Effects of Social Media Richness on Topic Duration in Professional Learning Communities

• In Event: Learning Communities

Abstract

The main purpose of this study is to explore how social media posts and their media richness features effect the duration of a topic discussed in a professional learning community #edchat. First, 643340 tweets were examined according to the media richness features by conducting machine learning algorithm. Then, tweets were analyzed by text mining techniques for topic analysis. As a result, there were 275 topics. Tweets were nested by topics and linear regression was applied to see the significant effects. According to the results, number of tweets, informational richness, and contextual richness have significant effect on predicting the duration of the topic. Interactional richness, and number of retweets have no significant effect on the duration.

Authors

• Okan Arslan, Texas Tech University

• Wanli Xing, University of Florida

• Fethi A. Inan, Texas Tech University