Evaluation of Recurrent Neural Network architectures for abusive language detection in cyberbullying contexts
Date Issued
2020-05-08
Author(s)
Markoski, Filip
Ljubešić, Nikola
Abstract
Cyberbullying is a form of bullying that takes
place over digital devices. Social media is one of the most
common environments where it occurs. It can lead to serious
long-lasting trauma and can lead to problems with fear, anxiety,
sadness, mood, energy level, sleep, and appetite. Therefore,
detection and tagging of hateful or abusive comments can help
in the mitigation or prevention of the negative consequences of
cyberbullying. This paper evaluates seven different
architectures relying on Long Short-Term Memory (LSTM)
and Gated Recurrent Unit (GRU) gating units for classification
of comments. The evaluation is conducted on two abusive
language detection tasks, on a Wikipedia data set and a Twitter
data set, obtaining ROC-AUC scores of up to 0.98. The
architectures incorporate various neural network mechanisms
such as bi-directionality, regularization, convolutions, attention
etc. The paper presents results in multiple evaluation metrics
which may serve as baselines in future scientific endeavours. We
conclude that the difference is extremely negligible with the
GRU models marginally outperforming their LSTM
counterparts whilst taking less training time.
place over digital devices. Social media is one of the most
common environments where it occurs. It can lead to serious
long-lasting trauma and can lead to problems with fear, anxiety,
sadness, mood, energy level, sleep, and appetite. Therefore,
detection and tagging of hateful or abusive comments can help
in the mitigation or prevention of the negative consequences of
cyberbullying. This paper evaluates seven different
architectures relying on Long Short-Term Memory (LSTM)
and Gated Recurrent Unit (GRU) gating units for classification
of comments. The evaluation is conducted on two abusive
language detection tasks, on a Wikipedia data set and a Twitter
data set, obtaining ROC-AUC scores of up to 0.98. The
architectures incorporate various neural network mechanisms
such as bi-directionality, regularization, convolutions, attention
etc. The paper presents results in multiple evaluation metrics
which may serve as baselines in future scientific endeavours. We
conclude that the difference is extremely negligible with the
GRU models marginally outperforming their LSTM
counterparts whilst taking less training time.
Subjects
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