Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection
Viresh Gupta, Baani Leen Kaur Jolly, Ramneek Kaur, Tanmoy Chakraborty · 2019
In this paper, we present a news bias prediction system, which we developed as part of a SemEval 2019 task.We developed an XG-Boost based system which uses character and word level n-gram features represented using TF-IDF, count vector based correlation matrix, and predicts if an input news article is a hyperpartisan news article.Our model was able to achieve a precision of 68.3% on the test set provided by the contest organizers.We also run our model on the Buz-zFeed corpus and find XGBoost with simple character level N-Gram embeddings to be performing well with an accuracy of around 96%.