Hate speech and offensive language detection in Dravidian languages using deep ensemble framework

Roy, Pradeep Kumar; Bhawal, Snehaan; Subalalitha, C. N.

doi:10.1016/j.csl.2022.101386

Cited by 44 publications

(10 citation statements)

References 23 publications

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“…Subsequently, Facebook has been another substantial source (Bilal et al, 2022; MacAvaney et al, 2019; Mozafari et al, 2022; Rodriguez et al, 2022; Sreelakshmi et al, 2020). Additionally, alternative platforms such as YouTube comments have been utilized (Kumar Roy et al, 2022; Roy et al, 2022; Sajid et al, 2020), along with resources like Wikipedia (Beddiar et al, 2021) and diverse online platforms (Alatawi et al, 2021; Beddiar et al, 2021). These data sources have been meticulously annotated to construct datasets for the explicit purpose of generating models for the categorization of hate speech.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Additionally, subtle forms of toxic content, like sarcasm or memes that target specific groups, can be particularly challenging to detect. Therefore, recent advances in applying transformer-based models to identify toxicity show how specific feature combination strategies [54] and ensemble models [55] achieve promising results. Finally, researchers evaluated the ability of Generative Pretrained Transformers (GPTs) to create synthetic datasets which can serve as input for deep learning architectures [56].…”

Section: ML For Toxicity Identificationmentioning

confidence: 99%

A Topology-Based Approach for Predicting Toxic Outcomes on Twitter and YouTube

Etta,

Cinelli,

Marco

et al. 2024

IEEE Trans. Netw. Sci. Eng.

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The benefits of an information ecosystem based on social media platforms came at the cost of the rise of several antisocial behaviours, including the use of toxic speech. To assess the aspects that concur with the formation of toxic conversations, we provide a multi-platform comparison on Twitter and YouTube between the 2022 Italian Political Elections, representing a potentially polarising topic, and the Italian Football League, a topic close to the country's popular culture. We first probe structural and conversational toxicity differences by analyzing 257K conversations (3.7M posts, 1M users) on both platforms. Then, we provide a machine learning approach that, by leveraging the previous features, identifies the presence of the following toxic comment in different stages of conversations. We show that football tends to exhibit lower toxicity levels than politics, with the latter producing more extended conversations that attract a broader audience and consequently fostering the polarization phenomenon. The implemented classifiers resulting from the conversation stage-based approach achieve state-of-the-art performances despite a restricted set of features. Furthermore, our cross-topic comparison shows that models trained on a divisive topic can be applied to other discussions without causing a degradation of their performance.

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“…Computer science highlighted that the difficulties in defining OHS affect the process of detecting this content where deep learning, machine learning (Bhawal, et al, 2021; Roy et al, 2020), and annotators play an important role. The use of code-mixed language (Roy et al, 2022 ) and undesired biases (Velankar et al, 2022) make OHS detection challenging. By focussing on context-dependent factors, these contributions failed to consider the moral judgement process affecting human work when annotators manually detect and label OHS (Velankar, et al, 2022).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Online Hate Speech as a Moral Issue: Exploring Moral Reasoning of Young Italian Users on Social Network Sites

Ieracitano

Balenzano²,

Girardi³

et al. 2023

Social Science Computer Review

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Taking a neo-Kohlbergian approach, we explore the moral reasoning of 486 young Italian users of social network sites exposed to moral dilemmas concerning online hate speech. The aims are to understand what moral reasoning schemas they use as they face homophobic, racist, or sexist online hate speech, and what influence personal values and moral disengagement might have on their moral reasoning process. The results reveal the prevalence of Maintaining Norms reasoning (conformity to rules and authority) in making moral decisions concerning online hate speech and confirms the mediating role of Hate Speech Moral Disengagement in the relationship between personal values and the moral reasoning process.

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Hate speech and offensive language detection in Dravidian languages using deep ensemble framework

Cited by 44 publications

References 23 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

A Topology-Based Approach for Predicting Toxic Outcomes on Twitter and YouTube

Online Hate Speech as a Moral Issue: Exploring Moral Reasoning of Young Italian Users on Social Network Sites

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