M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing During the research process, we discovered that a common feature of fake news is its tendency to use emotional language (Hayes-Bohanan, 2023). To translate this into a predictive feature we employed nltk’s SentimentIntensityAnalyzer to perform sentiment analysis. We used the compound score of each piece of text, a float value between -1 and 1 where more negative values mean negative sentiment and positive indicating positive sentiment. When plotting the results by class label we discovered that true text was more likely to use neutral wording while fake news was more likely to use more emotional language. Overall these results support the predictive power of text sentiment for fake news detection.

Figure 2: Distribution of sentiment polarity scores in real and fake texts. Interpreting Fake News Word Cloud We analyzed the fake and real text by creating Word Clouds displaying common words found in texts. Below are the Word Clouds and our interpretations.

4

29