Characteristics of Political Misinformation Over the Past Decade

TL;DR

This study analyzes political misinformation over twelve years, revealing its increasing prevalence, shifts in sharing modalities, and emotional tone, to aid in developing more robust detection algorithms.

Contribution

It provides a comprehensive temporal analysis of political misinformation characteristics, highlighting evolving sources, modalities, sentiment trends, and recurring themes.

Findings

Misinformation has increased dramatically in recent years.

Sharing sources have shifted towards text and images, with video sources rising.

Misinformation statements tend to be more negative and have become more negative over time.

Abstract

Although misinformation tends to spread online, it can have serious real-world consequences. In order to develop automated tools to detect and mitigate the impact of misinformation, researchers must leverage algorithms that can adapt to the modality (text, images and video), the source, and the content of the false information. However, these characteristics tend to change dynamically across time, making it challenging to develop robust algorithms to fight misinformation spread. Therefore, this paper uses natural language processing to find common characteristics of political misinformation over a twelve year period. The results show that misinformation has increased dramatically in recent years and that it has increasingly started to be shared from sources with primary information modalities of text and images (e.g., Facebook and Instagram), although video sharing sources containing misinformation are starting to increase (e.g., TikTok). Moreover, it was discovered that statements expressing misinformation contain more negative sentiment than accurate information. However, the sentiment associated with both accurate and inaccurate information has trended downward, indicating a generally more negative tone in political statements across time. Finally, recurring misinformation categories were uncovered that occur over multiple years, which may imply that people tend to share inaccurate statements around information they fear or don't understand (Science and Medicine, Crime, Religion), impacts them directly (Policy, Election Integrity, Economic) or Public Figures who are salient in their daily lives. Together, it is hoped that these insights will assist researchers in developing algorithms that are temporally invariant and capable of detecting and mitigating misinformation across time.