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Sainudiin, Raazesh
Publications (4 of 4) Show all publications
Sainudiin, R., Yogeeswaran, K., Nash, K. & Sahioun, R. (2019). Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election. Social Network Analysis and Mining, 9(1), Article ID 34.
Open this publication in new window or tab >>Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election
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
Keywords
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:nbn:se:uu:diva-391380 (URN)10.1007/s13278-019-0567-9 (DOI)000476554600003 ()
Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2019-08-30Bibliographically approved
Sainudiin, R. & Veber, A. (2018). Full likelihood inference from the site frequency spectrum based on the optimal tree resolution. Theoretical Population Biology, 124, 1-15
Open this publication in new window or tab >>Full likelihood inference from the site frequency spectrum based on the optimal tree resolution
2018 (English)In: Theoretical Population Biology, ISSN 0040-5809, E-ISSN 1096-0325, Vol. 124, p. 1-15Article in journal (Refereed) Published
Abstract [en]

We develop a novel importance sampler to compute the full likelihood function of a demographic or structural scenario given the site frequency spectrum (SFS) at a locus free of intra-locus recombination. This sampler, instead of representing the hidden genealogy of a sample of individuals by a labelled binary tree, uses the minimal level of information about such a tree that is needed for the likelihood of the SFS and thus takes advantage of the huge reduction in the size of the state space that needs to be integrated. We assume that the population may have demographically changed and may be non-panmictically structured, as reflected by the branch lengths and the topology of the genealogical tree of the sample, respectively. We also assume that mutations conform to the infinitely-many-sites model. We achieve this by a controlled Markov process that generates 'particles' in the hidden space of SFS histories which are always compatible with the observed SFS. To produce the particles, we use Aldous' Beta-splitting model for a one parameter family of prior distributions over genealogical topologies or shapes (including that of the Kingman coalescent) and allow the branch lengths or epoch times to have a parametric family of priors specified by a model of demography (including exponential growth and bottleneck models). Assuming independence across unlinked loci, we can estimate the likelihood of a population scenario based on a large collection of independent SFS by an importance sampling scheme, using the (unconditional) distribution of the genealogies under this scenario when the latter is available. When it is not available, we instead compute the joint likelihood of the tree balance parameter beta assuming that the tree topology follows Aldous' Beta splitting model, and of the demographic scenario determining the distribution of the inter-coalescence times or epoch times in the genealogy of a sample, in order to at least distinguish different equivalence classes of population scenarios leading to different tree balances and epoch times. Simulation studies are conducted to demonstrate the capabilities of the approach with publicly available code.

Place, publisher, year, edition, pages
ACADEMIC PRESS INC ELSEVIER SCIENCE, 2018
Keywords
Importance sampler, Semi-parametric estimation, Optimal tree resolution, Controlled Markov process on hidden genealogical trees
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-373021 (URN)10.1016/j.tpb.2018.07.002 (DOI)000453111000001 ()30048667 (PubMedID)
Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Sainudiin, R., Yogeeswaran, K., Nash, K. & Sahioun, R. (2018). Rejecting the Null Hypothesis of Apathetic Retweeting of US Politicians and SPLC-defined Hate Groups in the 2016 US Presidential Election. In: Brandes, U Reddy, C Tagarelli, A (Ed.), 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): . Paper presented at IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),Barcelona, Spain, August 28-31, 2018 (pp. 250-253). IEEE
Open this publication in new window or tab >>Rejecting the Null Hypothesis of Apathetic Retweeting of US Politicians and SPLC-defined Hate Groups in the 2016 US Presidential Election
2018 (English)In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) / [ed] Brandes, U Reddy, C Tagarelli, A, IEEE, 2018, p. 250-253Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Donald Trump, Twitter, 2016 US Presidential election, US hate groups, configuration model, scalable generalized Fisher's exact test, Apache Spark
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-375825 (URN)10.1109/ASONAM.2018.8508555 (DOI)000455640600040 ()978-1-5386-6051-5 (ISBN)
Conference
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),Barcelona, Spain, August 28-31, 2018
Available from: 2019-02-04 Created: 2019-02-04 Last updated: 2019-02-04Bibliographically approved
Burghelea, T., Moyers-Gonzalez, M. & Sainudiin, R. (2017). A nonlinear dynamical system approach for the yielding behaviour of a viscoplastic material. Soft Matter, 13(10), 2024-2039
Open this publication in new window or tab >>A nonlinear dynamical system approach for the yielding behaviour of a viscoplastic material
2017 (English)In: Soft Matter, ISSN 1744-683X, E-ISSN 1744-6848, Vol. 13, no 10, p. 2024-2039Article in journal (Refereed) Published
Abstract [en]

A nonlinear dynamical system model that approximates a microscopic Gibbs field model for the yielding of a viscoplastic material subjected to varying external stresses recently reported in R. Sainudiin, M. Moyers-Gonzalez and T. Burghelea, Soft Matter, 2015, 11(27), 5531-5545 is presented. The predictions of the model are in fair agreement with microscopic simulations and are in very good agreement with the micro-structural semi-empirical model reported in A. M. V. Putz and T. I. Burghelea, Rheol. Acta, 2009, 48, 673-689. With only two internal parameters, the nonlinear dynamical system model captures several key features of the solid-fluid transition observed in experiments: the effect of the interactions between microscopic constituents on the yield point, the abruptness of solid-fluid transition and the emergence of a hysteresis of the micro-structural states upon increasing/decreasing external forces. The scaling behaviour of the magnitude of the hysteresis with the degree of the steadiness of the flow is consistent with previous experimental observations. Finally, the practical usefulness of the approach is demonstrated by fitting a rheological data set measured with an elasto-viscoplastic material.

National Category
Polymer Chemistry Physical Sciences Mathematics
Identifiers
urn:nbn:se:uu:diva-320653 (URN)10.1039/c6sm02361d (DOI)000396291300011 ()28198901 (PubMedID)
Available from: 2017-06-30 Created: 2017-06-30 Last updated: 2017-06-30Bibliographically approved
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