Easy-access online social media metrics are associated with misinformation sharing activity

TL;DR

This study shows that simple social media metrics like tweet frequency and account age can help identify users more likely to share misinformation, offering a low-barrier method for early detection.

Contribution

The paper introduces a practical approach using accessible social network metrics to identify potential misinformation spreaders on Twitter.

Findings

Higher tweet frequency correlates with sharing low factuality content.

Older accounts are less likely to share misinformation.

Effects vary based on the number of accounts followed.

Abstract

Misinformation poses a significant challenge studied extensively by researchers, yet acquiring data to identify primary sharers is time-consuming and challenging. To address this, we propose a low-barrier approach to differentiate social media users who are more likely to share misinformation from those who are less likely. Leveraging insights from previous studies, we demonstrate that easy-access online social network metrics-average daily tweet count, and account age-can be leveraged to help identify potential low factuality content spreaders on X (previously known as Twitter). We find that higher tweet frequency is positively associated with low factuality in shared content, while account age is negatively associated with it. We also find that some of the effects differ depending on the number of accounts a user follows. Our findings show that relying on these easy-access social network metrics could serve as a low-barrier approach for initial identification of users who are more likely to spread misinformation, and therefore contribute to combating misinformation effectively on social media platforms.