Structurizing Misinformation Stories via Rationalizing Fact-Checks

Christo Wilson, Shan Jiang

Abstract:

Misinformation has recently become a welldocumented matter of public concern. Existing studies on this topic have hitherto adopted
a coarse concept of misinformation, which incorporates a broad spectrum of story types
ranging from political conspiracies to misinterpreted pranks. This paper aims to structurize these misinformation stories by leveraging fact-check articles. Our intuition is that
key phrases in a fact-check article that identify
the misinformation type(s) (e.g., doctored images, urban legends) also act as rationales that
determine the verdict of the fact-check (e.g.,
false). We experiment on rationalized models
with domain knowledge as weak supervision
to extract these phrases as rationales, and then
cluster semantically similar rationales to summarize prevalent misinformation types. Using archived fact-checks from Snopes.com, we
identify ten types of misinformation stories.
We discuss how these types have evolved over
the last ten years and compare their prevalence
between the 2016/2020 US presidential elections and the H1N1/COVID-19 pandemics.