Combating Fake News with Stance Detection using Recurrent Neural Networks

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/254912
Download file(s):
File Description SizeFormat 
254912.pdfFulltext986.31 kBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Combating Fake News with Stance Detection using Recurrent Neural Networks
Authors: Ågren, Christian
Ågren, Alexander
Abstract: Comparing the attitude that different news organizations have towards a certain claim or topic is an important part of the procedure used by human fact-checkers today for assessing the veracity of a news story reporting about the issue. In this thesis we focus on automating the challenging task of stance detection in the news domain, specifically determining the relative stance of an article towards a claim stated in an associated headline, making use of the labelled dataset delivered for supervision in the first stage of the Fake News Challenge. While the most successful approaches in this domain have used complex ensemble classifiers employing large sets of hand-engineered features, their performance is just marginally better than a simple bag-of-words model deriving only lexical similarity. Our approach makes use of recurrent neural networks that read headlines and articles word-by-word. The models we implement are comparable to the state-of-the-art systems, however, observing that severe overfitting occurs.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/254912
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.