Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election
Bibliographic Data
| ID | 4686290 |
|---|---|
| Authors | Raazesh Sainudiin (0000-0003-3265-5565, Uppsala University, corresponding author), Kumar Yogeeswaran (0000-0002-1978-5077, University of Canterbury), Kyle Nash (0000-0002-4628-1499, University of Alberta), Rania Sahioun (University of Canterbury) |
| Year | 2019 |
| Volume | 9 |
| Issue | 1 |
| Publication date | 2019-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-019-0567-9 |
| OpenAlex | W2963217293 |
| Language | EN |
| Citations received | 11 |
| References cited | 41 |
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
Benford's law · Biology · Ideology · Political science · Politics · Population · Presidential election · Presidential system · Sociology · Statistics · Computer Science · Demography · Electoral Systems and Political Participation · Hate Speech and Cyberbullying Detection · Law · Mathematics · Social Media and Politics
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| Unique citing works | 11 |
|---|---|
| Citations per year | 1,83 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 11 |