"Fitting in and Breaking Up: A Nonlinear Version of Coevolving Voter Models"

A paper of mine came out in final form today. Here are some details.

Title: Fitting in and Breaking Up: A Nonlinear Version of Coevolving Voter Models

Authors: Yacoub H. Kureh and Mason A. Porter

Abstract: We investigate a nonlinear version of coevolving voter models, in which node states and network structure update as a coupled stochastic process. Most prior work on coevolving voter models has focused on linear update rules with fixed and homogeneous rewiring and adopting probabilities. By contrast, in our nonlinear version, the probability that a node rewires or adopts is a function of how well it “fits in” with the nodes in its neighborhood. To explore this idea, we incorporate a local-survey parameter σ_i that encodes the fraction of neighbors of an updating node i that share its opinion state. In an update, with probability σ^q_i(for some nonlinearity parameter q), the updating node rewires; with complementary probability 1 − σ^q_i, the updating node adopts a new opinion state. We study this mechanism using three rewiring schemes: after an updating node deletes one of its discordant edges, it then either (1) “rewires-to-random” by choosing a new neighbor in a random process; (2) “rewires-to-same” by choosing a new neighbor in a random process from nodes that share its state; or (3) “rewires-to-none” by not rewiring at all (akin to “unfriending” on social media). We compar eour nonlinear coevolving voter model to several existing linear coevolving voter models on various network architectures. Relative to those models, we find in our model that initial network topology plays a larger role in the dynamics and that the choice of rewiring mechanism plays a smaller role. A particularly interesting feature of our model is that, under certain conditions, the opinion state that is held initially by a minority of the nodes can effectively spread to almost every node in a network if the minority nodes view themselves as the majority. In light of this observation, we relate our results to recent work on the majority illusion in social networks.

Short Course on "Mathematical and Computational Methods for Complex Social Systems" at the 2021 Joint Mathematics Meetings!

An exciting announcement!

We (@hzinnbrooks + @michellehfeng + Me + @al_volkening) are organizing an @amermathsoc 'Short Course' on "Mathematical and Computational Methods for Complex Social Systems" at the 2021 Joint Mathematics Meetings (@JointMath)! https://t.co/A4rXWVOWSU pic.twitter.com/e9hTEDLCN4

— Mason Porter (@masonporter) April 28, 2020

"A Model for the Influence of Media on the Ideology of Content in Online Social Networks"

One of my papers has just appeared in final form. Here are some details.

Title: A Model for the Influence of Media on the Ideology of Content in Online Social Networks

Authors: Heather Z. Brooks and Mason A. Porter

Abstract: Many people rely on online social networks as sources of news and information, and the spread of media content with ideologies across the political spectrum influences online discussions and impacts offline actions. To examine the impact of media in online social networks, we generalize bounded-confidence models of opinion dynamics by incorporating media accounts as influencers in a network. We quantify partisanship of content with a continuous parameter on an interval, and we formulate higher-dimensional generalizations to incorporate content quality and increasingly nuanced political positions. We simulate our model with one and two ideological dimensions, and we use the results of our simulations to quantify the “entrainment” of content from nonmedia accounts to the ideologies of media accounts in a network. We maximize media impact in a social network by tuning the number of media accounts and the numbers of followers of those accounts. Using numerical computations, we find that the entrainment of the ideology of content that is spread by nonmedia accounts to media ideology depends on a network's structural features, including its size, the mean number of followers of its nodes, and the receptiveness of its nodes to different opinions. We then introduce content quality—a key novel contribution of our work—into our model. We incorporate multiple media sources with ideological biases and quality-level estimates that we draw from real media sources and demonstrate that our model can produce distinct communities (“echo chambers”) that are polarized in both ideology and quality. Our model provides a step toward understanding content quality and ideology in spreading dynamics, with ramifications for how to mitigate the spread of undesired content and promote the spread of desired content.

Tales from the ArXiv: Modeling Stupid People

I am intrigued by this paper, which is called Cipolla’ s game: playing under the laws of human stupidity.

Here is a screenshot to whet your appetite.