Browsing by Subject "fake news"

Recent progress in natural language generation tools has raised concerns that the tools are being used to generate neural fake news. Fake news impacts our society in many ways, and they have been used for monetization schemes, to tip political elections, and have been shown to have a severe effect on people’s mental health. Accordingly, being able to detect neural fake news and countering their spread is becoming increasingly important. The aim of the thesis is to explore whether there are linguistic features that can help detect neural news. Using Grover, a neural language model, I generate a set of articles based on both real and fake human-written news. I then extract a range of linguistic features, previously found to differ between human-written real and fake news, to investigate whether the same features can be used detect Grover-written news, whether there are features that can differentiate between Grover-written news, whose source material is different, and whether based on these features Grover-written news are more similar to real or fake news. The data consists of 64 articles, of which 16 are real news sourced from reputable news sites and 16 are fake news articles from the ISOT Fake News Dataset. The other 32 articles are written by Grover, with having either the real news or fake news articles as source text (16 each). A broad range of linguistic features are extracted from the article bodies and titles to capture the style, complexity, and sentiment of the articles. The features measured include punctuation, quotes, syntax tree depths, and emotion counts. The results show that the same features which have been found to differ between real and fake news, can with some limitations be used to discern Grover Fake News (Grover-written articles based on fake news). However, Grover Real News (Grover-written articles based on real news) cannot reliably be discerned from real news. Moreover, while the features measured do not provide a reliable method for discerning Grover Real News and Grover Fake News from each other, there are still noticeable differences between the two groups. Grover Fake News can be differentiated from real news, but the texts can be considered of better quality than fake news. These findings also align with previous research, showcasing that Grover is adept at re-writing misinformation and making it more credible to readers, and that feature extraction alone cannot reliably distinguish neural fake news, but that human evaluation also needs to be considered.