Scrutinising medical practitioners' Twitter feeds: an analysis. (30th March 2023)

• Record Type: Journal Article
• Authors: Jain, Arushi; Bhatnagar, Vishal; Dey, Nilanjan; Ashour, Amira S.; Shi, Fuqian
• Is Part Of: International journal of information technology and management. Volume 22:Number 1/2(2023)
• Page Start: 127
• Page End: 139
• Publication Date: 2023-03-30
• Subjects: big data -- Hadoop distributed file system -- HDFS -- MapReduce -- naïve Bayes multi-label classifier -- Tweets -- evaluation-based measure -- label-based measure; Management information systems -- Periodicals; Information technology -- Periodicals; Management -- Data processing -- Periodicals; 658.403805
• Languages: English
• ISSNs: 1461-4111

Abstract

Mining of social media data has found widespread applications in recent times. Twitter feeds and Facebook posts are being used to device product marketing strategies, sentiment analysis, financial predictions and forebode alarming situations. Twitter feeds analysis can be applied for analysing the behaviour and experiences of medical practitioners. Doctors' informal conversations on Twitter can provide deep insights about their work experiences, their concerns about the profession, their feelings - pathos or excitement they feel - and the affecting conditions. In the present work, Twitter feed of doctors along with the Twitter hashtags are used to collect data from tweets with hashtags such as #DoctorProblems. Afterward, data analysis was performed to determine the major problems faced by the members of the medical fraternity. These problems were categorised into five main categories. The multi-label na