Effects of Feature Extraction and Classification Methods on Cyberbully Detection

Saraç, Esra; Özel, Selma Ayşe

doi:10.19113/sdufbed.20964

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…The researchers did not rely only on statistical measures, instead extracting the features of the content, such as profanity and pronouns. In [17], researchers used document frequency, which identifies the number of documents in which a given word appears. They defined a threshold of 0.1% for words to be included, and words with a percentage of occurrence below this threshold were considered to have been misspelled.…”

Section: Related Work 21 Cyberbullying Detectionmentioning

confidence: 99%

Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

Al-Ajlan¹,

Ykhlef²

2023

Computers, Materials &Amp; Continua

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There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms. Among them is cyberbullying, which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively. An alarmingly high percentage of people, especially teenagers, have reported being cyberbullied in recent years. A variety of approaches have been developed to detect cyberbullying, but they require time-consuming feature extraction and selection processes. Moreover, no approach to date has examined the meanings of words and the semantics involved in cyberbullying. In past work, we proposed an algorithm called Cyberbullying Detection Based on Deep Learning (CDDL) to bridge this gap. It eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting cyberbullying. This was accomplished by incorporating deep learning-specifically, a convolutional neural network (CNN)-into the detection process. Although this algorithm shows remarkable improvement in performance over traditional detection mechanisms, one problem with it persists: CDDL requires that many parameters (filters, kernels, pool size, and number of neurons) be set prior to classification. These parameters play a major role in the quality of predictions, but a method for finding a suitable combination of their values remains elusive. To address this issue, we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter setting. The proposed method does not require features for its predictions and its detection of cyberbullying is fully automated. The firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98% within acceptable polynomial time.

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Section: Related Work 21 Cyberbullying Detectionmentioning

confidence: 99%

Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

Al-Ajlan¹,

Ykhlef²

2023

Computers, Materials &Amp; Continua

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A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media

Kumar

Sachdeva

2021

World Wide Web

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As a constructive mode of information sharing, collaboration and communication, social media platforms offer users with limitless opportunities. The same hypermedia can be transposed into a synthetic and toxic milieu that provides an anonymous, destructive pedestal for online bullying and harassment. Automatic cyberbullying detection on social media using synthetic or real-world datasets is one of a proverbial natural language processing problem. Analyzing a given text requires capturing the existent semantics, syntactic and spatial relationships. Learning representative features automatically using deep learning models efficiently captures the contextual semantics and word order arrangement to build robust and superlative predictive models. This work puts forward a hybrid model, Bi-GRU-Attention-CapsNet (Bi-GAC), that benefits by learning sequential semantic representations and spatial location information using a Bi-GRU with self-attention followed by CapsNet for cyberbullying detection in the textual content of social media. The proposed Bi-GAC model is evaluated for performance using F1-score and ROC-AUC curve as metrics. The results show a superior performance to the existing techniques on the benchmark Formspring.me and MySpace datasets. In comparison to the conventional models, an improvement of nearly 9% and 3% in F-score is observed for MySpace and Formspring.me dataset respectively.

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A comprehensive review of cyberbullying-related content classification in online social media

Teng,

Varathan,

Crestani

2024

Expert Systems with Applications

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Effects of Feature Extraction and Classification Methods on Cyberbully Detection

Cited by 5 publications

References 25 publications

Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media

A comprehensive review of cyberbullying-related content classification in online social media

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