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使用深度学习揭穿多语言社交媒体帖子

Bina Kotiyal¹, Heman Pathak¹, Nipur Singh¹

¹Department of Computer Science, Gurukula Kangri (Deemed to be University), Haridwar, Uttarakhand India.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

|June 26, 2023

概括

这项研究引入了深度学习方法 (DSMPD) 来检测英语和印度语社交媒体帖子中的假新闻. 该模型分析了情绪和实体等特征来分类新闻,旨在有效打击错误信息.

科学领域:

人工智能的人工智能
自然语言处理自然语言处理.
计算语言学计算语言学

背景情况:

社交媒体上的假新闻对公众论构成重大威胁.
检测多语言假新闻需要先进的计算方法.

研究的目的:

开发和评估一个深度学习模型,用于揭穿多语言社交媒体帖子.
创建一个强大的系统来识别和分类假新闻在英语和印度语.

主要方法:

网络抓取和自然语言处理 (NLP) 技术被用来构建英语和印度语社交媒体帖子的数据集.
通过使用ELMo嵌入,TF-IDF,情绪分析和命名实体识别 (NER) 等功能训练了一个深度学习模型.
在超过45,000篇文章的数据集上评估了分类器,比较了机器学习 (ML) 和深度学习 (DL) 算法.

主要成果:

建议使用深度学习 (DSMPD) 方法解读多语言社交媒体帖子的方法在分类新闻方面表现出有效性.
该模型将新闻分为五个层次:真实,可能真实,可能伪造,伪造或危险伪造.
在ML和DL算法之间的性能比较中,确定了用于假新闻检测的最佳模型.

结论:

深度学习模型为在多种语言中检测假新闻提供了一个有希望的解决方案.
DSMPD的方法为分析社交媒体内容提供了一个框架,以打击错误信息的传播.
进一步的研究可以完善这些模型,以提高准确性和更广泛的语言支持.

关键词:

深度学习 (DL) 是指深度学习.ELMo (语言模型的嵌入)假新闻是虚假的新闻.机器学习 (ML) 是指机器学习.错误的信息是错误的信息.自然语言处理 (NLP)后面的真相后面的真相情绪分析是一种情绪分析.

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