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Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or nonblacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the ability to describe seen documents to the ability to predict unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4–11%

The digital ICT revolution is profoundly changing the way knowledge is created, communicated and is being deployed. New research methods based on computing and "big data" enable new means and forms for scientific collaboration also through policy measures supporting open access to data and research results. The exponential growth of digital resources and services is supported by the deployment of e-Infrastructure, which allows researchers to access remote facilities, run complex simulations or to manage and exchange unprecedented amounts of digital data.