A Bullying-Severity Identifier Framework Based on Machine Learning and Fuzzy Logic

Sedano, Carmen R.; Ursini, Edson L.; Martins, Paulo S.

doi:10.1007/978-3-319-59063-9_28

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…But they didn't deal with class imbalance data, fine grain classification to classify types of cyberbullying. Additionally, the work in [17] developed a framework to determine cyberbullying in texts, the framework employs a Fuzzy Logic System that uses the outputs of SVM classifiers as its inputs to identify the cyberbullying. Results show that it is necessary to improve the accuracy of SVM classifiers to determine the bullying severity through Fuzzy Logic.…”

Section: Related Workmentioning

confidence: 99%

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

Ibrahim,

Essameldin,

Saad

2024

IEEE Access

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Cyberbullying is a social media network issue, a global crisis affecting the victims and society. Automatically identifying cyberbullying on social media has become extremely hard because of the complicated nature and intricate language employed within these platforms. The brevity and informal nature of text often results in ambiguous or unclear expressions, making it challenging to accurately interpret the intended meaning. Identifying cyberbullying becomes even more complex when faced with uncertain or contextually vague content. Presently, numerous approaches are available for cyberbullying detection, However, they continue to grapple with the challenge of distinguishing between various forms of cyberbullying-related hate speech due to its ambiguous and vague nature, and they also fall short in terms of accuracy. This paper proposes a novel approach to fine-grained cyberbullying classification by integrating Neutrosophic Logic within the Multi-Layer Perceptron (MLP) model. The proposed model enhances cyberbullying types by mitigating the challenges posed by the ambiguity and overlapping boundaries between distinct categories of cyberbullying. The incorporation of Neutrosophic Logic aims to address the uncertainty, ambiguity, and indeterminacy within classification decisions, offering a more comprehensive and flexible approach for handling complex classification scenarios. The model, leveraging the one-against-one strategy in MLP classification, captures complex relationships between various types of cyberbullying, due to the overlaps and ambiguous instances within cyberbullying types. The testing phase of this model emphasizes the significance of Neutrosophic Logic, employing class probabilities from multiple one-against-one classifiers to provide a comprehensive insight into classification outcomes. The results of the proposed model demonstrate the performance enhancement of incorporating Neutrosophic Logic for fine-grained cyberbullying classification tasks.

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Section: Related Workmentioning

confidence: 99%

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

Ibrahim,

Essameldin,

Saad

2024

IEEE Access

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“…Machine learning techniques were also applied to detect cyberbullying traces, however, most of them relate by the bully perspective [11,12], therefore support cant be aimed if a person reports him as a victim. Similarly, a temporal-causal study of a victim discussed flight and fight reaction of a victim [7].…”

Section: Related Workmentioning

confidence: 99%

From Victim to Survivor: A Multilayered Adaptive Mental Network Model of a Bully Victim

Jabeen

Gerritsen

Treur

2020

Artificial Intelligence and Soft Computing

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“…As a result, they present a real success story for detecting cyberbullying in an elementary school. Reference [11] defends the need not to label young people as aggressors or victims of cyberbullying but rather to apply a degree of severity depending on their degree of harassment. They apply a classifier and then a fuzzy logic system that uses the classifier data to identify the severity of the harassment.…”

Section: Related Workmentioning

confidence: 99%

Harassment Detection Using Machine Learning and Fuzzy Logic Techniques

Molina-Gil¹,

Concepción-Sánchez²,

Caballero‐Gil³

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Social networks, instant messaging applications, smartphones and the Internet are the main technological tools used by adolescents for communication. While they can benefit from those tools, they can also be used as a weapon for harassment. Cyberbullying is the name used for a current global social problem derived from harassment that uses offensive messages, which is severely affecting the youngest. Different types of software to identify and filter offensive contents have been developed in the last years. However, most of them are time consuming, not scalable and focused on very specific environments. To address this problem, we propose a mobile application for smartphones that provides a potential offensive content detection in order to determine whether a cyberbullying attack exists or not. In particular, we have developed an application that combines data pre-processing, fuzzy logic and machine learning to predict cyberbullying content. The main idea is to install a mobile application on the smartphone of a possible victim, so that it runs in the background. The system analyzes all received messages and notifications using data processing and decision-making algorithms. Finally, a fuzzy logic technique helps the system to reach a conclusion under a certain degree of imprecision.

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A Bullying-Severity Identifier Framework Based on Machine Learning and Fuzzy Logic

Cited by 6 publications

References 16 publications

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

From Victim to Survivor: A Multilayered Adaptive Mental Network Model of a Bully Victim

Harassment Detection Using Machine Learning and Fuzzy Logic Techniques

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