摘要： •Various social media mining approaches were adopted for analyzing MOOC learning.•The daily, weekly, and monthly trends of MOOC-related tweets were investigated.•Sentiment analysis indicated mixed public opinion toward MOOCs.•The influencers of sentimental retweets about MOOCs were identified. Because many massive open online courses (MOOCs) have adopted social media tools for large student audiences to co-create knowledge and engage in collective learning processes, this study adopted various social media mining approaches to investigate Twitter messages related to MOOC learning. The first approach adopted in this study was calculating the important descriptive statistics of MOOC-related tweets and examining the daily, weekly, and monthly trends of MOOC that appeared on Twitter. This information can enable MOOC practitioners to observe participants’ temporal activities on social media and ascertain the most effective time to post or analyze tweets. Secondly, we investigated how public sentiment toward MOOC learning can be assessed according to related tweets. Because the availability and popularity of opinion-rich social networking services are increasing for MOOC communities, our findings from the sentiment analysis of Twitter data can afford substantial insights into participant perceptions of MOOC learning. Third, we analyzed the positive and negative retweets related to MOOCs and identified the influencers of these retweets. Social network diagrams were also developed to reveal how sentimental messages about MOOCs on Twitter were disseminated from the top influencers with the highest number of positive/negative retweets about MOOCs. Analyzing the relationships among top retweet users is vital to MOOC practitioners because they can use this information to filter or recommend MOOC-related messages to the influencers. In short, the findings pertaining social media mining in this study afford a holistic understanding of MOOC trends, public sentiment toward MOOC learning, and the influencers of MOOC-related retweets. 出版者： Elsevier Ltd 出版日期： 2015-10 出處： Computers in human behavior, 2015-10, Vol.51, p.568-577 資源來源： Elsevier ScienceDirect Journals Complete 版權： 2015 Elsevier Ltd 識別號： ISSN: 0747-5632 識別號： EISSN: 1873-7692 識別號： DOI: 10.1016/j.chb.2015.02.066
