Clickbait Detection for Internet News Title with Deep Learning Feed Forward

2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2022-11-03 DOI:10.1109/COMNETSAT56033.2022.9994567

B. Kindhi, Sean John Rawlings

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Abstract

Clickbait has been widely circulated on social media and has become one of the ways used to increase reader traffic and website/website visitors, but this clickbait is often misused by website managers in increasing visitor traffic to get an income or profit by ignoring the satisfaction of news readers with how to display a trapping title and hyperbole and the information in the content does not match what is stated in the news title. Today's society is in an emergency for clickbait news, even on national news pages sometimes they still use the title clickbait. In this study, a clickbait news prediction system is proposed on the news circulating. A deep learning neural network method has been proposed, and the architecture we use is flexible feed forward, namely by providing classes with semantic or multiple-meaning languages. Our proposed deep learning architecture on the neural network is able to classify clickbait news with accuracy values of 80%. The purpose of this research is to provide intelligent education to the public to be able to sort out news easily.

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基于深度学习前馈的网络新闻标题标题党检测

标题党已经在社交媒体上广泛流传，成为增加读者流量和网站/网站访问者的一种方式，但是这种标题党经常被网站管理者滥用，以增加访问者流量来获得收入或利润，忽视新闻读者对如何显示诱骗标题和夸张的满意，以及内容中的信息与新闻标题所表达的内容不匹配。今天的社会对标题党新闻来说是紧急的，即使在全国性的新闻页面上，有时他们仍然使用标题党。本研究提出了一种针对新闻传播的标题党新闻预测系统。提出了一种深度学习神经网络方法，采用灵活前馈的架构，即通过提供具有语义或多含义语言的类。我们在神经网络上提出的深度学习架构能够以80%的准确率对标题党新闻进行分类。本研究的目的是为公众提供智能教育，使他们能够轻松地整理新闻。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)

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