Statistical Modeling Of Stance Detection

  • Author / Creator
    Mavrin, Borislav
  • 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.

  • Subjects / Keywords
  • Graduation date
    2017-11:Fall 2017
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. 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.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Statistical Machine Learning
  • Supervisor / co-supervisor and their department(s)
    • Niu, Di (Electrical and Computer Engineering)
    • Kong, Linglong (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Mizera, Ivan (Mathematical and Statistical Sciences)
    • Kong, Linglong (Mathematical and Statistical Sciences)
    • Zhu, Guozhong (AB School of Business)
    • Niu, Di (Electrical and Computer Engineering)
    • Kashlak , Adam (Mathematical and Statistical Sciences)