Analysis of Cyber-bullying in a virtual social networking

Ochoa, Alberto; Ponce, Julio; Jaramillo, Rubén; Ornelas, Francisco; Hernández, Alberto; Azpeitia, Daniel; Elías, Arturo

doi:10.1109/his.2011.6122110

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…The datasets utilized for the purpose of this study were real-time and consist of massive amounts of noise inherent to the dynamic nature of various social media platforms. Before applying multivariable analysis techniques (Ochoa et al, 2011 ), a rigorous data preprocessing stage was implemented to ensure the quality and reliability of the dataset. This involved cleaning and filtering the social media content to remove noise, irrelevant information, and duplicate posts.…”

Section: Proposed Frameworkmentioning

confidence: 99%

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

Nitya Harshitha,

Prabu,

Suganya

et al. 2024

Front. Artif. Intell.

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The emergence of social media has given rise to a variety of networking and communication opportunities, as well as the well-known issue of cyberbullying, which is continuously on the rise in the current world. Researchers have been actively addressing cyberbullying for a long time by applying machine learning and deep learning techniques. However, although these algorithms have performed well on artificial datasets, they do not provide similar results when applied to real-time datasets with high levels of noise and imbalance. Consequently, finding generic algorithms that can work on dynamic data available across several platforms is critical. This study used a unique hybrid random forest-based CNN model for text classification, combining the strengths of both approaches. Real-time datasets from Twitter and Instagram were collected and annotated to demonstrate the effectiveness of the proposed technique. The performance of various ML and DL algorithms was compared, and the RF-based CNN model outperformed them in accuracy and execution speed. This is particularly important for timely detection of bullying episodes and providing assistance to victims. The model achieved an accuracy of 96% and delivered results 3.4 seconds faster than standard CNN models.

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Section: Proposed Frameworkmentioning

confidence: 99%

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

Nitya Harshitha,

Prabu,

Suganya

et al. 2024

Front. Artif. Intell.

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“…A possible example concerns private information disclosable via GPS updates. We also mention user-to-user insecurities, which can lead to bullying or blackmailing (Ochoa et al, 2011) and (Honjo et al, 2011). • Technological space: it represents the set of security risks related to specific technologies (both hardware and software) used to implement the OSN service.…”

Section: Taxonomy Of Osn-securitymentioning

confidence: 99%

A taxonomy-based model of security and privacy in online social networks

Caviglione

Coccoli

Merlo

2014

IJCSE

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Social environments were already present in the original web vision, but nowadays are mainly available through online social networks (OSNs), which are a real cultural phenomenon. However, their actual deployment is very heterogeneous, reflecting into different development choices and functional architectures. Such aspects, jointly with the intrinsic sharing of personal information, lead to severe risks both in terms of security and privacy. In this perspective, our work proposes a taxonomy-based approach to describe and model the complex security space characterising OSNs. The contributions of this paper are: 1) to introduce a systematic approach to define the 'problem space' of an OSN; 2) to showcase basic models for organising the engineering and the needed checking procedures.Reference to this paper should be made as follows: Caviglione, L., Coccoli, M. and Merlo, A. (2014) 'A taxonomy-based model of security and privacy in online social networks', Int. J.

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“…Further research by Ochoa et al (2011) suggested the use of multi-agent systems to understand cyberbullying. This approach argues for the need to understand the social and cultural implications of cyber-technologies.…”

Section: The Role Of Technologymentioning

confidence: 99%

Cyberbullying and the law: A review of psychological and legal challenges

Asam

Samara

2016

Computers in Human Behavior

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Cyberbullying, in its different forms, is common among children and adolescents and is facilitated by the increased use of technology. The consequences of cyberbullying could be severe, especially on mental health, potentially leading to suicide in extreme cases. Although parents, schools and online social networking sites are encouraged to provide a safe online environment, little is known about the legal avenues which could be utilised to prevent cyberbullying or act as a deterrent to such. This article attempts to explore current laws, and the challenges that exist to establishing cyberbullying legislation in the context of the UK. It is arguable that a number of statutes may be of assistance in relation to cyberbullying, namely However, given the lack of clear definition of bullying, the applicability of these laws to cyberbullying is open to debate. Establishing new legislation or a modification to existing laws is particularly challenging for a number of reasons, namely: an absence of consistent bullying/cyberbullying definition, a difficulty in determining intention to harm or evidence of such, a lack of surveillance, a lack of general awareness, issues surrounding jurisdiction, the role of technology, and the age of criminal responsibility. These challenges are elaborated and discussed in this article.

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Analysis of Cyber-bullying in a virtual social networking

Cited by 8 publications

References 10 publications

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

A taxonomy-based model of security and privacy in online social networks

Cyberbullying and the law: A review of psychological and legal challenges

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