DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification

TL;DR

This paper investigates how misinformation spreads on Twitter during COVID-19 by analyzing social interactions and content homogeneity, revealing a correlation between user behavior and tweet topics that supports the echo chamber hypothesis.

Contribution

It introduces a novel metric to measure social interaction diversity and links social network behavior with content homogeneity in misinformation spread.

Findings

Correlation between social interaction patterns and tweet content.

Homogeneous content is associated with less diverse social interactions.

Behavioral patterns support the echo chamber hypothesis in misinformation communities.

Abstract

The emergence of the COVID-19 pandemic resulted in a significant rise in the spread of misinformation on online platforms such as Twitter. Oftentimes this growth is blamed on the idea of the "echo chamber." However, the behavior said to characterize these echo chambers exists in two dimensions. The first is in a user's social interactions, where they are said to stick with the same clique of like-minded users. The second is in the content of their posts, where they are said to repeatedly espouse homogeneous ideas. In this study, we link the two by using Twitter's network of retweets to study social interactions and topic modeling to study tweet content. In order to measure the diversity of a user's interactions over time, we develop a novel metric to track the speed at which they travel through the social network. The application of these analysis methods to misinformation-focused data from the pandemic demonstrates correlation between social behavior and tweet content. We believe this correlation supports the common intuition about how antisocial users behave, and further suggests that it holds even in subcommunities already rife with misinformation.