Hate speech recognition in multilingual text: hinglish documents

Yadav, Arun Kumar; Kumar, Mohit; Kumar, Abhishek; Shivani, .; Kusum, .; Yadav, Divakar

doi:10.1007/s41870-023-01211-z

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Machine learning has emerged as a promising approach for automated hate speech detection (Sultan et al, 2023). By training models on labeled datasets, machine learning algorithms can learn patterns and linguistic cues associated with hate speech (Yadav et al, 2023a), enabling them to distinguish between offensive content and non-offensive language.…”

Section: Research Articlementioning

confidence: 99%

A comparative analysis of machine learning algorithms for hate speech detection in social media

Omran,

Al Tararwah,

Al Qundus

2023

ONLINE J COMMUN MEDIA TECHNOL

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A<b> </b>detecting and mitigating hate speech in social media, particularly on platforms like Twitter, is a crucial task with significant societal impact. This research study presents a comprehensive comparative analysis of machine learning algorithms for hate speech detection, with the primary goal of identifying an optimal algorithmic combination that is simple, easy to implement, efficient, and yields high detection performance. Through meticulous pre-processing and rigorous evaluation, the study explores various algorithms to determine their suitability for hate speech detection. The focus is finding a combination that balances simplicity, ease of implementation, computational efficiency, and strong performance metrics. The findings reveal that the combination of naïve Bayes and decision tree algorithms achieves a high accuracy of 0.887 and an F1-score of 0.885, demonstrating its effectiveness in hate speech detection. This research contributes to identifying a reliable algorithmic combination that meets the criteria of simplicity, ease of implementation, quick processing, and strong performance, providing valuable guidance for researchers and practitioners in hate speech detection in social media. By elucidating the strengths and limitations of various algorithmic combinations, this research enhances the understanding of hate speech detection. It paves the way for developing robust solutions, creating a safer, more inclusive digital environment.

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Section: Research Articlementioning

confidence: 99%

A comparative analysis of machine learning algorithms for hate speech detection in social media

Omran,

Al Tararwah,

Al Qundus

2023

ONLINE J COMMUN MEDIA TECHNOL

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“…Hate speech can also be defined as a deliberate attack directed at a particular group motivated by aspects of group identity [2]. In this technology era, internet and social media platforms have become an integral part of social life [3]. Twitter is one of the social media platforms that is widely used by the public.…”

Section: Introductionmentioning

confidence: 99%

Classifying possible hate speech from text with deep learning and ensemble on embedding method

Caprisiano,

Ramadhansyah,

Zahra

2024

Bulletin EEI

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Hate speech can be defined as the use of language to express hatred towards another party. Twitter is one of the most widely used social media platforms in the community. In addition to submitting user-generated content, other users can provide feedback through comments. There are several users who intentionally or unintentionally provide negative comments. Even though there are regulations regarding the prohibition of hate speech, there are still those who make negative comments. Using the deep learning method with the long short-term memory (LSTM) model, a classifier of possible hate speech from messages on Twitter is carried out. With the ensemble method, term frequency times inverse document frequency (TF-IDF) and global vector (GloVe) get 86% accuracy, better than the stand-alone word to vector (Word2Vec) method, which only gets 80%. From these results, it can be concluded that the ensemble method can improve accuracy compared to only using the stand-alone method. Ensemble methods can also improve the performance of deep learning systems and produce better results than using only one method.

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Information Technology for Identifying Hate Speech in Online Communication Based on Machine Learning

Tverdokhlib,

Vysotska,

Pukach

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Hate speech recognition in multilingual text: hinglish documents

Cited by 6 publications

References 32 publications

A comparative analysis of machine learning algorithms for hate speech detection in social media

A comparative analysis of machine learning algorithms for hate speech detection in social media

Classifying possible hate speech from text with deep learning and ensemble on embedding method

Information Technology for Identifying Hate Speech in Online Communication Based on Machine Learning

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