Automated accounts on Twitter known as Twitterbots are being used for malicious purposes. There is a particular concern in the use of Twitterbots in information warfare to inﬂuence politics or cause civil unrest. Research has been conducted into methods that could be used to identify Twitterbots with the aim of removing their presence from Twitter. The aim of this project is to evaluate the Twitterbot identiﬁcation methods that have been proposed and to propose a new framework for Twitterbot identiﬁcation. The techniques examined include human-based methods, supervised machine learning, activity correlation, monitoring user activity and clustering. In addition a botnet case study has been examined that shows that a targeted approach to Twitterbot identiﬁcation can be effective.