Disinformation Detection using Passive Aggressive Algorithms
Songqiao Yu, Dan Chia-Tien Lo · 2020
Disinformation, also as known as fake news, is overwhelming. Intentionally false information is widespread. However, the detection of fake news is remaining to be a challenge due to the nature of the complexity of languages. Linear regression algorithms are proven to be effective in many practices. In this paper, the Passive-Aggressive and the Multinomial Naive Bayes are studied for fake news detection that involves term frequency and inverse document frequency to vectorize news content. Our results show that the Passive-Aggressive is more efficient than Multinomial Naive Bayes by combing with term frequency and inverse document frequency and could be applied as a primary screen for complex disinformation detection practically.