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Permanent link (DOI): https://doi.org/10.7939/R3CF9JM4S

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Statistical Modeling Of Stance Detection Open Access

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Other title
Subject/Keyword
NLP
RNN
stance detection
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Mavrin, Borislav
Supervisor and department
Niu, Di (Electrical and Computer Engineering)
Kong, Linglong (Mathematical and Statistical Sciences)
Examining committee member and department
Zhu, Guozhong (AB School of Business)
Kashlak , Adam (Mathematical and Statistical Sciences)
Mizera, Ivan (Mathematical and Statistical Sciences)
Niu, Di (Electrical and Computer Engineering)
Kong, Linglong (Mathematical and Statistical Sciences)
Department
Department of Mathematical and Statistical Sciences
Specialization
Statistical Machine Learning
Date accepted
2017-09-26T09:30:50Z
Graduation date
2017-11:Fall 2017
Degree
Master of Science
Degree level
Master's
Abstract
In recent years fake news has become a more serious problem. This is mainly due to the popularity of social networks, search engines and news ag- gregators that propagate fake news. Classifying news as fake is a hard problem. However it is possible to distinguish between fake and real news, by consider- ing how many related tweets agree/disagree with the news. Therefore, in the simplest case the problem can be reduced to identifying whether a given tweet agrees with, disagrees with or is unrelated to the news in question. In general this problem is referred to as ’stance detection’. In machine learning termi- nology this is a classification problem. This thesis investigates more advanced Natural Language Models, such as matching Long Short Term Memory model and soft attention mechanism applied to stance detection problem. The ideas are tested using a publicly available data set.
Language
English
DOI
doi:10.7939/R3CF9JM4S
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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