Hate Speech &amp; Offensive Language Detection Using ML &amp;NLP

Panchala, Geetha Harshini; Sasank, V. V. S.; Adidela, Dory Ratna Harshitha; Yellamma, Pachipala; Ashesh, K.; Prasad, Chitturi

doi:10.1109/icssit53264.2022.9716417

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The fundamental nature of ML or hyperparameter choices can have an impact on the achievement of generalization. Generalizability issues affect several HSD components, including dataset preparation, model training, verification, and implementation (Panchala et al, 2022).…”

Section: Battling ML Challengesmentioning

confidence: 99%

“…Senarath and Purohit (2020) enhanced contextual interpretation in ML models for social media posts. According to Panchala et al (2022), traditional ML can discriminate between hate speech and offensive speech with accuracy. Al‐Makhadmeh and Tolba (2020) applied an ensemble DL classifier with optimized functions and weight‐updating processes.…”

Section: Challengesmentioning

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: Battling ML Challengesmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

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

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

View full text Add to dashboard Cite

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“…Semantic features feature can support and enhance the contextual interpretation of the word senses of a machine learning model. Recently, [108] attempted to distinguish between these two classes by using traditional machine learning with superior outcomes. Furthermore, [25] applied an ensemble deep learning classifier using an optimized function and weight-updating process.…”

Section: E) Separation Of Hate Speech From An Offensive Instancementioning

confidence: 99%

Twitter Hate Speech Detection: A Systematic Review of Methods, Taxonomy Analysis, Challenges, and Opportunities

2023

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Hate speech detection has substantially increased interest among researchers in the domain of natural language processing (NLP) and text mining. The number of studies on this topic has been growing dramatically. Thus, the purpose of this analysis is to develop a resource that consists of an outline of the approaches, methods, and techniques employed to address the issue of Twitter hate speech. This study can be used to aid researchers in the development of a more effective model for future studies. This review focused on studies published over the past eight years, i.e., from 2015 to 2022. This systematic search was carried out in December 2020 and updated in July 2022. Ninety-one articles published within the mentioned period met the set criteria and were selected for this review. From the evaluation of these works, it is clear that a perfect solution has yet to be found. To conclude, this paper focused on presenting an in-depth understanding of current perspectives and highlighted research opportunities to boost the quality of hate speech detection systems. In turn, this helps social networking services that seek to detect hate messages generated by users before they are posted, thus reducing the risk of targeted harassment.

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Offensive language and hate speech detection using deep learning in football news live streaming chat on YouTube in Thailand

Pookpanich,

Siriborvornratanakul

2024

Soc. Netw. Anal. Min.

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Hate Speech & Offensive Language Detection Using ML &NLP

Cited by 5 publications

References 15 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

Twitter Hate Speech Detection: A Systematic Review of Methods, Taxonomy Analysis, Challenges, and Opportunities

Offensive language and hate speech detection using deep learning in football news live streaming chat on YouTube in Thailand

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