Covid-19 Hoax Detection Using KNN in Jaccard Space

Ema Utami, A. Iskandar, Wahyu Hidayat, Agung Budi Prasetyo, A. D. Hartanto
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引用次数: 0

Abstract

Social media has become a communication key to spark thinking, dialogue and action around social issues. Hoax is information that added or subtracted from the content of the actual news. The spread of unconfirmed Covid-19 news can cause public concern. The purpose of this research was to modify KNN with Jaccard Space in the classification of hoax news related to Covid-19. The data used from Jabar Saber Hoaks and Jala Hoaks. The classification results with KNN with Jaccard Space and stemming Nazief & Adriani get the highest accuracy than other models in this research. The accuracy of the KNN model on the Jaccard Space with stemming Nazief & Adriani and K = 5 was 75.89%, while for Naïve Bayes was 65.18%.
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在Jaccard空间中使用KNN检测新冠肺炎恶作剧
社交媒体已成为围绕社会问题引发思考、对话和行动的沟通关键。恶作剧是指在实际新闻内容中添加或减去的信息。未经证实的新冠肺炎消息的传播可能引起公众关注。本研究的目的是利用Jaccard Space对与新冠肺炎相关的恶作剧新闻进行分类,修改KNN。使用的数据来自Jabar Saber Hoaks和Jala Hoaks。与本研究中的其他模型相比,使用带有Jaccard Space的KNN和词干Nazief&Adriani的分类结果获得了最高的准确度。在Jaccard空间上,以Nazief和Adriani为词尾,K=5的KNN模型的准确率为75.89%,而对于Naïve Bayes,准确率为65.18%。
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审稿时长
12 weeks
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