论文标题
标题诊断：内容农场头条的操纵
Headline Diagnosis: Manipulation of Content Farm Headlines
论文作者
论文摘要
随着技术的增长，新闻通过社交媒体传播。为了吸引更多的读者并获得更多的利润，一些新闻机构以更具吸引力的方式重现了大型新闻。因此，必须准确预测新闻文章是否来自官方新闻机构至关重要。这项工作开发了基于复杂的神经网络的标题分类，以确定新闻文章的信誉。该模型主要集中于研究头条新闻的关键因素。这些因素包括单词细分，言论部分和情感功能。随着将这些特征集成到建议的分类模型中，所示的评估可实现93.99％的精度。
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.
