{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T04:08:09Z","timestamp":1751342889740,"version":"3.41.0"},"reference-count":57,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>The rapid growth of technology has transformed education from conventional classroom teaching to learner centred, flexible learning anytime anywhere resulting in the popularity of Massive Open Online Courses (MOOCs). It is evident that many MOOCs have adopted social media tools to engage their learners through information sharing, participation, and collaboration to create and consume information. In addition to this, social media is the best place to understand crowd sentiments considering the massive amount of data being generated. This study through text mining of MOOC providers related Twitter data discovers the sentiments of users towards MOOCs, association rules, and the engagement of MOOC providers in Twitter. This article also analyses the traits of the influencers in seven MOOC providers and discovers that the influencers are popular users with higher numbers of followers count. The implications are discussed for MOOC industry, academic institutions, and the government.<\/jats:p>","DOI":"10.4018\/ijsds.2018010106","type":"journal-article","created":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T15:17:45Z","timestamp":1517498265000},"page":"79-101","source":"Crossref","is-referenced-by-count":0,"title":["Tweeting Continuing Education"],"prefix":"10.4018","volume":"9","author":[{"given":"Ritanjali","family":"Panigrahi","sequence":"first","affiliation":[{"name":"Indian Institute of Management, MDU Campus, Rohtak, India"}]},{"given":"Praveen Ranjan","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Indian Institute of Management, MDU Campus, Rohtak, India"}]}],"member":"2432","reference":[{"key":"IJSDS.2018010106-0","doi-asserted-by":"publisher","DOI":"10.1145\/170036.170072"},{"key":"IJSDS.2018010106-1","first-page":"487","article-title":"Fast algorithms for mining association rules.","author":"R.Agrawal","year":"1994","journal-title":"Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215,"},{"key":"IJSDS.2018010106-2","doi-asserted-by":"publisher","DOI":"10.1111\/jcal.12127"},{"key":"IJSDS.2018010106-3","doi-asserted-by":"publisher","DOI":"10.1109\/TLT.2014.2311807"},{"key":"IJSDS.2018010106-4","doi-asserted-by":"publisher","DOI":"10.1504\/IJIMA.2015.072886"},{"key":"IJSDS.2018010106-5","doi-asserted-by":"publisher","DOI":"10.1080\/08874417.2015.11645770"},{"key":"IJSDS.2018010106-6","doi-asserted-by":"publisher","DOI":"10.1002\/asi.22768"},{"issue":"2","key":"IJSDS.2018010106-7","article-title":"Study on the Classification of Negative Sentiment Weibo Messages in the Post-disaster Situation.","volume":"14","author":"H.Bai","year":"2016","journal-title":"Journal of Digital Information Management"},{"key":"IJSDS.2018010106-8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2015.11.010"},{"key":"IJSDS.2018010106-9","doi-asserted-by":"publisher","DOI":"10.19173\/irrodl.v12i3.902"},{"issue":"1","key":"IJSDS.2018010106-10","first-page":"75","article-title":"Tweeting educational technology: A tale of professional community of practice.","volume":"5","author":"I.Blau","year":"2012","journal-title":"International Journal of Cyber Society and Education"},{"key":"IJSDS.2018010106-11","unstructured":"Chaturvedi, R., & Ezeife, C. 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