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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 pretrained word embeddings (GLoVe) as well as N-gram graphs (NGGs): a graph-based representation which proved to produce efficient, very lowdimensional 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.

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<p>Climate impact and adaptation measures are becoming urgent to be put in place and anticipated. During the past years, climate change effects have been producing adverse conditions in many parts of the world, with significant societal and financial impacts. Advanced analysis tools are needed to process ensembles of simulations of the future climate, in order to generate useful and tailored products for end users.</p><p>An example of a complex analysis tool used in climate research and adaptation studies is a tool to follow storm tracks. In the context of climate change, it is important to know how storm tracks will change in the future, in both their frequency and intensity. Storms can cause significant societal impacts, hence it is important to assess future patterns. Having access to this type of complex analysis tool is very useful, and integrating them with front-ends like the IS-ENES climate4impact (C4I) can enable the use of those tools by a larger number of researchers and end users.</p><p>Integrating this type of complex tool is not an easy task. It requires significant development effort, especially if one of the objectives is also to adhere to FAIR principles. The DARE Platform enables research developers to faster develop the implementations of scientific workflows more rapidly. This work presents how such a complex analysis tool has been implemented to be easily integrated with the C4I platform. The DARE Platform also provides easy access to e-infrastructure services like EUDAT B2DROP, to store intermediate or final results and powerful provenance-powered tools to help researchers manage their work and data.</p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements N°824084 and N°777413.</p>