Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language

Marreddy, Mounika; Oota, Subba Reddy; Vakada, Lakshmi Sireesha; Chinni, Venkata Charan; Mamidi, Radhika

doi:10.1109/ijcnn55064.2022.9892105

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…This process is akin to learning from the social context, allowing the model to grasp the nuanced dynamics of hate speech propagation. By iteratively applying graph convolutions across layers, GCNs can capture both local and global patterns of influence within the network, thereby identifying not only individual hate speech contributors but also influential users or communities that amplify toxic content (Duong et al, 2022; Marreddy et al, 2022). Furthermore, GCNs can adapt to changing network dynamics and emerging hate speech patterns, ensuring their relevance in the ever‐evolving landscape of online discourse.…”

Section: Discussionmentioning

confidence: 99%

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

Rawat,

Kumar,

Samant

2024

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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: Discussionmentioning

confidence: 99%