this model, beliefs are signed edges connecting concepts, which lead to the belief interaction via social balance condition. Another point was that ..
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Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework may offer explanations for how social transitions can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We argue that the inclusion of cognitive factors into a social model could provide a more complete picture of collective human dynamics.
Information may spread like either simple or complex contagion in the same system.
https://www.nature.com/articles/srep02522
Led by Lilian Weng (https://lilianweng.github.io), we showed that hashtags may spread like simple contagion ("virally") or complex contagion and this allows us to predict their success.
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How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics and marketing applications.
The fact that the same thing can spread in fundamentally different (simple vs. complex contagion) dynamical patterns (e.g., see optimal modularity: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.113.088701) suggests that there may be a more fundamental dynamical mechanism that underpins both.
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That's where this paper comes in.
We focus on the observation that in our belief network model, there can be two very different types of influence: stabilizing and de-stabilizing.
When a belief network is "ready" to adopt a new belief (stabilizing), this new belief is readily adopted.
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But, if new belief would disrupt a stable belief network, it will be difficult to be adopted.
So, with belief weights, depending on the relationship between the new belief and the existing beliefs, the model exhibits two distinct dynamics: simple & complex contagion!
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It is also possible to reproduce the famous experiment by Damon Centola.
When we have the "simple" configuration, it spreads better in a random network than a clustered "large world"; when we have the "complex" setting, it spreads better in the clustered large-world than a small-world!
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This model can also reproduce the optimal modularity phenomenon. When the new belief is going against the existing belief system, the clustering matters more and there is an optimal amount of clustering that can spread the information best.
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The model gives us an intuitive way to think about simple v. complex contagion as well as vulnerability to mis/disinfo.
When the existing belief system is "primed" to accept a certain belief, it can spread virally; when it conflicts with existing beliefs, social reinforcement is critical.
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Finally, I think the most important take-away may be:
Just looking at the "message" is not enough to understand social contagion; it is also important to understand the belief system of the receivers.
In a way, "us" determines how a message spread rather than the message itself.
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YY Ahn
David Ruffner