Date of Final Oral Examination (Defense)
Type of Culminating Activity
Master of Science in Computer Science
Francessa Spezzano, Ph.D.
Anne Hamby, Ph.D.
Steven Cutchin, Ph.D.
With the growth of modern technology, we are living in a world where anyone can share news with the tap of a finger. The simplified process of news sharing has brought an inundation of information on the Internet, along with a vast amount of fake news. Researchers have been working to understand the characteristics of fake news, in order to accurately identify them through automated text analysis.
In this thesis, we propose the Narrative Content and Narrative Discourse features brought from the ideas by van Laer et al., Berger et al., and Aleti et al. We performed various experiments including fake news classification, cross-dataset validation, and news popularity prediction. Overall, we confirmed that the proposed features can successfully identify fake news, and bring better results when combined with Bidirectional Encoder Representations from Transformers(BERT)-extracted features.
Kim, Hongmin, "Fake News Detection Using Narrative Content and Discourse" (2023). Boise State University Theses and Dissertations. 2045.
Available for download on Sunday, July 06, 2025