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Supervised Machine Learning Applications for Detecting Internet Research Agency Misinformation

Authors: Thomas Wiese; Jessica Wiese;

Supervised Machine Learning Applications for Detecting Internet Research Agency Misinformation

Abstract

Misinformation has shifted political narratives across the globe. Because information shared over social media platforms lack traditional publishers and editors, the public is more susceptible to consuming information that is untrue. During the 2016 U.S. presidential election, the Russian government sponsored information operatives to spread misleading and/or false claims through social media. This study defines a method for automated detection of misinformation on social media using machine learning.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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