FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911866042712064 |
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| author | Raza, Shaina Khan, Tahniat Chatrath, Veronica Paulen-Patterson, Drai Rahman, Mizanur Bamgbose, Oluwanifemi |
| author_facet | Raza, Shaina Khan, Tahniat Chatrath, Veronica Paulen-Patterson, Drai Rahman, Mizanur Bamgbose, Oluwanifemi |
| contents | In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information. To tackle this challenge head-on, we introduce FakeWatch, a comprehensive framework carefully designed to detect fake news. Leveraging a newly curated dataset of North American election-related news articles, we construct robust classification models. Our framework integrates a model hub comprising of both traditional machine learning (ML) techniques, and state-of-the-art Language Models (LMs) to discern fake news effectively. Our objective is to provide the research community with adaptable and precise classification models adept at identifying fake news for the elections agenda. Quantitative evaluations of fake news classifiers on our dataset reveal that, while state-of-the-art LMs exhibit a slight edge over traditional ML models, classical models remain competitive due to their balance of accuracy and computational efficiency. Additionally, qualitative analyses shed light on patterns within fake news articles. We provide our labeled data at https://huggingface.co/datasets/newsmediabias/fake_news_elections_labelled_data and model https://huggingface.co/newsmediabias/FakeWatch for reproducibility and further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09858 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections Raza, Shaina Khan, Tahniat Chatrath, Veronica Paulen-Patterson, Drai Rahman, Mizanur Bamgbose, Oluwanifemi Computation and Language In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information. To tackle this challenge head-on, we introduce FakeWatch, a comprehensive framework carefully designed to detect fake news. Leveraging a newly curated dataset of North American election-related news articles, we construct robust classification models. Our framework integrates a model hub comprising of both traditional machine learning (ML) techniques, and state-of-the-art Language Models (LMs) to discern fake news effectively. Our objective is to provide the research community with adaptable and precise classification models adept at identifying fake news for the elections agenda. Quantitative evaluations of fake news classifiers on our dataset reveal that, while state-of-the-art LMs exhibit a slight edge over traditional ML models, classical models remain competitive due to their balance of accuracy and computational efficiency. Additionally, qualitative analyses shed light on patterns within fake news articles. We provide our labeled data at https://huggingface.co/datasets/newsmediabias/fake_news_elections_labelled_data and model https://huggingface.co/newsmediabias/FakeWatch for reproducibility and further research. |
| title | FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.09858 |