  Automated social media bots have existed almost as long as the social media
environments they inhabit. Their emergence has triggered numerous research
efforts to develop increasingly sophisticated means to detect these accounts.
These efforts have resulted in a cat and mouse cycle in which detection
algorithms evolve trying to keep up with ever evolving bots. As part of this
continued evolution, our research proposes a multi-model 'tool-box' approach in
order to conduct detection at various tiers of data granularity. To support
this toolbox approach this research also uses random string detection applied
to user names to filter twitter streams for bot accounts and use this as
labeled training data for follow on research.
