Unveiling Social Media Comments with a Novel Named Entity Recognition System for Identity Groups
Andr\'es Carvallo, Tamara Quiroga, Carlos Aspillaga, Marcelo, Mendoza

TL;DR
This paper introduces a novel NER system tailored for identifying and tagging identity groups in social media comments, enhancing hate speech detection by recognizing specific group mentions with high accuracy.
Contribution
The study develops a specialized NER model for identity groups, extending traditional hate speech detection methods to improve detection and tagging of targeted attack entities.
Findings
Model achieves an average F1-score of 0.75 in identifying groups.
Outperforms in ethnicity attack span detection with an F1-score of 0.80.
Shows strong generalization to minority classes like sexual orientation and gender.
Abstract
While civilized users employ social media to stay informed and discuss daily occurrences, haters perceive these platforms as fertile ground for attacking groups and individuals. The prevailing approach to counter this phenomenon involves detecting such attacks by identifying toxic language. Effective platform measures aim to report haters and block their network access. In this context, employing hate speech detection methods aids in identifying these attacks amidst vast volumes of text, which are impossible for humans to analyze manually. In our study, we expand upon the usual hate speech detection methods, typically based on text classifiers, to develop a Named Entity Recognition (NER) System for Identity Groups. To achieve this, we created a dataset that allows extending a conventional NER to recognize identity groups. Consequently, our tool not only detects whether a sentence…
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Taxonomy
TopicsTopic Modeling · Expert finding and Q&A systems · Advanced Text Analysis Techniques
