Hate Speech Detection in Twitter using Natural Language Processing

Pariyani, Bhavesh; Shah, Krish; Shah, Meet M.; Vyas, Tarjni; Degadwala, Sheshang

doi:10.1109/icicv50876.2021.9388496

Cited by 20 publications

(6 citation statements)

References 14 publications

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“…Rizos et al (2019) tackled class imbalance with data augmentation using a recurrent neural language generation system, resulting in significant performance improvements, particularly for the hate speech class. Recognizing the importance of managing imbalanced datasets, Pariyani et al (2021) suggest employing a range of approaches to effectively handle challenges in HSD models.…”

Section: Battling ML Challengesmentioning

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: Battling ML Challengesmentioning

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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“…Imbalance is a further challenge to the model and may demand distinct mechanisms. To manage the unbalance class dataset, a range of approaches must be used [111].…”

Section: B) Imbalance Issuementioning

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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“…Bhavesh Pariyani et al [9] made an automated hate speech detection model using different Natural Language Processing models to classify hate speech using ML algorithms. Aljarah I, Habib M, Hijazi N, et al [10] collected the dataset that is related to racism, terrorism, Islam, etc.…”

Section: Hate Speech and Nlpmentioning

confidence: 99%

Hate Speech Detection Model on Web 3.0 Based Platform using Blockchain and NLP

Farooq,

Ali

2022

VFAST trans. softw. eng.

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With the increase in usage of social media applications like Facebook, Twitter, or Instagram, hate speech is also on the rise. Hate speech can be defined as the ill talk toward any race, caste, religion, and ethnicity. Now with the new development of web 3.0 which is decentralized, it is difficult to control elements like hate speech because there is no central body that can control it. In this research paper, we have proposed an NLP-based hate speech detection model for Web 3.0 that is able to detect hate speech with the accuracy of 89% before it gets to Blockchain. The NLP-based algorithms, IPFS, and smart contracts are used.

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Hate Speech Detection in Twitter using Natural Language Processing

Cited by 20 publications

References 14 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

Hate Speech Detection Model on Web 3.0 Based Platform using Blockchain and NLP

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