Roman Urdu Multi-Class Offensive Text Detection using Hybrid Features and SVM

Sajid, Tauqeer; Hassan, Mehdi; Ali, Mohsan; Gillani, Rabia

doi:10.1109/inmic50486.2020.9318069

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…From more complicated neural networks like CNNs and RNNs to more conventional techniques like Naive Bayes (NB) and SVM, the ML models utilized for HSD range in complexity (Sajid et al, 2020; Watanabe et al, 2018). The architectural representations of both CNNs and RNNs are depicted in Figures 5 and 6, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Numerous research investigations have meticulously delved into the realm of identifying hate speech, with a notable emphasis on discerning hate speech within political contexts, as is evident from the works of (Oriola & Kotze, 2020; Ribeiro et al, 2018; Wang et al, 2022). Additionally, the academic discourse extends to the scrutiny of hate speech intertwined with religious themes, as underscored by (Al‐Hassan & Al‐Dossari, 2022; Ali et al, 2021; Ghosh et al, 2023; Mozafari et al, 2022; Qureshi & Sabih, 2021; Sajid et al, 2020). Visual representation of the distribution of these topics is offered in Figure 12 and Table 9, which convey the number of instances within each thematic domain.…”

Section: Discussionmentioning

confidence: 99%

“…The scope of research investigations further encompasses expressions of antagonism targeting marginalized groups, notably immigrants, as documented by the works of Irani et al (2021) MacAvaney et al (2019), Qureshi and Sabih (2021), and Zhou et al (2020), along with discussions relating to women, as outlined by Sharma and Shrivastava (2018), Zhou et al (2020), and Irani et al (2021). Hate speech founded on considerations of race and ethnicity also comes under the purview of these studies, evident in the research of MacAvaney et al (2019), Watanabe et al (2018), Sajid et al (2020), Sharma and Shrivastava (2018), Zhang and Luo (2018), Pronoza et al (2021), and Baydogan and Alatas (2021) and those addressing hate speech within the framework of the COVID‐19 outbreak, such as Khanday et al (2022) and Ahmed and Lin (2022).…”

Section: Discussionmentioning

confidence: 99%

“…Race (Mathew et al, n.d.;Alatawi et al, 2021;Chiu et al, 2021;MacAvaney et al, 2019;Sajid et al, 2020;Shannaq et al, 2022;Sharma & Shrivastava, 2018;Zhang & Luo, 2018) Religion (Sajid et al, 2020;Zhang & Luo, 2018), (Mathew et al, n.d.;Ali et al, 2021;Mozafari et al, 2022;Qureshi & Sabih, 2021) Ethnicity (Ali et al, 2021;Ghosh et al, 2023;Mozafari et al, 2022;Pronoza et al, 2021;Watanabe et al, 2018) Sexist (Al-Hassan & Al-Dossari, 2022;Baydogan & Alatas, 2021;Chiu et al, 2021;MacAvaney et al, 2019;Mozafari et al, 2022;Qureshi & Sabih, 2021;Shannaq et al, 2022) Political (Govers et al, 2023;Oriola & Kotze, 2020;Plaza-Del-Arco et al, 2021;Ribeiro et al, 2017;Wang et al, 2022) Women and immigrants (Irani et al, 2021;MacAvaney et al, 2019;Zhou et al, 2020) Covid-19 (Ahmed & Lin, 2022;Khanday et al, 2022;Su et al, 2023) Gender (Ghosh et al, 2023) Crime (Mathew et al, n.d.) and those addressing hate speech within the framework of the COVID-19 outbreak, such as Khanday et al (2022) and…”

Section: Target Studymentioning

confidence: 99%

“…Traditional machine learning SVM (Ali et al, 2021;Balouchzahi et al, 2021;Oriola & Kotze, 2020;Saeed Tawfik, 2020;Sajid et al, 2020;Sharma & Shrivastava, 2018;Vogel & Meghana, 2021;Watanabe et al, 2018) NB (Priyadarshini et al, 2023;Saeed Tawfik, 2020;Sajid et al, 2020;Sharma & Shrivastava, 2018) RF (MacAvaney et al, 2019;Oriola & Kotze, 2020;Saeed Tawfik, 2020;Sharma & Shrivastava, 2018) LR (Balouchzahi et al, 2021;Oriola & Kotze, 2020;Saeed Tawfik, 2020;Sajid et al, 2020) GB (Oriola & Kotze, 2020;Sajid et al, 2020) DT (Priyadarshini et al, 2023) NLP (Das et al, 2021;Saeed Tawfik, 2020;Sharma & Shrivastava, 2018;Vogel & Meghana, 2021;Watanabe et al, 2018) K-means (Ali et al, 2021) Word2vec (Balouchzahi et al, 2021;Priyadarshini et al, 2023) GloVe (Priyadarshini et al, 2023;Rodriguez et al, 2022) Deep learning CNN (Bilal et al, 2022;Das et al, 2021;Ghosh et al, 2023;Zhang & Luo, 2018;Zhou et al, 2020) LSTM (Bilal et al, 2022) BERT (Alzahrani & Jololian, 2021;…”

Section: Category Technique Studymentioning

confidence: 99%

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Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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The realm of Natural Language Processing and Text Mining has seen a surge in interest from researchers in hate speech detection, leading to an increase in related studies. This analysis aims to create a valuable resource by summarizing the methods and strategies used to combat hate speech in social media. We perform a detailed review to achieve a deep knowledge of the hate speech detection landscape from 2018 to 2023, revealing global incidents of hate speech in 2022–2023. Sixty‐six relevant articles were selected for this review. Existing studies were analyzed and categorized into five method categories: Machine Learning, Deep Learning, Ensemble models, Graph Neural Networks, and Graph Convolutional Networks. These advancements can aid social networking services in identifying hate messages before being posted, reducing the risk of harassment. The review also covers available hate speech datasets and highlights research challenges, but it is clear that a definitive solution to this problem is yet to be found. Future research directions are recommended to address the ongoing challenges in Hate Speech Detection.This article is categorized under: Applications of Computational Statistics > Computational Linguistics Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART) Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Target Studymentioning

confidence: 99%

Section: Category Technique Studymentioning

confidence: 99%

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Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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Passion-Net: a robust precise and explainable predictor for hate speech detection in Roman Urdu text

Mehmood,

Ghafoor,

Asim

et al. 2023

Neural Comput & Applic

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With an aim to eliminate or reduce the spread of hate content across social media platforms, the development of artificial intelligence supported computational predictors is an active area of research. However, diversity of languages hinders development of generic predictors that can precisely identify hate content. Several language-specific hate speech detection predictors have been developed for most common languages including English, Chinese and German. Specifically, for Urdu language a few predictors have been developed and these predictors lack in predictive performance. The paper in hand presents a precise and explainable deep learning predictor which makes use of advanced language modelling strategies for the extraction of semantic and discriminative patterns. Extracted patterns are utilized to train an attention-based novel classifier that is competent in precisely identifying hate content. Over coarse-grained benchmark dataset, the proposed predictor significantly outperforms state-of-the-art predictor by 8.7% in terms of accuracy, precision and F1-score. Similarly, over fine-grained dataset, in comparison with state-of-the-art predictor, it achieves performance gain of 10.6%, 17.6%, 18.6% and 17.6% in terms of accuracy, precision, recall and F1-score.

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ToxLex_bn: A curated dataset of bangla toxic language derived from Facebook comment

Rashid

2022

Data in Brief

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Roman Urdu Multi-Class Offensive Text Detection using Hybrid Features and SVM

Cited by 10 publications

References 7 publications

Hate speech detection in social media: Techniques, recent trends, and future challenges

Hate speech detection in social media: Techniques, recent trends, and future challenges

Passion-Net: a robust precise and explainable predictor for hate speech detection in Roman Urdu text

ToxLex_bn: A curated dataset of bangla toxic language derived from Facebook comment

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