Detection and Cross-domain Evaluation of Cyberbullying in Facebook Activity Contents for Turkish

Çoban, Önder; Özel, Selma Ayşe; İnan, Ali

doi:10.1145/3580393

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…Table 2 illustrates information about previous research that collected corpora in low-resource languages, such as Turkish (Beyhan et al, 2022 ; Coban et al, 2023 ), Kurdish (Saeed et al, 2022 ), and Bangali (Romim et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, in Mursi et al (2022), Islamic 10.3389/frai.2024 Radicalism is the type of hate to detect, and thus the binary labels are extremist or non-extremist. In Coban et al (2023), the target was to detect whether cyberbullying terms exist in Facebook posts; thus, the binary labels used were cyberbullying or non-cyberbullying.…”

Section: Hate Speech and Related Conceptsmentioning

confidence: 99%

“…Harassment refers to persistent unwanted behavior that causes distress or fear, oen involving repetitive and intrusive actions (Gilani et al, 2014). Cyberbullying speci cally occurs in the digital realm, encompassing the use of technology to harass, intimidate, or demean others (Coban et al, 2023). Offense refers to actions or expressions that cause displeasure or upset, while abuse involves using power or authority to harm or control others (Husain, 2020).…”

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confidence: 99%

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Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Ahmad,

Azzeh,

Alnagi

et al. 2024

Front. Artif. Intell.

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Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Hate Speech and Related Conceptsmentioning

confidence: 99%

mentioning

confidence: 99%

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Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Ahmad,

Azzeh,

Alnagi

et al. 2024

Front. Artif. Intell.

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“…The study in [19] introduced an attention-based LSTM-CNN model that dynamically weights the importance of textual and visual features extracted from social media posts, enabling the model to focus on salient cues indicative of cyberbullying behavior. By attending to informative features, the attention mechanism improved the model's discriminative power and robustness in cyberbullying detection [20].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Baiganova,

Toxanova,

Yerekesheva

et al. 2024

ijacsa

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With the burgeoning use of social media platforms, online harassment and cyberbullying have become significant concerns. Traditional mechanisms often falter, necessitating advanced methodologies for efficient detection. This study presents an innovative approach to identifying cyberbullying incidents on social media sites, employing a hybrid neural network architecture that amalgamates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). By harnessing the sequential processing capabilities of LSTM to analyze the temporal progression of textual data, and the spatial discernment of CNN to pinpoint bullying keywords and patterns, the model demonstrates substantial improvement in detection accuracy compared to extant methods. A diverse dataset, encompassing multiple social media platforms and linguistic styles, was utilized to train and test the model, ensuring robustness. Results evince that the LSTM-CNN amalgamation can adeptly handle varied sentence structures and contextual nuances, outstripping traditional machine learning classifiers in both specificity and sensitivity. This research underscores the potential of hybrid neural networks in addressing contemporary digital challenges, urging further exploration into blended architectures for nuanced problem-solving in cyber realms.

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“…Table 2 illustrates information about previous research that collected corpora in low-resource languages, such as Turkish (Beyhan et al, 2022;Coban et al, 2023), Kurdish (Saeed et al, 2022), and Bangali (Romim et al, 2021).…”

Section: Hate Speech Datasets For Other Languagesmentioning

confidence: 99%

Hate Speech Detection in the Arabic Language: Corpus Design, Construction and Evaluation

Ahmad,

Azzeh,

Alnagi

et al. 2023

Preprint

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Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven Machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

show abstract

Detection and Cross-domain Evaluation of Cyberbullying in Facebook Activity Contents for Turkish

Cited by 9 publications

References 32 publications

Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Hate Speech Detection in the Arabic Language: Corpus Design, Construction and Evaluation

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