Coding Twitter: Lessons from a content analysis of informal science

August 11, 2016

Twitter has become an important platform for textual communication and information sharing. With 500 million tweets sent daily, it offers an abundance of accessible and economical data on human interaction. But tweets exhibit specific characteristics of brevity, fluidity, and meaning embedded in a broader context, which pose serious challenges for the researcher engaged in content analyses.

In this paper, we would like to share our experience conducting a content analysis of Twitter data as part of the TwISLE project, a three-year study funded by the National Science Foundation to investigate informal science learning and engagement on Twitter. We will use de Beaugrande and Dressler's (1981) standards of textuality to highlight peculiarities of tweets as a form of text, explore the challenges these raised for our research process, and suggest solutions for rebuilding the context that is often elusive on Twitter. 

This paper was presented at the 2016 Annual Meeting of the American Educational Research Association by authors Noah Goodman and Daniel Light.


Daniel Light