Research and Twitter Text Mining for a Systematic Literature Review

PROJECT TITLE :

Twitter and Research A Systematic Literature Review Through Text Mining

ABSTRACT:

Researchers have gathered Twitter data to investigate a variety of subjects. This growing body of knowledge, however, has yet to be comprehensively examined in order to consolidate Twitter-related papers. Traditional methods of manually selecting and analyzing samples of topically related papers have constrained the scope of existing literature review studies. The objectives of this retrospective study are to identify the most popular Twitter-based research topics, summarize the temporal trend of topics, and interpret the evolution of topics over the last ten years. This work uses an efficient and effective approach to systematically mine a large number of Twitter-based studies in order to characterize the relevant literature. This study gathered relevant papers from three databases and used text mining and trend analysis to uncover semantic trends and track the evolution of research subjects over the course of a decade. In more than 18,000 manuscripts published between 2006 and 2019, we discovered 38 topics. This study discovered that although 23.7 percent of topics showed no significant trend (P = 0.05), 21% of topics had increasing trends, and 55.3 percent of topics had decreasing trends. These hot and cold topics are divided into three categories: application, methodology, and technology. Researchers, educators, and publishers will benefit from the contributions of this study, which can be used in the developing field of Twitter-based research.

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