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Civic engagement in the United States has increased since the election of President Trump. This increase is evident online as people are using Twitter to assert their digital citizenship by voicing their opinions regarding President Donald J. Trump and demonstrating solidarity with various civic movements. President Trump's election has caused many people to recognize how policies impact their daily lives and shed previous understandings as described by Freire (2005) as concientization. This study employed a Content Analysis to classify Tweets from #DisruptJ20 posted during inauguration week according to concientization and Support or Opposition of President Trump. A Sentiment Analysis revealed that supporters of President Trump were much more negative than those who oppose President Trump. Results of the Logistic Regression found that variables related to network structure (Friends, Followers, and Likes) were more likely to predict Retweets than concientization. Results of Hierarchical Linear Modeling indicate the average level of concientization was positively related to being Retweeted. Implications include recognizing that digital citizens value content that illustrates how matters of state are impacting their lives. As concientization increases in America, the more likely it is for people with opposing viewpoints to understand one another and work for mutually beneficial social change. [ABSTRACT FROM AUTHOR]

This article examines research methods for analyzing social construction of knowledge in online discussion forums. We begin with an examination of the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997) and its applicability to analyzing social construction of knowledge. Next, employing a dataset from an online discussion, we demonstrate how interaction analysis can be supplemented by employing other research techniques such as learning analytics and social network analysis that shed light on the social dynamics that support knowledge construction. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ("big data") in order to discover the factors that contribute to learning (Long & Siemens, 2011, p. 34). Social network analysis characterizes the information infrastructure that supports the construction of knowledge in social contexts (Scott, 2012). By combining interaction analysis with learning analytics and social network analysis, we were able to conceptualize the process by which knowledge construction takes place in online platforms. [ABSTRACT FROM AUTHOR]