Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156821
Title: Topological data analysis for fake news detection
Authors: Deng, Ran
Keywords: Science::Mathematics::Topology
Science::Mathematics::Statistics
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Deng, R. (2022). Topological data analysis for fake news detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156821
Abstract: This project aims to contribute to the under-researched field of topological data analysis (TDA) for text classification through the task of fake news detection. For this task, three individual models have been used: least absolute shrinkage and selection operator (LASSO) using 0th Dimensional Persistent Image (PI) vectors, Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). Two ensemble models were also used to improve performances by supplementing contextual information from deep-learning models with structural information from PI vectors: BiLSTM + TDA and BERT + TDA. The results suggest that when structural information is given equal or lesser influence than contextual information, the ensemble performs better than the base models on average. This project offers a possible way of utilising TDA features to improve performances in text classification tasks, and a comparison between different models for organizations concerned with false information detection in general.
URI: https://hdl.handle.net/10356/156821
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

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