Text mining has become a vital zone that has been attached to some examined ranges such as computational etymology, data mining, and information recovery (IR). Almost all people today use social networking activities in their daily interactions with no sorting. This can result in a range of inconsistencies, including lexical, semantic, linguistic, and syntactic ambiguities, making it difficult to determine the accurate data arrangement. Fittingly, the study identified the concept of text mining in terms of its impact on social networks. This study highlights the positive impact of intelligent techniques and how to use text mining to detect the news credibility on Facebook. The study introduced a background that highlighted the related aspects, the relation between these domains, and the news credibility. The study also presents the recent research in these fields with demonstrating the roles of these techniques for the required study target. The study could support as the foundation of future text mining studies on social networks data.

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.