Public school teachers in the United States are often constrained in terms of their ability to express their moral views on issues that a ect their schools, classrooms, students, and teaching practices, but are able to express their ideas, concerns, and frustrations as private citizens using social media. Previously we developed the Tweet Capture and Clustering System (TCCS) in order to explore how teachers use Twitter, looking at word usage among a group of teacher tweeters, and attempting to nd clusters of teachers who have similar patterns of word usage in their tweets. In the work reported here, we look at teacher tweeters across the 12 months of 2016, seeking to understand how the clusters and the words used in these clusters vary from month to month. In this initial look at the dynamics of the system, we see some evidence of word usage changing across the 12-month period. This initial work sug- gests that extending TCCS to have temporal topic tracing as a core capability will be a meaningful addition to of the system.

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Submitted, accepted, presented, and published at 3rd International Workshop on Social Media World Sensors (SIDEWAYS 2017) in conjunction with The 28th ACM Conference on Hypertext and Social Media (ACM Hypertext 2017) held in Prague, Czech Republic at Charles University from July 4-7, 2017.

This research and papers are part of the #TeacherTweets project.


Stephen Houser, Academic Consulting and Technology, Bowdoin College, Brunswick, Maine

Doris Santoro, Department of Education, Bowdoin College, Brunswick, Maine

Clare Bates Congdon, Department of Computer Science, Bowdoin College, Brunswick, Maine

Jessica Hochman, School of Information Pratt Institute, New York, NY


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