Serra, J; Leontiadis, I; Spathis, D; Blackburn, J; Stringhini, G; Vakali, A; (2017) Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words. In: Nivre, J and Bhattacharyya, P and Hearst, M and Zhou, M, (eds.) Proceedings of the annual meeting of the Association of Computational Linguistics (ACL) 2017 - ALW1: 1st Workshop on Abusive Language Online. : Vancouver, Canada.

Preview

Text hate-ALW2017.pdf - Accepted Version Download (217kB) | Preview

Abstract

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%

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item