Automated false claims detection using deep neural networks
Master thesis
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Date
2018-06Metadata
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- Studentoppgaver (TN-IDE) [823]
Abstract
Recently false claims and misinformation have become rampant in the web, affecting
election outcomes, stock markets, and various other societal issues. Consequently,
fact-checking and claim verification websites such as snopes.com are becoming
increasingly popular and are also being integrated into news search engines such as
Google news. However, these websites require expert analysis which is slow and
not scalable. Many recent papers have proposed machine learning methods using
handpicked linguistic and source-based cues to automate the claim verification
process. In this thesis, we propose deep neural models which avoid tedious feature
engineering and strong assumptions and yet detect false claims with high accuracy.
To achieve this, we propose a hybrid model which combines textual content of the
news articles as well as the reactions they receive in social media forums such as
Reddit. Using large-scale manually curated data from fact-checking websites such
as snopes.com, politifact.com and emergent.info we perform extensive experiments
to show that our models outperform the state-of-the-art CRF-based models.
Description
Master's thesis in Computer science