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Rejecting the Null ...
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Sainudiin, RaazeshUppsala universitet,Tillämpad matematik och statistik
(författare)
Rejecting the Null Hypothesis of Apathetic Retweeting of US Politicians and SPLC-defined Hate Groups in the 2016 US Presidential Election
- Artikel/kapitelEngelska2018
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IEEE,2018
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LIBRIS-ID:oai:DiVA.org:uu-375825
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-375825URI
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https://doi.org/10.1109/ASONAM.2018.8508555DOI
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Språk:engelska
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Sammanfattning på:engelska
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We characterize the Twitter networks of both major presidential candidates, Donald Trump and Hillary Clinton, with various American hate groups defined by the US Southern Poverty Law Center (SPLC). We further examined the Twitter networks for Bernie Sanders, Ted Cruz, and Paul Ryan, for 9 weeks around the 2016 election (4 weeks prior to the election and 4 weeks post-election). By carefully accounting for the observed heterogeneity in the Twitter activity levels across individuals under the null hypothesis of apathetic retweeting that is formalized as a random network model based on the directed, multi-edged, self-looped, configuration model, our data revealed via a generalized Fisher's exact test that there were significantly many Twitter accounts linked to SPLC-defined hate groups belonging to seven ideologies (Anti-Government, Anti-Immigrant, Anti-LGBT, Anti-Muslim, Alt-Right, Neo-Nazi, and White-Nationalist) and also to @realDonaldTrump relative to the accounts of the other four politicians. The exact hypothesis test uses Apache Spark's distributed sort and join algorithms to produce independent samples in a fully scalable way from the null model.
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Yogeeswaran, KumarUniv Canterbury, Dept Psychol, Christchurch, New Zealand
(författare)
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Nash, KyleUniv Alberta, Dept Psychol, Edmonton, AB, Canada
(författare)
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Sahioun, RaniaUniv Canterbury, Dept Psychol, Christchurch, New Zealand
(författare)
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Uppsala universitetTillämpad matematik och statistik
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): IEEE, s. 250-2539781538660515
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