Headline Diagnosis: Manipulation of Content Farm Headlines
This addresses the issue of misleading news for social media users, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of identifying content farm headlines by developing a CNN-based classification model that uses word segmentation, POS tags, and sentiment features, achieving 93.99% accuracy.
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.