Comparative Analysis of Machine Learning and Deep Learning Algorithms for Detection of Online Hate Speech

Dhamija, Tashvik; Anjum,; Katarya, Rahul

doi:10.1007/978-981-16-0942-8_48

Cited by 2 publications

(1 citation statement)

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“…They utilized SVM as the classification algorithm to determine whether the data falls under the category of HS or not. Dhamija and Katarya [38] implemented roBERTa-based embedding and classified them by Decision Trees. Badri et al [39] trained a BiGRU classifier with a combination of FastText and Glove word embedding for hate and offensive language detection on the OLID dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Pars-HaO: Hate and Offensive Language Detection on Persian Tweets Using Machine Learning and Deep Learning

Karami Sheykhlan,

Abdoljabbar,

Karimpour

2023

Preprint

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<p>As social networks continue to gain widespread popularity, an urgent requirement arises to automatically identify and detect offensive language and hate speech. While there is a wealth of research and datasets available for English in this domain, there is currently a scarcity of research and datasets focused on identifying hate speech and offensive language in Persian text. This article introduces a 3-class dataset named Pars-HaO, consisting of 8013 tweets, to fill the gap in existing research. We collected the dataset by combining comments from pages that are more exposed to hate speech and using a keyword-based approach. Three annotators then labeled the tweets. In this study, we employed a combination of the Convolutional Neural Network (CNN) model and four widely recognized machine learning models, namely Support Vector Machine (SVM) and Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) as a baseline. Then, we compared the base models with Long Short-Term Memory(LSTM), Bidirectional LSTM (BiLSTM), and CNN models, each trained using the output of the last hidden state of Bidirectional Encoder Representations from Transformers (BERT). Experimental results on the Pars HaO dataset demonstrated that the BERT with BiLSTM technique yielded the best outcome, achieving a macro F1-score of 70%. <br> </p>

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Section: Literature Reviewmentioning

confidence: 99%