Abstract
As social media technologies alter the variation, transmission and sorting of online information, short-term cultural evolution is transformed. In these media contexts, cultural evolution is an intra-generational process with much ‘horizontal’ transmission. As a pertinent case study, here we test variations of culture-evolutionary neutral models on recently-available Twitter data documenting the spread of true and false information. Using Approximate Bayesian Computation to resolve the full joint probability distribution of models with different social learning biases, emphasizing context versus content, we explore the dynamics of online information cascades: Are they driven by the intrinsic content of the message, or the extrinsic value (e.g., as a social badge) whose intrinsic value is arbitrary? Despite the obvious relevance of specific learning biases at the individual level, our tests at the online population scale indicate that unbiased learning model performs better at modelling information cascades whether true or false.
Highlights
Cultural evolution is undoubtedly altered by social media technologies, which impose new, often algorithmic, biases on social learning at an accelerated tempo on a vast virtual landscape of interaction
The number of parameters is, implicitly, taken into account in the Bayes factor: To approximate the likelihood while doing the Approximate Bayesian Computation (ABC) we randomly sample the same number of data points from the prior distribution, if the number of parameters for one model is higher, the parameter space is bigger and the sample size drawn from the prior will cover a smaller fraction of the total space, yielding a lower probability to find good simulations that fall under our λ threshold
In calibrating neutral model variations against distributions of Twitter cascade sizes, we find that the Unbiased neutral model applies well to re-tweeting activity, and better than models with added conformity bias
Summary
Cultural evolution is undoubtedly altered by social media technologies, which impose new, often algorithmic, biases on social learning at an accelerated tempo on a vast virtual landscape of interaction. Models of unbiased (neutral) copying have been applied to social media (Gleeson et al, 2014) In this approach, we assume that the probability of a message being re-tweeted depends only on the current frequency of the message and not on its content. To model the dynamics of Twitter cascades, we test several different models of context-biased learning These models can be compared based on their ability to replicate the data while minimizing the number of model parameters. We run the model until reaching a steady state (for τ = 4μ−1 time steps (Evans and Giometto, 2011)) In this basic unbiased copying model, a re-tweet is chosen from among N Twitter users, as opposed to choosing from the different Tweet messages themselves.
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