Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a headline can be determined by supporting or refuting evidence found in a resulting web query. These models are believed to be reasoning in some way; however, it has been shown that these same results, or better, can be achieved without considering the claim at all – only the evidence. This implies that other signals are contained within the examined evidence, and could be based on manipulable factors such as emotion, sentiment, or part-of-speech (POS) frequencies, which are vulnerable to adversarial inputs. We neutralize some of these signals through multiple forms of both neural and non-neural pre-processing and style transfer, and find that this flattening of extraneous indicators can induce the models to actually require both claims and evidence to perform well. We conclude with the construction of a model using emotion vectors built off a lexicon and passed through an ``emotional attention'' mechanism to appropriately weight certain emotions. We provide quantifiable results that prove our hypothesis that manipulable features are being used for fact-checking.

Automatic Fake News Detection: Are current models “fact-checking” or“gut-checking”?

**Organizers:** Rami Aly, Christos Christodoulopoulos, Oana Cocarascu, Zhijiang Guo, Arpit Mittal,
Michael Schlichtkrull, James Thorne, Andreas Vlachos
 **Description:** 
With billions of individual pages on the web providing information on almost every conceivable topic, we should have the
ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is
contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form
text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research
and media coverage: false information coming from unreliable sources. The FEVER workshops are a venue for work in
verifiable knowledge extraction and to stimulate progress in this direction.
 
**Please visit our [website](https://fever.ai/)**

(FEVER)        Fifth Workshop on Fact Extraction and VERification

workshop paper

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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Automatic Fake News Detection: Are current models “fact-checking” or“gut-checking”?

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