A Novel IN-Gram Technique for Improving the Hate Speech Detection for Larger Datasets

Gupta, Bharat; Goel, Nikita; Jain, Dhruv; Gupta, Namita

doi:10.1007/978-981-15-2329-8_62

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Six distinct algorithms have been used to detect hate speech related to anti‐Muslim sentiment, including DL (Vidgen & Yasseri, n.d.). Due to the learning problem presented by the tiny dataset and few examples for other classes, Gupta et al (2020) used TF‐IDF, N‐grams, and a sentiment lexicon with an SVM to achieve better results.…”

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

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

show abstract

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

show abstract

“…For this issue, six different algorithms, including deep learning, were implemented to detect Islamophobia hate speech [51]. However, the dataset with a small number of instances containing other classes posed a challenge in the learning process; hence, [72] used TF-IDF, N-gram, and a sentiment lexicon with an SVM for better results. Some recent studies [31,32] are concerned with detecting hate speech related to COVID-19.…”

Section: Other Challengesmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

A Novel IN-Gram Technique for Improving the Hate Speech Detection for Larger Datasets

Cited by 2 publications

References 6 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

Contact Info

Product

Resources

About