利用机器学习模型从印尼语在线媒体中识别恶作剧新闻

Inggrid Yanuar Risca Pratiwi
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引用次数: 0

摘要

信息通信技术的发展是当今信息爆炸的主要诱因之一。如今,各种新闻内容不仅很容易获得,而且很容易通过互联网上的各种平台制作,包括流行的网络媒体,如博客和网站。因此,目前在博客和网站上传播的许多新闻内容导致虚假新闻内容(恶作剧),可以误导读者的看法和想法。因此,开发一种能够检测假新闻内容存在的系统,将假新闻内容存在所造成的损失降到最低是非常重要的。在本研究中,提出了朴素贝叶斯算法作为机器学习模型,用于检测印尼语在线媒体中的假新闻内容。结果,全局准确率值达到71%,召回率、精度和F1-Score值总体上都在70%以上,表明所提出的模型可以很好地检测假新闻内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hoax news identification using machine learning model from online media in Bahasa Indonesia
Information and communication technology that’s developing is one of the main triggers of the information explosion today. Nowadays, various news content is not only easy to obtain but also easy to produce through various platforms on the internet, including popular online media, such as blogs and websites. So a lot of news content on blogs and websites that are currently being circulated leads to fake news content (hoaxes) that can mislead the perception and thoughts of the readers. Therefore, it is important to develop a system that can detect the presence of fake news content to minimize the losses caused by the presence of fake news content. In this study, the Naive Bayes algorithm is proposed as a machine learning model that will be used to detect fake news content in Indonesian language online media. As a result, the global accuracy value reached 71% with recall, precision, and F1-Score values as a whole above 70% which indicates that the proposed model can detect fake news content quite well.
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