The Dynamics of (Not) Unfollowing Misinformation Spreaders

TL;DR

This paper investigates how users disengage from health misinformation spreaders on Twitter, revealing that unfollowing is rare ($\approx$0.52% per month) and that misinformation ties are highly persistent. Through a two-study design with large-scale, edge-level data, it identifies reciprocity, initial exposure, and partisan ideology as key predictors of unfollowing, with liberals more likely to unfollow than conservatives and a stronger effect for extreme liberals. The findings imply that interventions should focus on preventing initial exposure and consider ideological differences when designing nudges to dissolve misinformation ties. Overall, the study highlights the resilience of misinformation networks and the potential value of targeted strategies to reduce exposure at the source rather than relying on voluntary unfollowing alone.

Abstract

Many studies explore how people 'come into' misinformation exposure. But much less is known about how people 'come out of' misinformation exposure. Do people organically sever ties to misinformation spreaders? And what predicts doing so? Over six months, we tracked the frequency and predictors of ~900K followers unfollowing ~5K health misinformation spreaders on Twitter. We found that misinformation ties are persistent. Monthly unfollowing rates are just 0.52%. In other words, 99.5% of misinformation ties persist each month. Users are also 31% more likely to unfollow non-misinformation spreaders than they are to unfollow misinformation spreaders. Although generally infrequent, the factors most associated with unfollowing misinformation spreaders are (1) redundancy and (2) ideology. First, users initially following many spreaders, or who follow spreaders that tweet often, are most likely to unfollow later. Second, liberals are more likely to unfollow than conservatives. Overall, we observe a strong persistence of misinformation ties. The fact that users rarely unfollow misinformation spreaders suggests a need for external nudges and the importance of preventing exposure from arising in the first place.

Figures (11)

• Figure 1: Graphical summary of studies.
• Figure 2: Distribution of the political ideology of followers from the Modeling Sample, measured using the method from barbera_birds_2015. Most followers are conservative.
• Figure 3: Number of misinformation spreaders from the Modeling Sample followed at T1. Ideology is cut at zero using barbera_birds_2015. Misinformation exposure is right-skewed.
• Figure 4: Characteristics of those Modeling Sample followers who are in the top 10% for following misinformation spreaders. Ideology is a continuous measure where positive is conservative. 'Recip' is the proportion of a follower's ties to spreaders that are reciprocated.
• Figure 5: Comparing unfollowing rates across studies, misinformation spreaders are unfollowed relatively infrequently.