An Explainable Knowledge Graph Based Machine Learning Model for Fact Checking

Simple item page
dc.contributor.advisorNguyen, Uyen T.
dc.contributor.authorKundu, Arghya
dc.date.accessioned2024-03-18T18:13:18Z
dc.date.available2024-03-18T18:13:18Z
dc.date.issued2024-03-16
dc.date.updated2024-03-16T10:47:06Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractMisinformation is a growing threat to the economy, social stability, public health, democracy, and national security. One of the most effective methods to combat misinformation is fact checking. In this thesis, we propose fact checking methods using NLP and misinformation propagation patterns. The contributions are, A KG-based fact checking model that uses two separate KGs, one containing true claims and the other, false claims. Additionally, we employ XAI techniques to provide explanations for the model's classification, increasing transparency and user trust. A propagation-based classifier to complement the above KG-based fact checking model for misinformation detection on Twitter. This model uses temporal, spatial and "infectiousness" properties of misinformation. A translator program that converts text with slang and non-standard words (SNSW) into standard English for fact checking on Reddit. The translated content is then input into the above KG-based fact checking model, increasing the model's accuracy.
dc.identifier.urihttps://hdl.handle.net/10315/41936
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer engineering
dc.subjectComputer science
dc.subject.keywordsKnowledge graph
dc.subject.keywordsMisinformation detection
dc.subject.keywordsXAI
dc.subject.keywordsTwitter
dc.subject.keywordsReddit
dc.titleAn Explainable Knowledge Graph Based Machine Learning Model for Fact Checking
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Kundu_Arghya_2023_Masters.pdf
Size:
3.57 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Electrical and Computer Engineering

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.