Topological data analysis for fake news detection
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 Persi...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156821 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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