使用机器学习技术识别COVID-19期间在线社交网络的宣传。

Akib Mohi Ud Din Khanday, Qamar Rayees Khan, Syed Tanzeel Rabani
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引用次数: 46

摘要

由于无法获得疫苗,COVID-19影响了整个世界。由于社交距离,在线社交网络在大流行时期被大量使用。信息在不知道来源真实性的情况下被大量共享。宣传是一种为了获得政治和宗教影响而故意分享的信息。它是一种系统的、深思熟虑的形成意见和影响一个人的思想的方式,以实现一个宣传者的预期意图。在2019冠状病毒病期间,人们正在分享关于这种致命病毒的各种宣传信息。我们使用twitter的应用程序接口(API)从twitter提取数据,Annotation是手动执行的。混合特征工程用于选择最相关的特征。推文的二进制分类是在机器学习算法的帮助下进行的。决策树算法的结果优于其他算法。为了获得更好的结果,可以改进特征工程,并将深度学习用于分类任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying propaganda from online social networks during COVID-19 using machine learning techniques.

COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task.

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