Headline Diagnosis: Manipulation of Content Farm Headlines

• URL: http://arxiv.org/abs/2204.11408v1
• Date: Mon, 25 Apr 2022 02:55:33 GMT
• Title: Headline Diagnosis: Manipulation of Content Farm Headlines
• Authors: Yu-Chieh Chen (1), Pei-Yu Huang (2), Chun Lin (3), Yi-Ting Huang (3) and Meng Chang Chen (3) ((1) Hal{\i}c{\i}o\u{g}lu 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)
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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