Echo chambers in the Ising model and implications on the mean magnetization

@article{Baravi2022EchoCI,
  title={Echo chambers in the Ising model and implications on the mean magnetization},
  author={Talia Baravi and Ofer Feinerman and Oren Raz},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  year={2022},
  volume={2022}
}
The echo-chamber effect is a common term in opinion dynamic modeling to describe how a person’s opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i… 

References

SHOWING 1-10 OF 41 REFERENCES
Depolarization of echo chambers by random dynamical nudge
TLDR
The random dynamical nudge (RDN) is introduced, which presents each agent with input from a random selection of other agents’ opinions and does not require surveillance of every person’s opinions to prevent the segregation of online communities on complex social issues.
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution
TLDR
An alternate model that treats individual opinions as spins in an Ising system at dynamic equilibrium is considered, which formalizes the Ising influence maximization problem and presents a gradient ascent algorithm that uses the susceptibility to efficiently calculate local maxima of the magnetization.
Statistical mechanics of influence maximization with thermal noise
TLDR
By introducing thermal noise into influence models, the dynamics exactly resemble spins in a heterogeneous Ising system, and it is demonstrated that influence maximization depends crucially on the temperature of the system, a fact that has not been appreciated by existing research.
Maximizing Activity in Ising Networks via the TAP Approximation
TLDR
This work proposes a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the naive mean field and TAP approximations and provides sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of 1-1/e.
Heterogeneous Preference and Local Nonlinearity in Consensus Decision Making.
TLDR
It is revealed that individuals (spins) without a preference play a central role in collective decision making, both in maximizing the ability of the system to achieve consensus and in minimizing the time taken to do so (via a process reminiscent of stochastic resonance).
Influence maximization in social networks: An ising-model-based approach
TLDR
A greedy placement algorithm is developed that can efficiently find an appropriate subset of network members, “bribing” whom can efficiently propagate a particular opinion in the network, using an Ising-model-based approach.
The echo chamber effect on social media
TLDR
A comparative analysis of more than 100 million pieces of content concerning several controversial topics from Gab, Facebook, Reddit, and Twitter shows that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter.
On the Phase Diagram of the Random Field Ising Model on the Bethe Lattice
The ferromagnetic Ising model on the Bethe lattice of degree k is considered in the presence of a dichotomous external random field ξx = ±α and the temperature T≥0. We give a description of a part of
The echo chamber is overstated: the moderating effect of political interest and diverse media
ABSTRACT In a high-choice media environment, there are fears that individuals will select media and content that reinforce their existing beliefs and lead to segregation based on interest and/or
On the computational complexity of Ising spin glass models
TLDR
In a spin glass with Ising spins, the problems of computing the magnetic partition function and finding a ground state are studied and are shown to belong to the class of NP-hard problems, both in the two-dimensional case within a magnetic field, and in the three-dimensional cases.
...
...