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Headline Diagnosis: Manipulation of Content Farm Headlines

2022-04-25 02:55:33
Yu-Chieh Chen (1), Pei-Yu Huang (2), Chun Lin (3), Yi-Ting Huang (3), Meng Chang Chen (3) ((1) Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, United States, (2) Management and Digital Innovation, University of London, Singapore, (3) Institute of Information Science, Academia Sinica, Taipei, Taiwan)
arXiv_CL
           
Abstract

As technology grows faster, the news spreads through social media. In order to attract more readers and acquire additional profit, some news agencies reproduce massive news in a more appealing manner. Therefore, it is essential to accurately predict whether a news article is from official news agencies. This work develops a headline classification based on Convoluted Neural Network to determine credibility of a news article. The model primarily focuses on investigating key factors from headlines. These factors include word segmentation, part-of-speech tags, and sentiment features. With integrating these features into the proposed classification model, the demonstrated evaluation achieves 93.99% for accuracy.

Abstract (translated)

URL

https://arxiv.org/abs/2204.11408

PDF

https://arxiv.org/pdf/2204.11408.pdf

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