RESUMOO presente artigo busca realizar uma Revisão Sistemática com o intuito de verificar estudos na área de Reconhecimento e Padrões com Redes Sociais Científicas envolvendo estudos dos cientistas. O método de Revisão Sistemática foi escolhido devido a sua consolidação metodológica em realizar buscas de forma mais rigorosa em bases de dados. Como resultado, nove artigos foram encontrados, sendo que apenas dois deles apresentaram um trabalho relacionado com a questão central de pesquisa. Palavras-chave:Redes neurais científicas; Revisão sistemática; Reconhecimento de padrões. ABSTRACTThis article aims to conduct a Systematic Review to verify studies in the area of Pattern Recognition with Scientific Social Networks involving studies of scientists. The Systematic Review Method was chosen for its methodological consolidation in the more rigorous search in the databases. As a result, nine articles were found, and only two of them presented an article related to the central question of the research.

This article presents an investigative work, carrying out a Systematic Literature Review to find the quantity of studies that involve Pattern Recognition and Scientific Social Networks Online. The search was also expanded to find metrics with Pattern Recognition. The intention to find the quantitative arose due to the personal need to find references that involve this study to develop related scientific works. For this, 8 databases were used to carry out this research, which are: Library and Information Science Abstracts (LISA), Library, Information Science And Technology Abstracts (LISTA), Sociological Abstracts, SocINDEX, IEEE, Web Of Science, Scopus and SAGE Journals Online. After a series of data treatments, the results found indicate that the study is considered recent and that it lacks a research schedule for consolidation.

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Usually, scientific research begins with the collection of data in which online social media tools can be some of the most rewarding and informative resources. The extensive measure of accessible information pulls in users from undergraduate students to postdoc. The search for scientific themes has popularized due to the availability of abundant publications that resides in scientific social networks such as Mendeley, ResearchGate etc. Articles are published on these media inform of text for knowledge dissemination, scientific support, research, updates etc, and are frequently uploaded after its publication in a proceedings or journal. In this sense, data collected from database often contains high noise and its analysis can be treated as a characterization undertaking as it groups the introduction of a content into either good or bad. In this text, we present quantitative and qualitative analysis of papers popularity in Mendeley repository by using naive Bayes Classifier.