@misc{10481/57503,
year = {2019},
month = {8},
url = {http://hdl.handle.net/10481/57503},
abstract = {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.},
organization = {The 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.},
publisher = {IEEE},
keywords = {Misinformation},
keywords = {Data Science},
keywords = {US elections},
keywords = {Politics},
title = {Not All Lies Are Equal. A Study Into the Engineering of Political Misinformation in the 2016 US Presidential Election},
doi = {10.1109/ACCESS.2019.2938389},
author = {Oehmichen, Axel and Hua, Kevin and Díaz López, Julio Amador and Molina Solana, Miguel José and Gómez Romero, Juan and Guo, Yi-Ke},
}