Towards the Importance of the Type of Deep Neural Network and Employment of Pre-trained Word Vectors for Toxicity Detection: An Experimental Study

Authors

DOI:

https://doi.org/10.13052/jwe1540-9589.2082

Keywords:

word embedding, word vector, deep neural network, convolutional neural network, recurrent neural network, toxic comment detection

Abstract

As a natural consequence of offering many advantages to their users, social media platforms have become a part of daily lives. Recent studies emphasize the necessity of an automated way of detecting the offensive posts in social media since these ‘toxic’ posts have become pervasive. To this end, a novel toxic post detection approach based on Deep Neural Networks was proposed within this study. Given that several word embedding methods exist, we shed light on which word embedding method produces better results when employed with the five most common types of deep neural networks, namely,  blobid0.jpg, blobid1.jpg, blobid2.jpg, blobid3.jpg, and a combination of blobid4.jpg and blobid5.jpg. To this end, the word vectors for the given comments were obtained through four different methods, namely, (blobid6.jpg) blobid7.jpg, (blobid8.jpg) blobid9.jpg, (blobid10.jpg) blobid11.jpg, and (blobid12.jpg) the blobid13.jpg layer of deep neural networks. Eventually, a total of twenty benchmark models were proposed and both trained and evaluated on a gold standard dataset which consists of blobid14.jpg tweets. According to the experimental result, the best blobid15.jpg, blobid16.jpg, was obtained on the proposed blobid4.jpg model without employing pre-trained word vectors which outperformed the state-of-the-art works and implies the effective embedding ability of blobid4.jpgs. Other key findings obtained through the conducted experiments are that the models, that constructed word embeddings through the blobid13.jpg layers, obtained higher blobid15.jpgs and converged much faster than the models that utilized pre-trained word vectors.

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Author Biography

Abdullah Talha Kabakus, Department of Computer Engineering, Faculty of Engineering, Duzce University, Turkey

Abdullah Kabakus received the bachelor’s degree in computer engineering from Cankaya University in 2010, the master’s degree in computer engineering from Gazi University in 2014, and the philosophy of doctorate degree in Electrical-Electronics & Computer Engineering from Duzce University in 2017, respectively. He is currently working as an Associate Professor at the Department of Computer Engineering, Faculty of Engineering, Duzce University. His research areas include mobile security, deep learning, and social network analysis. He has been serving as a reviewer for many highly-respected journals.

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Published

2021-11-19

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Articles