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Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.ORCID iD: 0000-0003-3265-5565
Univ Canterbury, Dept Psychol, Christchurch, New Zealand.
Univ Alberta, Dept Psychol, Edmonton, AB, Canada.
Univ Canterbury, Dept Psychol, Christchurch, New Zealand.
2019 (English)In: Social Network Analysis and Mining, ISSN 1869-5450, E-ISSN 1869-5469, Vol. 9, no 1, article id 34Article in journal (Refereed) Published
Abstract [en]

We characterize the Twitter networks of the major presidential candidates, Donald J. Trump and Hillary R. 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). We carefully account for the observed heterogeneity in the Twitter activity levels across individuals through 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, White-Nationalist and Neo-Nazi) 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. Additionally, by exploring the empirical Twitter network we found that significantly more individuals had the fewest retweet degrees of separation simultaneously from Trump and each one of these seven hateful ideologies relative to the other four politicians. We conduct this exploration via a geometric model of the observed retweet network, distributed vertex programs in Spark's GraphX library and a visual summary through neighbor-joined population retweet ideological trees. Remarkably, less than 5% of individuals had three or fewer retweet degrees of separation simultaneously from Trump and one of several hateful ideologies relative to the other four politicians. Taken together, these findings suggest that Trump may have indeed possessed unique appeal to individuals drawn to hateful ideologies; however, such individuals constituted a small fraction of the sampled population.

Place, publisher, year, edition, pages
SPRINGER WIEN , 2019. Vol. 9, no 1, article id 34
Keywords [en]
Donald Trump, Twitter, 2016 US presidential election, US hate groups, Configuration model, Scalable generalized Fisher's exact test, Apache Spark, Directed degrees of separation, Empirical geometric retweet model, Population retweet ideological trees
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-391380DOI: 10.1007/s13278-019-0567-9ISI: 000476554600003OAI: oai:DiVA.org:uu-391380DiVA, id: diva2:1347182
Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2019-08-30Bibliographically approved

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