Hate Speech Detection on Multilingual Twitter Using Convolutional Neural Networks

Elouali, Aya; Elberrichi, Zakaria; Elouali, Nadia

doi:10.18280/ria.340111

Cited by 9 publications

(18 citation statements)

References 17 publications

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“…Deep neural networks have been widely used for multilingual offensive language recognition because of their ability to acquire complex representations of text across different languages. There was an extensive use of convolutional neural networks (CNNs) ( Elouali, Elberrichi & Elouali, 2020 ; Bigoulaeva et al, 2023 , 2022 ; Bigoulaeva, Hangya & Fraser, 2021 ), CNN-GRU (gated recurrent unit) ( Deshpande, Farris & Kumar, 2022 ; Aluru et al, 2020 ) and recurrent neural networks (RNNs) like long short-term memory (LSTM), where Vashistha & Zubiaga (2021) used CNN-LSTM model, and Pamungkas, Basile & Patti (2021a) utilized LSTM along with MUSE and mBERT. Adding to that, Bigoulaeva et al (2022) , and Vitiugin, Senarath & Purohit (2021) used LSTM for word embeddings.…”

Section: Approaches On Multilingual Hate Speech Detectionmentioning

confidence: 99%

A survey on multi-lingual offensive language detection

Mnassri,

Farahbakhsh,

Chalehchaleh

et al. 2024

PeerJ Computer Science

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The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term “Offensive Language” encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single language, focus and represents more linguistic diversities and cultural factors. By exploring multilingual offensive content, we can broaden our understanding and effectively combat the widespread global impact of offensive language. This survey examines the existing state of multilingual offensive language detection, including a comprehensive analysis on previous multilingual approaches, and existing datasets, as well as provides resources in the field. We also explore the related community challenges on this task, which include technical, cultural, and linguistic ones, as well as their limitations. Furthermore, in this survey we propose several potential future directions toward more efficient solutions for multilingual offensive language detection, enabling safer digital communication environment worldwide.

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Section: Approaches On Multilingual Hate Speech Detectionmentioning

confidence: 99%

A survey on multi-lingual offensive language detection

Mnassri,

Farahbakhsh,

Chalehchaleh

et al. 2024

PeerJ Computer Science

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“…To the best of our knowledge, the study by Elouali et al [7] is the only study that addresses the detection of hate speech in a mix of different languages, including pure English and code-mixed Hindi-English. In this study, we propose an efficient method to accurately detect hate speech in a mix of English and Hindi-English tweets.…”

Section: Contributionmentioning

confidence: 99%

“…Like India, many other countries have also imposed strict laws to combat online hate speech [5,6]. Twitter is adamant on removing hateful content but is still criticized for not being effective at it [7].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Data Pre-Processing on Hate Speech Detection in a Mix of English and Hindi–English (Code-Mixed) Tweets

Al-Hussaeni,

Sameer,

Karamitsos

2023

Applied Sciences

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Due to the increasing reliance on social network platforms in recent years, hate speech has risen significantly among online users. Government and social media platforms face the challenging responsibility of controlling, detecting, and removing massively growing hateful content as early as possible to prevent future criminal acts, such as cyberviolence and real-life hate crimes. Twitter is used globally by people from various backgrounds and nationalities; it contains tweets posted in different languages, including code-mixed language, such as Hindi–English. Due to the informal format of tweets with variations in spelling and grammar, hate speech detection is especially challenging in code-mixed text. In this paper, we tackle the critical issue of hate speech detection on social media, with a focus on a mix of English and Hindi–English (code-mixed) text messages on Twitter. More specifically, we aim to evaluate the impact of data pre-processing on hate speech detection. Our method first performs 10-step data cleansing; then, it builds a detection method based on two architectures, namely a convolutional neural network (CNN) and a combination of CNN and long short-term Memory (LSTM) algorithms. We tune the hyperparameters of the proposed model architectures and conduct extensive experimental analysis on real-life tweets to evaluate the performance of the models in terms of accuracy, efficiency, and scalability. Moreover, we compare our method with a closely related hate speech detection method from the literature. The experimental results suggest that our method results in an improved accuracy and a significantly improved runtime. Among our best-performing models, CNN-LSTM improved accuracy by nearly 2% and decreased the runtime by almost half.

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“…Numerous feature types and feelings were derived and organized in 15 distinct data combinations. In [17], created a method where, taking profits of neural network (NN), classifies tweets written in 7 distinct languages (and also those above one language at once) to hate speech (HS) or non-HS. It utilized a convolutional neural network (CNN) and character-level representation.…”

Section: Introductionmentioning

confidence: 99%

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Motwakel¹,

Al-onazi²,

Alazwari³

et al. 2023

Computer Systems Science and Engineering

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Arabic is the world's first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people's everyday lives. This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection (IALODL-OHSD) on Arabic Cross-Corpora. The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media. In the IALODL-OHSD model, a threestage process is performed, namely pre-processing, word embedding, and classification. Primarily, data pre-processing is performed to transform the Arabic social media text into a useful format. In addition, the word2vec word embedding process is utilized to produce word embeddings. The attentionbased cascaded long short-term memory (ACLSTM) model is utilized for the classification process. Finally, the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results. To illustrate a brief result analysis of the IALODL-OHSD model, a detailed set of simulations were performed. The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

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Hate Speech Detection on Multilingual Twitter Using Convolutional Neural Networks

Cited by 9 publications

References 17 publications

A survey on multi-lingual offensive language detection

A survey on multi-lingual offensive language detection

The Impact of Data Pre-Processing on Hate Speech Detection in a Mix of English and Hindi–English (Code-Mixed) Tweets

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

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