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As the internet is becoming part of our daily routine there is sudden growth and popularity of online news reading. This news can become a major issue to the public and government bodies (especially politically) if its fake hence authentication is necessary. It is essential to flag the fake news before it goes viral and misleads the society. In this paper, various Natural Language Processing techniques along with the number of classifiers are used to identify news content for its credibility. Further this technique can be used for various applications like plagiarism check , checking for criminal records.

Culture has been considered the primary motivator affecting consumers' decisions for years. Despite the importance of consumer culture, the systematic review of literature studying consumer culture is less and remains highly fragmented. This study uses a topic modelling approach to explore the role of culture in marketing literature to propose a way forward for future researchers interested in this domain. The study helps to synthesise cultural impact in marketing with a specific focus on dominant research topics and key themes, presenting clarity to the extant knowledge base. This study offers two potential contributions to the research community. First, this study demonstrates the application of structured topic modelling as a text analytics method that helps to report the evolution and thematic structure of this rapidly expanding domain. Second, the scientometric analysis in this study maps the conceptual structure of research with interconnected research themes hidden in this domain.