An "Opinion Reproduction Number" for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks
CoRR(2024)
Abstract
We study the spreading dynamics of content on networks. To do this, we use a
model in which content spreads through a bounded-confidence mechanism. In a
bounded-confidence model (BCM) of opinion dynamics, the agents of a network
have continuous-valued opinions, which they adjust when they interact with
agents whose opinions are sufficiently close to theirs. The employed
content-spread model introduces a twist into BCMs by using bounded confidence
for the content spread itself. To study the spread of content, we define an
analogue of the basic reproduction number from disease dynamics that we call an
opinion reproduction number. A critical value of the opinion
reproduction number indicates whether or not there is an “infodemic” (i.e., a
large content-spreading cascade) of content that reflects a particular opinion.
By determining this critical value, one can determine whether or not an opinion
will die off or propagate widely as a cascade in a population of agents. Using
configuration-model networks, we quantify the size and shape of content
dissemination using a variety of summary statistics, and we illustrate how
network structure and spreading model parameters affect these statistics. We
find that content spreads most widely when the agents have large expected mean
degree or large receptiveness to content. When the amount of content spread
only slightly exceeds the critical opinion reproduction number (i.e., the
infodemic threshold), there can be longer dissemination trees than when the
expected mean degree or receptiveness is larger, even though the total number
of content shares is smaller.
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