Not All Lies Are Equal. A Study Into the Engineering of Political Misinformation in the 2016 US Presidential Election
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AuthorOehmichen, Axel; Hua, Kevin; Díaz López, Julio Amador; Molina Solana, Miguel José; Gómez Romero, Juan; Guo, Yi-Ke
MisinformationData ScienceUS electionsPolitics
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.
SponsorshipThe work of M. Molina-Solana was supported by the European Commission under Grant 743623. The work of J. Amador Díaz López was supported by the Imperial College Research Fellowship. The work of J. Gómez-Romero was supported by the Universidad de Granada under Grant P9-2014-ING and in part by the Spanish Ministry of Education, Culture and Sport under the José Castillejo Research Stays Programme.
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.