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arXiv · 2204.11408

Headline Diagnosis: Manipulation of Content Farm Headlines

Yu-Chieh Chen·Pei-Yu Huang·Chun Lin·Yi-Ting Huang·Meng Chang Chen
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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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Yu-Chieh Chen, Pei-Yu Huang, Chun Lin, Yi-Ting Huang, Meng Chang Chen. 2022-04-25. Headline Diagnosis: Manipulation of Content Farm Headlines. https://arxiv.org/abs/2204.11408

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