About: Misinformation Has High Perplexity

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Misinformation Has High Perplexity
Creator	Bang, Yejin Fung, Pascale Lee, Nayeon Madotto, Andrea
source	ArXiv
abstract	Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly. However, the usual supervised approach to debunking via misinformation classification requires human-annotated data and is not suited to the fast time-frame of newly emerging events such as the COVID-19 outbreak. In this paper, we postulate that misinformation itself has higher perplexity compared to truthful statements, and propose to leverage the perplexity to debunk false claims in an unsupervised manner. First, we extract reliable evidence from scientific and news sources according to sentence similarity to the claims. Second, we prime a language model with the extracted evidence and finally evaluate the correctness of given claims based on the perplexity scores at debunking time. We construct two new COVID-19-related test sets, one is scientific, and another is political in content, and empirically verify that our system performs favorably compared to existing systems. We are releasing these datasets publicly to encourage more research in debunking misinformation on COVID-19 and other topics.
has issue date	2020-06-08(xsd:dateTime)
has license	arxiv
sha1sum (hex)	e764597776aee7093aee356c45bcf5a60b5a6561
resource representing a document's title	Misinformation Has High Perplexity
resource representing a document's body	covid:e764597776aee7093aee356c45bcf5a60b5a6561#body_text
is schema:about of	named entity 'political' named entity 'claims' named entity 'manner' named entity 'sentence' named entity 'COVID-19' »more»

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