Headline Diagnosis: Manipulation of Content Farm Headlines
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

TL;DR
This paper presents a CNN-based method for classifying news headlines to detect manipulation and credibility, focusing on linguistic features like word segmentation, POS tags, and sentiment, achieving high accuracy.
Contribution
It introduces a novel CNN model that integrates multiple linguistic features for headline credibility classification, improving detection of manipulated content.
Findings
Achieved 93.99% accuracy in headline classification
Identified key linguistic features influencing credibility
Demonstrated effectiveness of CNN with integrated features
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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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Misinformation and Its Impacts · Spam and Phishing Detection
