Temporal Analysis of Misinformation on Parler

TL;DR

This study analyzes the spread of misinformation on Parler, revealing that a significant portion of posts are false, especially around major events, and categorizing misinformation into COVID-19, election, and BLM topics.

Contribution

It provides a large-scale temporal analysis of misinformation on Parler, linking misinformation trends to real-world events and categorizing prevalent false claims.

Findings

69.2% of posts labeled as 'false'

Misinformation spikes around major events

Identified three main misinformation categories

Abstract

Social media platforms have facilitated the rapid spread of dis- and mis-information. Parler, a US-based fringe social media platform that positions itself as a champion of free-speech, has had substantial information integrity issues. In this study, we seek to characterize temporal misinformation trends on Parler. Comparing a dataset of 189 million posts and comments from Parler against 1591 rated claims (false, barely true, half true, mostly true, pants on fire, true) from Politifact, we identified 231,881 accuracy-labeled posts on Parler. We used BERT-Topic to thematically analyze the Poltifact claims, and then compared trends in these categories to real world events to contextualize their distribution. We identified three distinct categories of misinformation circulating on Parler: COVID-19, the 2020 presidential election, and the Black Lives Matter movement. Our results are significant, with a surprising 69.2% of posts in our dataset found to be 'false' and 7.6% 'barely true'. We also found that when Parler posts ('parleys') containing misinformation were posted increased around major events (e.g., George Floyd's murder).