A Survey for News Credibility in Social Networks

Farah Yasser, Sayed Abdelgaber Abdelmawgoud, A. Idrees
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Abstract

Text mining has been a vital area that has been linked to some fields of research such as machine learning, data analysis and gathering, and information recovery. To extract knowledge and information, Natural Language Processing (NLP) was used alternative techniques. Text mining analyses unstructured data to provide critical data and information plans in a timely manner. Nowadays everyone uses online communication activities to keep in touch with others in their daily life. As a result, they're a great way to connect. Not sorting in a paragraph in a format suitable for word recognition has become a point of contention. intensity can cause a variety of inconsistencies, such as lexical, semantic, linguistic, and syntactic ambiguities, determining the proper data arrangement. Information and data are required for learning things and reaching knowledge. This paper covered how to use text mining to determine the credibility of news on social media. The findings of this study could be used as the basis for future text mining research.
社交网络中的新闻可信度调查
文本挖掘一直是一个重要的领域,它与机器学习、数据分析和收集以及信息恢复等一些研究领域联系在一起。为了提取知识和信息,采用了自然语言处理(NLP)替代技术。文本挖掘对非结构化数据进行分析,及时提供关键数据和信息计划。如今,每个人在日常生活中都使用在线交流活动与他人保持联系。因此,它们是一种很好的联系方式。不以适合单词识别的格式对段落进行排序已成为争论的焦点。强度会导致各种不一致,例如词汇、语义、语言和句法上的歧义,从而决定正确的数据安排。学习事物和获取知识需要信息和数据。本文介绍了如何使用文本挖掘来确定社交媒体上新闻的可信度。本研究结果可作为未来文本挖掘研究的基础。
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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