A Comparative Approach For Clickbait Detection Using Deep Learning

M. A. Shaikh, Sneha Annappanavar
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引用次数: 2

Abstract

The use of Online Clickbait in different social media platforms have increased momentarily. Basically, click baits are the eye-catching titles or headlines which exaggerate the facts and make the user to “click” on it. These clickbaits comes in many forms like images, videos also through advertisements. This links will lead you to anonymous websites which contains very little information and create nuisance on the internet. In social media clickbaits are very commonly used and Detection of Clickbait is a very crucial process. This paper proposes a method using a deep learning algorithm namely Convolution Neural Network (CNN) for detecting the clickbaits on the social media platforms. The used method focuses on the textual features which consider the word sequence information and also learns the word meanings from entire dataset. Our Results obtained a high accuracy of 0.82% comparatively better than different Machine Learning algorithms. We also did comparative analysis with the classification algorithm called Random Forest (RF).
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使用深度学习的标题党检测比较方法
在不同的社交媒体平台上,在线标题党(Online Clickbait)的使用暂时有所增加。基本上,点击诱饵是那些引人注目的标题或标题,夸大事实,让用户“点击”它。这些点击诱饵有很多形式,比如图片、视频,也有广告。这些链接会把你带到包含很少信息的匿名网站,并在互联网上制造麻烦。在社交媒体中,点击诱饵是非常常用的,检测点击诱饵是一个非常关键的过程。本文提出了一种利用深度学习算法卷积神经网络(CNN)检测社交媒体平台点击诱饵的方法。该方法关注文本特征,既考虑了词序列信息,又从整个数据集中学习词义。我们的结果获得了0.82%的高准确率,相对优于其他机器学习算法。我们还与随机森林(Random Forest, RF)分类算法进行了比较分析。
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