Automatic extraction and identification of users' responses in Facebook medical quizzes. Issue 127 (April 2016) - Rodríguez-González, Alejandro; Menasalvas Ruiz, Ernestina; Mayer Pujadas, Miguel A.
Highlights: New England Journal of Medicine (NEJM) is a very prestigious medical journal. NEJM Facebook page currently has more than 1.25 million of users. Medical quizzes are one of the methods to test the knowledge of future physicians. Our approach allows extracting medical quizzes published in NEJM Facebook page. This is the first study done about the content of medical quizzes in social networks. Abstract: Background: In the last few years the use of social media in medicine has grown exponentially, providing a new area of research based on the analysis and use of Web 2.0 capabilities. In addition, the use of social media in medical education is a subject of particular interest which has been addressed in several studies. One example of this application is the medical quizzes of The New England Journal of Medicine (NEJM) that regularly publishes a set of questions through their Facebook timeline. Objective: We present an approach for the automatic extraction of medical quizzes and their associated answers on a Facebook platform by means of a set of computer-based methods and algorithms. Methods: We have developed a tool for the extraction and analysis of medical quizzes stored on Facebook timeline at the NEJM Facebook page, based on a set of computer-based methods and algorithms using Java. The system is divided into two main modules: Crawler and Data retrieval. Results: The system was launched on December 31, 2014 and crawled through a total of 3004 valid posts and 200, 081 valid comments. The first post was dated on July 23, 2009 and the last one on December 30, 2014. 285 quizzes were analyzed with 32, 780 different users providing answers to the aforementioned quizzes. Of the 285 quizzes, patterns were found in 261 (91.58%). From these 261 quizzes where trends were found, we saw that users follow trends of incorrect answers in 13 quizzes and trends of correct answers in 248. Conclusions: This tool is capable of automatically identifying the correct and wrong answers to a quiz provided on Facebook posts in a text format to a quiz, with a small rate of false negative cases and this approach could be applicable to the extraction and analysis of other sources after including some adaptations of the information on the Internet.