Conspiratorial beliefs observed through entropy principles

TL;DR

This paper introduces an information-theoretic entropy framework to quantify how conspiracy theories spread by analyzing changes in entropy in the interpretation of events, based on crowd-sourced online content.

Contribution

It presents a novel entropy-based method to measure the propagation of conspiracy beliefs through analysis of information changes in event reports.

Findings

Entropy increases as conspiracy beliefs grow.

Higher entropy correlates with greater doubt in event interpretation.

Patterns of entropy change vary across different online aggregation levels.

Abstract

We propose a novel approach framed in terms of information theory and entropy to tackle the issue of conspiracy theories propagation. We start with the report of an event (such as 9/11 terroristic attack) represented as a series of individual strings of information denoted respectively by two-state variable Ei=+/-1, i=1,..., N. Assigning Ei value to all strings, the initial order parameter and entropy are determined. Conspiracy theorists comment on the report, focusing repeatedly on several strings Ek and changing their meaning (from -1 to +1). The reading of the event is turned fuzzy with an increased entropy value. Beyond some threshold value of entropy, chosen by simplicity to its maximum value, meaning N/2 variables with Ei=1, doubt prevails in the reading of the event and the chance is created that an alternative theory might prevail. Therefore, the evolution of the associated entropy is a way to measure the degree of penetration of a conspiracy theory. Our general framework relies on online content made voluntarily available by crowds of people, in response to some news or blog articles published by official news agencies. We apply different aggregation levels (comment, person, discussion thread) and discuss the associated patterns of entropy change.