The DARE platform is designed to live in-between user applications and the underlying com- puting resources. It is built on top of containerisation as well as parallelisation technologies, e.g., Kubernetes and MPI. Interfacing with client systems and end-users is achieved via REST- ful APIs. The execution of scientific workflows is achieved via a Workflows-as-a-service layer, which can handle workflows described in either the dispel4py Python library (Filgueira, Krause, Atkinson, Klampanos, & Moreno, 2017), or in the Common Workflow Language (CWL) (Am- stutz et al., 2016).

The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Hate Speech content on microblogging platforms such as Twitter. Current EU and US legislation against hateful language, in conjunction with the large amount of data produced in these platforms has led to automatic tools being a necessary component of the Hate Speech detection task and pipeline. In this study, we examine the performance of several, diverse text representation techniques paired with multiple classification algorithms, on the automatic Hate Speech detection and abusive language discrimination task. We perform an experimental evaluation on binary and multiclass datasets, paired with significance testing. Our results show that simple hate-keyword frequency features (BoW) work best, followed by pre-trained word embeddings (GLoVe) as well as N-gram graphs (NGGs): a graph-based representation which proved to produce efficient, very low-dimensional but rich features for this task. A combination of these representations paired with Logistic Regression or 3-layer neural network classifiers achieved the best detection performance, in terms of micro and macro F-measure.