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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.

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