We investigated whether and how political misinformation is engineered using a dataset
of four months worth of tweets related to the 2016 presidential election in the United States. The data
contained tweets that achieved a significant level of exposure and was manually labelled into misinformation
and regular information. We found that misinformation was produced by accounts that exhibit different
characteristics and behaviour from regular accounts. Moreover, the content of misinformation is more novel,
polarised and appears to change through coordination. Our findings suggest that engineering of political
misinformation seems to exploit human traits such as reciprocity and confirmation bias. We argue that
investigating how misinformation is created is essential to understand human biases, diffusion and ultimately
better produce public policy.