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Mirna Adriani
Information Retrieval Lab. Faculty of Computer Science Universitas Indonesia
Computer Science & IT

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Detecting Controversial Articles on Citizen Journalism Alfan Farizki Wicaksono; Sharon Raissa Herdiyana; Mirna Adriani
Jurnal Ilmu Komputer dan Informasi Vol 11, No 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.521 KB) | DOI: 10.21609/jiki.v11i1.478

Abstract

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.
Co-Authors Alfan Farizki Wicaksono Sharon Raissa Herdiyana