Hate Speech Detection in Code-switched Text Messages

Ombui, Edward; Muchemi, Lawrence; Wagacha, Peter Waiganjo

doi:10.1109/ismsit.2019.8932845

Cited by 19 publications

(4 citation statements)

References 18 publications

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“…Mandl [111] proposed the first Hindi-English hate speech dataset containing tweets written in both Roman and the native Devanagari script. Additionally, a Swahili-English code-mixed hate speech dataset was recently published [87]. They gathered their dataset from Twitter, mainly related to the 2017 general election in Kenya.…”

Section: What Datasets Are Available For Multilingual Abusive Language Detection Study?mentioning

confidence: 99%

Towards multidomain and multilingual abusive language detection: a survey

Pamungkas

Basile

Patti

2021

Pers Ubiquit Comput

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Abusive language is an important issue in online communication across different platforms and languages. Having a robust model to detect abusive instances automatically is a prominent challenge. Several studies have been proposed to deal with this vital issue by modeling this task in the cross-domain and cross-lingual setting. This paper outlines and describes the current state of this research direction, providing an overview of previous studies, including the available datasets and approaches employed in both cross-domain and cross-lingual settings. This study also outlines several challenges and open problems of this area, providing insights and a useful roadmap for future work.

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Section: What Datasets Are Available For Multilingual Abusive Language Detection Study?mentioning

confidence: 99%

Towards multidomain and multilingual abusive language detection: a survey

Pamungkas

Basile

Patti

2021

Pers Ubiquit Comput

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“…Ensemble techniques in the context of HSD represent a sophisticated approach to improving the accuracy and robustness of models used to identify and combat harmful online content focusing on people or groups according to protected traits (Khanday et al, 2022; Ombui et al, 2019). Ensemble techniques are particularly valuable because they combine the predictions of multiple ML models to make collective decisions that are often more accurate and reliable than those of individual models.…”

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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“…By employing varied approaches in dataset creation, researchers can explore whether these different contexts share common characteristics that could augment the effectiveness of hate speech detection. Although laws and regulations by authorities such as governmental organizations and human rights organizations are exerting pressure on social media companies to properly address the phenomenon of hate speech on their platforms, the majority of these companies depend upon users to flag such information, which is subsequently subject to some kind of manual screening, and this strategy appears to be impractical ( Ombui, Muchemi & Wagacha, 2019 ). This study sheds light on the role that automated solutions can play in resolving the problem.…”

Section: Discussionmentioning

confidence: 99%

Detection of cyberhate speech towards female sport in the Arabic Xsphere

Alhayan,

Almobarak,

Shalabi

et al. 2024

PeerJ Computer Science

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The recent rapid growth in the number of Saudi female athletes and sports enthusiasts’ presence on social media has exposed them to gender-hate speech and discrimination. Hate speech, a harmful worldwide phenomenon, can have severe consequences. Its prevalence in sports has surged alongside the growing influence of social media, with X serving as a prominent platform for the expression of hate speech and discriminatory comments, often targeting women in sports. This research combines two studies that explores online hate speech and gender biases in the context of sports, proposing an automated solution for detecting hate speech targeting women in sports on platforms like X, with a particular focus on Arabic, a challenging domain with limited prior research. In Study 1, semi-structured interviews with 33 Saudi female athletes and sports fans revealed common forms of hate speech, including gender-based derogatory comments, misogyny, and appearance-related discrimination. Building upon the foundations laid by Study 1, Study 2 addresses the pressing need for effective interventions to combat hate speech against women in sports on social media by evaluating machine learning (ML) models for identifying hate speech targeting women in sports in Arabic. A dataset of 7,487 Arabic tweets was collected, annotated, and pre-processed. Term frequency-inverse document frequency (TF-IDF) and part-of-speech (POS) feature extraction techniques were used, and various ML algorithms were trained Random Forest consistently outperformed, achieving accuracy (85% and 84% using TF-IDF and POS, respectively) compared to other methods, demonstrating the effectiveness of both feature sets in identifying Arabic hate speech. The research contribution advances the understanding of online hate targeting Arabic women in sports by identifying various forms of such hate. The systematic creation of a meticulously annotated Arabic hate speech dataset, specifically focused on women’s sports, enhances the dataset’s reliability and provides valuable insights for future research in countering hate speech against women in sports. This dataset forms a strong foundation for developing effective strategies to address online hate within the unique context of women’s sports. The research findings contribute to the ongoing efforts to combat hate speech against women in sports on social media, aligning with the objectives of Saudi Arabia’s Vision 2030 and recognizing the significance of female participation in sports.

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Hate Speech Detection in Code-switched Text Messages

Cited by 19 publications

References 18 publications

Towards multidomain and multilingual abusive language detection: a survey

Towards multidomain and multilingual abusive language detection: a survey

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

Detection of cyberhate speech towards female sport in the Arabic Xsphere

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