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 | Schools: | School of Physical and Mathematical Sciences | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SPMS Student Reports (FYP/IA/PA/PI) |
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MH4900_FYP_Report_Final.pdf Restricted Access | 615.06 kB | Adobe PDF | View/Open |
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