In the 2016 U.S. Presidential election, some candidates used to automated accounts, or bots, to boost their social media presence and followership. Categorizing all automated accounts as “bots” obfuscates the role different types of bots play in the spread of political information in election campaigns. Exploring strategies for automated information diffusion helps scholars understand and model online political behavior. This paper presents an initial effort aimed at understanding the disparate roles of bots in diffusion of political messages on Twitter. Having collected over 300 million tweets from candidates and the public from the U.S. presidential election, we use three OLS regression models to explore the strategic advantages of different types of automated accounts. We approach this by analyzing retweet events, testing a series of hypotheses regarding bots’ influence on the size of retweet events, and the change in candidates’ followers. Next, we develop an estimator to analyze the spread of information across the networks, demonstrating that, while ‘benevolent bots’ serve as overt information aggregators and have an effect on information diffusion, “nefarious bots” act as false amplifiers, covertly mimicking the spread of online information with no effect on diffusion. Making this important distinction allows us to disambiguate the concept of “bots” and reach a more nuanced and detailed understanding of the role of automated accounts in information diffusion in political campaigning online.