Headline Diagnosis: Manipulation of Content Farm Headlines
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.
Keywords
Cite
@article{arxiv.2204.11408,
title = {Headline Diagnosis: Manipulation of Content Farm Headlines},
author = {Yu-Chieh Chen and Pei-Yu Huang and Chun Lin and Yi-Ting Huang and Meng Chang Chen},
journal= {arXiv preprint arXiv:2204.11408},
year = {2022}
}
Comments
Accepted by The 26th Taiwan Academic Network Conference (TANET) 2020