Not All Lies Are Equal. A Study Into the Engineering of Political Misinformation in the 2016 US Presidential Election Oehmichen, Axel Hua, Kevin Díaz López, Julio Amador Molina Solana, Miguel José Gómez Romero, Juan Guo, Yi-Ke Misinformation Data Science US elections Politics 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 signi cant 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 ndings suggest that engineering of political misinformation seems to exploit human traits such as reciprocity and con rmation bias. We argue that investigating how misinformation is created is essential to understand human biases, diffusion and ultimately better produce public policy. 2019-10-24T11:32:30Z 2019-10-24T11:32:30Z 2019-08-29 info:eu-repo/semantics/article Oehmichen, A., Hua, K., López, J. A. D., Molina-Solana, M., Gómez-Romero, J., & Guo, Y. K. (2019). Not all lies are equal. A study into the engineering of political misinformation in the 2016 US presidential election. IEEE Access, 7, 126305-126314. http://hdl.handle.net/10481/57503 10.1109/ACCESS.2019.2938389 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España IEEE