Name
Title | ||
|---|---|---|
Not All Lies Are Equal. A Study Into the Engineering of Political Misinformation in the 2016 US Presidential Election |
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 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2938389 | IEEE ACCESS |
Keywords | DocType | Volume |
Misinformation,data science,US elections,politics | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Axel Oehmichen | 1 | 25 | 2.88 |
| Kevin Hua | 2 | 0 | 0.34 |
| Julio Amador Diaz Lopez | 3 | 0 | 0.34 |
| Miguel Molina-Solana | 4 | 48 | 12.80 |
| Juan Gomez-Romero | 5 | 0 | 0.34 |
| Yike Guo | 6 | 1319 | 165.32 |