CLNews: The First Dataset of the Chilean Social Outbreak for Disinformation Analysis
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Date
2022-10-17
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Abstract
Disinformation is one of the main threats that loom on social networks. Detecting disinformation is not trivial and requires training
and maintaining fact-checking teams, which is labor-intensive. Recent studies show that the propagation structure of claims and user
messages allows a better understanding of rumor dynamics. Despite these findings, the availability of verified claims and structural
propagation data is low. This paper presents a new dataset with
Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies. The dataset contains verified
claims checked during the Chilean social outbreak, which allows for
studying the phenomenon of disinformation during this crisis. We
study propagation patterns of verified content in CLNews, showing
differences between false rumors and other types of content. Our
results show that false rumors are more persistent than the rest
of verified contents, reaching more people than truthful news and
presenting low barriers of readability to users. The dataset is fully
available and helps understand the phenomenon of disinformation
during social crises being one of the first of its kind to be released.
Description
Keywords
Rumor detection, Disinformation, Propagation trees