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Taylor and Francis Group, Journalism Practice, 2(10), p. 233-250, 2016

DOI: 10.1080/17512786.2015.1123109

Nature Research, Nature Neuroscience, 1(19), p. 48-54, 2015

DOI: 10.1038/nn.4182

Wiley, Policy and Internet, 2(7), p. 223-242, 2015

DOI: 10.1002/poi3.85

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Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci

Journal article published in 2015 by Eilis Hannon ORCID, Helen Spiers, Joana Viana, Ruth Pidsley, Joe Burrage, Therese M. Murphy, Claire Troakes, Gustavo Turecki, Michael C. O'Donovan ORCID, Leonard C. Schalkwyk, Nicholas J. Bray, Jonathan Mill
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby's murder to train and test a supervised machine learning text classifier that distinguishes between hateful and/or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.