Fake News Detection with Headlines

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dc.contributor.advisorLi, Mengen_US
dc.contributor.authorRamirez, Gareden_US
dc.date.accessioned2023-12-14T21:48:44Zen_US
dc.date.available2023-12-14T21:48:44Zen_US
dc.date.issued2023-12-12en_US
dc.description.abstractFake news has become an increasing problem due to the rising use of the Internet and social media. It is important to be able to distinguish sources of fake and misleading news articles to ensure that misinformation does not sow discord, erode trust in credible sources, and negatively impact our personal and societal well-being. Moreover, in an age where many people only skim headlines without delving into the full articles, the ability to discern fake news from headlines alone becomes even more crucial. To detect and classify fake news, we implement and compare five machine learning models–naive Bayes, logistic regression, decision tree, random forest, and support vector machine–on two different datasets: a benchmark dataset and a dataset with full articles and headlines. We utilize measures such as term frequency-inverse document frequency and sentiment scores, as predictors in our models. We find that naive Bayes consistently performs best on both datasets with accuracies of 64.40% and 92.56%, respectively.en_US
dc.format.extent23 ppen_US
dc.identifier.citationRamirez, Gared. "Fake News Detection with Headlines." (2023-12-12) Rice University: https://hdl.handle.net/1911/115329en_US
dc.identifier.digitalSTAT-450-Gared-Ramirezen_US
dc.identifier.urihttps://hdl.handle.net/1911/115329en_US
dc.language.isoengen_US
dc.publisherRice Universityen_US
dc.rightsThis work is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.titleFake News Detection with Headlinesen_US
dc.typeWhite paperen_US
dc.type.dcmiTexten_US
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