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In this content analysis, we explored how students address instructor ideology in the university classroom through the social media platform Twitter. We employed Boolean search operators through Salesforce Marketing Cloud Radian6 software to gather tweets and identified English language tweets by how students referenced their instructor's ideology. Tweets originated in the United States (U.S.), the United Kingdom (U.K.), and Australia. Using qualitative coding procedures, we identified seven themes in the data that described how students wrote about instructor ideology on Twitter:venting regarding instructor's ideology,sharing classroom anecdote,affirming instructor's beliefs/actions,expressing grade concern,articulating an opposing view,reporting conflict, andindicating confusion. Across categories, Twitter was employed primarily as a back-stage means to communicate expressive dissent regarding instructor ideology and also to demonstrate students’ identity. We identified differences between categories based on the perceived ideology of the students’ instructors as well as differences in the number of tweets per capita originating in each country. [ABSTRACT FROM PUBLISHER]

This study analyzed tweets from handles associated with the Russian Internet Research Agency in an effort to better understand the tactics employed by that organization on the social media platform Twitter in their attempt to influence U.S. political discourse and the outcome of the 2016 U.S. Presidential election. We sampled tweets from the month preceding the election and analyzed to understand the qualitative nature of these tweets as well as quantitative differences between how types of IRA Twitter accounts communicated. Seven categories of tweet behavior were identified: attack left, support right, attack right, support left, attack media, attack civil institutions, and camouflage. While camouflage was the most common type of tweet (52.6%), descriptive analyses showed it was followed by attack left (12%) and support right (7%). A variety of quantitative differences were shown between how account types behaved. • We sampled Russian Internet Research Agency tweets from the month preceding the 2016 U.S. Presidential election. • We analyzed tweets to understand IRA messaging as well as differences between how types of IRA accounts communicated. • Seven distinct categories of behavior were identified and categories were employed differently by account type. • We discuss implications for what findings mean relative to prevailing and competing narratives surrounding IRA activity. • Findings suggest the IRA was both sowing division while also supporting candidate Trump. [ABSTRACT FROM AUTHOR]