Convolutional Neural Networks for Toxic Comment Classification

By Spiros V. Georgakopoulos, Sotiris Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos. 2018

237 citations recorded by the source corpus.

Flood of information is produced in a daily basis through the global internet usage arising from the online interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since online texts with high toxicity can cause personal attacks, online harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several attempts to identify an efficient model for online toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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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),…”
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (2019, background): “tition (Georgakopoulos et al., 2018), including as additional training material for the aforementioned TRAC shared (Fortuna et al.”
Multilingual Sentiment Analysis and Toxicity Detection for Text Messages in Russian (2021, background): “In 2018, a dataset was presented containing 223549 comments in English, of which 159571 comments refer to the training set [10], [11].”
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2020, methodology): “The dataset was also used outside of the competition (Georgakopoulos et al., 2018), including as additional training material for the aforementioned TRAC shared task (Fortuna et al.”
A Dutch Dataset for Cross-lingual Multilabel Toxicity Detection (2021, background): “For a detailed overview of multi-label toxicity, look to van Aken et al., or Gunasekera et al. (Georgakopoulos et al., 2018; Wulczyn et al., 2017b).”
Towards Robust Toxic Content Classification (2019, methodology): “Since toxic content classification is a text classification task, traditional techniques ranging from bag of words models (Georgakopoulos et al. 2018) to CNNs (Georgakopoulos et al. 2018) and RNNs (van Aken et al. 2018; Gunasekara and Nejadgholi 2018) have all been applied.”
Crowdsourcing Subjective Tasks: The Case Study of Understanding Toxicity in Online Discussions (2019, background): “For example, [16] aims to discover toxic comments in a large pool of documents provided by a current Kag-gle competition regarding Wikipedia talk page edits.”