University of Twente Student Theses


Sentiment as a ground truth stance indicator against fake news

Steegh, E.G.A.J. (2021) Sentiment as a ground truth stance indicator against fake news.

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Abstract:This report investigates a novel ground truth approach to fake news detection, focusing on sentiment detection to interpret the author’s stance. A three-pronged model was built combining an LDA for topic detection, VADER to create sentiment sequence, a stacked LSTM to interpret those sentiment sequences and a statistical description of the sequences. The model interpreted the popular ISOT fake news dataset to build a knowledge base and achieved an 80% instance classification accuracy on the dataset using J48, finding value in sentiment analysis as a tool for fake news detection.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:17 linguistics and theory of literature, 54 computer science
Programme:Creative Technology BSc (50447)
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