The microblogging service Twitter provides vast amounts of user-generated language data. In this article I give an overview of related work on Twitter as an object of study. I also describe the anatomy of a Twitter message and discuss typical uses of the Twitter platform. The Twitter Application Programming Interface (API) will be introduced in a generic, non-technical way to provide a basic under-standing of existing opportunities but also limitations when working with Twitter data. I propose a basic classification system for existing tools that can be used for collecting and analyzing Twitter data and introduce some exemplary tools for each category. Then, I present a more comprehensive work-flow for conducting studies with Twitter data, which comprises the following steps: crawling, annotation, analysis and visualization. Finally, I illustrate the generic workflow by describing an exemplary study from the context of social TV research. At the end of the article, the main issues concerning tools and methods for the analysis of Twitter data are briefly addressed.