Fake news is capturing increasing attention worldwide and severely impacting our society. In this thesis, I examine three aspects of this issue with the help of computerized textual analysis techniques. In the first essay, I propose a domain adaptive transfer learning approach to detect fake content. I extract domain-invariant linguistic features associated with fake general news and transfer them to three specific domains: political news, financial news and online reviews, to address the missing label problem in fake news detection. I further derive a measure to explain the utility of transfer learning. In the second essay, I investigate the impact of fake news on finance and social media. For the impact on finance, I first show that humans cannot tell the authenticity of news, while their perceived fakeness of financial news influences their trading intention. I then conduct a survey study to examine what factors constitute fake news perception. By leveraging data collected from the survey study, I apply domain adaptive transfer learning to infer any financial news’s perceived fakeness and show that this deep learning-based perceived fakeness predicts less trading volume and stock volatility. For the impact on social media, I apply the same model to study how individuals interact with deceptive tweets on social media during social crises and how they respond to these tweets. In the third essay, I investigate the effectiveness of two platform interventions (i.e., a fake news flag and a forwarding restriction policy) in combating fake news. By leveraging latent semantic analysis, word lexicon, and topic modeling, I provide empirical evidence to the impacts of platform interventions on fake news dissemination and survival, as well as the underlying mechanisms.
| Date of Award | 9 Jun 2021 |
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| Original language | English |
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| Awarding Institution | - Hong Kong University of Science and Technology
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| Supervisor | Kar Yan Tam (Chief supervisor) & Mike Ka Pui So (Co-supervisor) |
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Three Essays on Computerized Textual Analysis of Fake News
Ng, K. C. (Author). 9 Jun 2021
Student thesis: PhD