Social contagion induced by uncertain information

TL;DR

This paper investigates how uncertainty and probabilistic inference in social threshold models influence the spread of information, revealing that misperceptions can trigger cascades where certainty would prevent them.

Contribution

It introduces a probabilistic threshold model accounting for uncertainty, showing non-monotonic spreading and the potential for cascades driven by misperceptions.

Findings

Uncertainty can induce social cascades not seen under certainty.

Probabilistic inference leads to non-monotonic spreading dynamics.

Misperceptions can trigger self-fulfilling cascades.

Abstract

Information and individual activities often spread globally through the network of social ties. While social contagion phenomena have been extensively studied within the framework of threshold models, it is common to make an assumption that may be violated in reality: each individual is able to observe the neighbors' states without error. Here, we analyze the dynamics of global cascades under uncertainty in an otherwise standard threshold model. Each individual uses statistical inference to estimate the probability distribution of the number of active neighbors when deciding whether to be active, which gives a probabilistic threshold rule. Unlike the deterministic threshold model, the spreading process is generally non-monotonic as the inferred distribution of neighbors' states may be updated as a new signal arrives. We find that social contagion may arise as a self-fulfilling event in that misperception may trigger a cascade in regions where cascades would never occur under certainty.