Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 1078–1082 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 1078 Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features Vertika Srivastava Sudeep Kumar Sahoo Ankita Gupta Rohit R.R Divya Prakash Yeon Hyang Kim Samsung R&D Institute India, Bangalore {v.srivastava, gupta.ankita, p.divya, sudeep.sahoo, rohit.r.r, purine.kim}@samsung.com Abstract In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (one- sided). It shows blind beliefs, opinions and un- reasonable adherence to a party, idea, faction or a person. Through this task, we aim to de- velop an automated system that can be used to detect hyperpartisan news and serve as a pre- screening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0% accuracy) and 1st rank on the secondary metric (82.1% F1-score), among all participating teams. Comparison with the best performing system on the leaderboard1 shows that our system is behind by only 0.2% abso- lute difference in accuracy. 1 Introduction Today in the age of digitization, a smartphone has become an indispensable tool for information sharing and consumption. It is much more conve- nient for users to read news through online articles and social media platforms. These platforms pro- vide them quick and easy access to information almost everywhere. However, it is highly possi- ble that the shared information is unverified and may bias the reader’s opinions. This issue is ex- acerbated by the fact that most of the people find it difficult to distinguish between what’s real and objective, what’s fake and what’s partisan. Although these online news articles are ex- pected to be written well-balanced without any prejudices, the authors/media houses may at times spill their standpoints and beliefs. Instead of pro- viding a holistic view to the readers, the author 1https://pan.webis.de/semeval19/semeval19- web/leaderboard.html tries to convey a picture with which he agrees and thus making the audience biased towards a party or a faction. When these news articles are extremely polarized towards one side of the argument, they are referred to as “Hyperpartisan News Articles”. This extreme polarization can leave users vul- nerable to detrimental arguments and cloud their judgment to make objective decisions. These hy- perpartisan articles may also carry some common elements of fake news. They are typically used to spread propaganda and manipulate readers. It tar- gets human psychology by creating confirmation bias and echo chambers and therefore impairing their ability to dispel the hyperpartisan articles in favor of neutral articles. SemEval-2019 Task 4 aims to solve this issue of hyperpartisanship. The objective of this task is to detect if a given news article has hyperpartisan arguments (extremely one-sided). In this paper, we have described our system that automates the process of identifying and annotating news articles as hyperpartisan or not. 2 Related Work The problem of …

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