M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing Introduction Our project involves the detection of fake news using machine learning and Natural Language Processing techniques while prioritizing explainability for prediction. In recent years, the term "fake news" has gained significant attention, referring to news articles lacking factual basis and often intended to mislead or promote certain agendas (Desai & Oehrli, 2023). These articles may contain outright falsehoods or omit crucial contextual information. The detection of fake news is crucial due to its detrimental impact on society and democratic processes. For instance, during the 2016 United States election, misinformation played a significant role, influencing voters and undermining the democratic process (Guess et al., 2020). Moreover, fake news during the COVID-19 pandemic has disrupted public health responses and posed serious risks to public safety (Nelson et al., 2020). The data used for production will be a combination of multiple datasets from Kaggle and universities, spanning topics including politics, news, and sports. We decided to use multiple sets to increase the available data and to introduce diversity. The AI product/model targets various end users, including individuals, media organizations, fact-checking agencies, and social media platforms. Individuals can verify news authenticity before sharing, while media organizations and fact-checkers can utilize it to detect misleading content. Integration into social media platforms would help flag or remove fake news, curbing its spread. Our project aims to develop a system employing natural language processing and machine learning to identify fake news. Using deep learning techniques, we'll classify text as trustworthy or fake, deploying the best-performing model through Gradio for

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