PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

Sharmila, P.; Anbananthen, Kalaiarasi Sonai Muthu; Chelliah, Deisy; Parthasarathy, S.; Kannan, Subarmaniam

doi:10.1109/access.2022.3210177

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…New datasets that better reflect data distributions in the real world can be created (MacAvaney et al, 2019). Multimodal HSD can be explored which can include images with text and video datasets to collect additional tweets on hate speech (Qureshi & Sabih, 2021; Sharmila et al, 2022). Multi‐lingual models for HSD in social media can be developed (Khan, Fazil, et al, 2022; Kumar Roy et al, 2022).…”

Section: Discussion and Future Workmentioning

confidence: 99%

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: Discussion and Future Workmentioning

confidence: 99%

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

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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“…The study provides insights into detecting and analyzing anti-Asian hate speech across different demographics. Recent research has explored various machine learning such as J48graft [3] and deep learning techniques including Pattern-Based Deep Hate Speech Detection (PDHS) [4] to detect and moderate toxic comments automatically.…”

Section: Literature Surveymentioning

confidence: 99%

Toxic Comment Detection Using Bidirectional Sequence Classifiers

Maity,

More,

Patil

et al. 2024

2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)

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With the rising surge of online toxicity, automating the identification of abusive language becomes crucial for improving online discourse. This study proposes a deep learning system that efficiently uses multiple labels to classify harmful comments using bi-directional Long Short-Term Memory (LSTM) networks. By leveraging contextual information, the bi-LSTM model achieves state-of-the-art performance in classifying subtle forms of toxicity such as threats, insults, identity hate, and obscenity. The model achieves above 95% accuracy on benchmark datasets with rigorous data processing, optimized neural architecture, and the utilization of FastText embeddings to handle words that are not in the vocabulary. This technique can automatically filter different levels of toxicity, promoting positive online interactions when integrated into online platforms. The proposed study outlines an end-to-end pipeline incorporating recent NLP advancements and deep contextualized language models to address contemporary challenges in AI-enabled content moderation.

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“…Shannaq et al (2022) use a genetic algorithm and XGBoost to detect hate speech in Arabic. Sharmila et al (2022) devised the Dual‐level Cross Attention approach to classify material into three categories: hateful, offensive and neither. In Table 3, a detailed summary of various recent hate speech detection research is given.…”

Section: Hate Speech Detection In Different Data Modalitiesmentioning

confidence: 99%

“…developed a capsule network-based Convolutional and Bi-Directional Gated Recurrent Unit classifier Shannaq et al (2022). use a genetic algorithm and XGBoost to detect hate speech in Arabic Sharmila et al (2022). devised the Dual-level Cross Attention approach to classify material into three categories: hateful, offensive and neither.…”

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confidence: 99%

Hate speech detection: A comprehensive review of recent works

Gandhi,

Ahir,

Adhvaryu

et al. 2024

Expert Systems

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There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques in varied application domains, it would be intriguing to consider these techniques for automated hate speech detection. In literature, there have been efforts to recognize and categorize hate speech using varied Machine Learning (ML) and Deep Learning (DL) techniques. Hence, considering the need and provocations for hate speech detection we aim to present a comprehensive review that discusses fundamental taxonomy as well as recent advances in the field of online hate speech identification. There is a significant amount of literature related to the initial phases of hate speech detection. The background section provides a detailed explanation of the previous research. The subsequent section that follows is dedicated to examining the recent literature published from the year 2020 onwards. The paper presents some of the hate speech datasets considered for hate speech detection. Furthermore, the paper discusses different data modalities, namely, textual hate speech detection, multi‐modal hate speech detection and multilingual hate speech detection. Apart from systematic review on hate speech detection, the paper also implement several multi‐label models to compare the performance of hate speech detection by employing classic ML technique namely, Logistic Regression and DL technique namely, Long Short‐Term Memory (LSTM) and a multiclass multi‐label architecture. In the implemented architecture, we have derived two new elements to quantify the hatefulness and intensity of hatred to improve the results for hate speech detection using Indonesian tweet dataset. Empirical Analysis of the model reveals that the implemented approach outperforms and is able to achieve improved results for the underlying dataset.

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PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

Cited by 7 publications

References 34 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

Toxic Comment Detection Using Bidirectional Sequence Classifiers

Hate speech detection: A comprehensive review of recent works

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