The Dynamics of (Not) Unfollowing Misinformation Spreaders

Fuente: arXiv
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Autori principali: Ashkinaze, Joshua, Gilbert, Eric, Budak, Ceren
Natura: Preprint
Pubblicazione: 2024
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author Ashkinaze, Joshua
Gilbert, Eric
Budak, Ceren
author_facet Ashkinaze, Joshua
Gilbert, Eric
Budak, Ceren
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_13480
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle The Dynamics of (Not) Unfollowing Misinformation Spreaders
Ashkinaze, Joshua
Gilbert, Eric
Budak, Ceren
Social and Information Networks
Computers and Society
Human-Computer Interaction
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.
title The Dynamics of (Not) Unfollowing Misinformation Spreaders
topic Social and Information Networks
Computers and Society
Human-Computer Interaction
url https://arxiv.org/abs/2401.13480