Spider-Jerusalem at SemEval-2019 Task 4: Hyperpartisan News Detection

This paper describes our system for detecting hyperpartisan news articles, which was submitted for the shared task in SemEval 2019 on Hyperpartisan News Detection. We developed a Support Vector Machine (SVM) model that uses TF-IDF of tokens, Language Inquiry and Word Count (LIWC) features, and structural features such as number of paragraphs and hyperlink count in an article. The model was trained on 645 articles from two classes: mainstream and hyperpartisan. Our system was ranked seventeenth out of forty two participating teams in the binary classification task with an accuracy score of 0.742 on the blind test set (the accuracy of the top ranked system was 0.822). We provide a detailed description of our preprocessing steps, discussion of our experiments using different combinations of features, and analysis of our results and prediction errors.