Headline Diagnosis: Manipulation of Content Farm Headlines

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.