M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing

Figure 4: Merged Dataset True News Wordcloud Analysis of individual and merged data sets Word Clouds provides the following insight. Firstly, the prevalence of words related to current events such as "Trump," "NRA," "Thursday," "New York," and "election" indicates a focus on recent developments and important issues. Secondly, the predominantly neutral language observed in the word cloud, featuring words like "meeting", "people", "statement", and "issue" suggests a commitment to factual reporting devoid of emotional language or unsubstantiated claims. Thirdly, the inclusion of sources like "CNN", "BBC", and "AP" signifies a diverse range of credible sources contributing to true news articles, ensuring a well-rounded perspective. Lastly, the presence of factual language with words like "deal," "rule," and "percent" underscores a focus on objective reporting, enhancing the reliability and trustworthiness of true news content. Background Information Fake news detection is critical due to its societal impacts, necessitating automated methods amid the vast online information landscape. Natural Language Processing (NLP) techniques, including text classification, sentiment analysis, and topic modeling, have emerged as promising tools for combating misinformation (Waheeb et al., 2022). Additionally, 6 31