I’m out of breath from laughing! I think? A dataset of COVID-19 Humor and its toxic variants

Bogireddy, Neha Reddy; Suresh, Smriti; Rai, Sunny

doi:10.1145/3543873.3587591

Cited by 3 publications

(1 citation statement)

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“…Notably, an intensified focus on classifier ensembles and the efficiency of meta-learning strategies is essential for advancing this domain (Zampieri et al, 2019). In addition, a comprehensive exploration of the role of humor in HSD tasks is warranted (Fortuna & Nunes, 2018;Mathew et al, 2020) with the literature underscoring an enthusiastic interest in sarcasm detection, colloquially referred to as humor tweet detection, in some quarters (Bogireddy et al, 2023;Kazienko et al, 2023). Furthermore, considerations related to dialects are imperative to mitigate the risks of intentional racial biases in HSD, as cogently articulated by (Field et al, 1925).…”

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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