A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content

By Isuru Gunasekara, Isar Nejadgholi. 2018

43 citations recorded by the source corpus.

Language toxicity identification presents a gray area in the ethical debate surrounding freedom of speech and censorship. Today’s social media landscape is littered with unfiltered content that can be anywhere from slightly abusive to hate inducing. In response, we focused on training a multi-label classifier to detect both the type and level of toxicity in online content. This content is typically colloquial and conversational in style. Its classification therefore requires huge amounts of annotated data due to its variability and inconsistency. We compare standard methods of text classification in this task. A conventional one-vs-rest SVM classifier with character and word level frequency-based representation of text reaches 0.9763 ROC AUC score. We demonstrated that leveraging more advanced technologies such as word embeddings, recurrent neural networks, attention mechanism, stacking of classifiers and semi-supervised training can improve the ROC AUC score of classification to 0.9862. We suggest that in order to choose the right model one has to consider the accuracy of models as well as inference complexity based on the application.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (2023, methodology): “While earlier detectors commonly employed neural networks like CNNs, RNNs, and LSTMs (Georgakopoulos et al., 2018; van Aken et al., 2018; Gunasekara and Nejadgholi, 2018; Joulin et al., 2017; Kshirsagar et al., 2018; Mishra et al., 2018; Mitrovi´c et al., 2019; Sigurbergsson and Derczynski, 2020),…”
Towards Ethics by Design in Online Abusive Content Detection (2020, background): “However, the accuracy of lexicon and feature-based systems is often signiﬁcantly lower than the accuracy of deep learning models (Dixon et al., 2018; Gunasekara and Nejadgholi, 2018; Founta et al., 2019).”
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification (2019, methodology): “The automatic identification of hate speech has been mostly formulated as a natural language processing problem (e.g. Mishra et al., 2018; Gunasekara and Nejadgholi, 2018; Kshirsagar et al., 2018; Magu and Luo, 2018; Sahlgren et al., 2018).”
Developing an online hate classifier for multiple social media platforms (2020, background): “Gunasekara and Nejadgholi [77] note that “conventional machine learning classifiers such as linear regressions models have also been used to effectively detect abusive online language.”
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective (2020, unknown): “However, the accuracy of lexicon- and feature-based systems is often significantly lower than the accuracy of deep learning models (Dixon et al., 2018; Gunasekara & Nejadgholi, 2018; Founta, Chatzakou, Kourtellis, Blackburn, Vakali, & Leontiadis, 2019).”
Artificial intelligence in the creative industries: a review (2020, unknown): “2017; Gunasekara and Nejadgholi 2018).”