NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 971–975 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 971 NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector Duc-Vu Nguyen♦, Dang Van Thin♦, and Ngan Luu-Thuy Nguyen♥ ♦Multimedia Communications Laboratory ♥Faculty of Computer Science University of Information Technology, Vietnam National University Ho Chi Minh City, Vietnam {vund,thindv,ngannlt}@uit.edu.vn Abstract This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperparti- san argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. The Pa- parazzo system, in which we use tri-grams of words and hepta-grams of characters, offi- cially ranks thirteen with an accuracy of 0.747. Another system of ours, which utilizes tri- grams of words, tri-grams of characters, tri- grams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4. 1 Introduction Fake news is a noteworthy term in recent years. The rise of users and rapid spread information on social networking have made on automatic con- trolling of fake news more difficult. Fake news ar- ticles are typically extremely one-sided (hyperpar- tisan), inflammatory, emotional, and often riddled with untruths (Potthast et al., 2018). The influence of misinformation varies depending on the style it is written in. For example, sarcasm in a sports news article will have less of an impact than news written in the hyper-partisan argumentation style, which can sway voter decision in an election. Hyperpartisan detection in news articles is one of the ways to control fake news on the media and public. Kiesel et al. (2019) provided a new task, which they name “Hyperpartisan News De- tection,” to decide whether a news article text fol- lows a hyperpartisan argumentation. We approach this task following traditional text classification by extracting style features. The bag-of-words model is the way of text representation and is applied to sentiment analysis effectively (Pang et al., 2002). Matsumoto et al. (2005) applied text mining tech- niques on dependency sub-trees as features for sentiment analysis at the document level. Our re- sults show that n-grams of words and dependency sub-trees features from sentences of the document have certain impacts on the performance of the classifier. The details of the features in our sys- tems and the results are described in Section 3 and Section 4. 2 Task Description SemEval2019 Task 4 has only one task, in which participants are required to build the systems for hyperpartisan news detection. The task is to predict which category (“hyper-partisan” vs. “not hyper-partisan”) an argumentation belongs to when given the news article in English (Kiesel et al., 2019). There are 645 articles in the for-ranking training set, and 628 articles in the for-ranking testing set (all of them are labeled through crowdsourcing on an article basis). Be- sides, the organizers …

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