Virtual Harassment, Real Understanding: Using a Serious Game and Bayesian Networks to Study Cyberbullying

• URL: http://arxiv.org/abs/2309.08428v1
• Date: Fri, 15 Sep 2023 14:30:28 GMT
• Title: Virtual Harassment, Real Understanding: Using a Serious Game and Bayesian Networks to Study Cyberbullying
• Authors: Jaime P\'erez, Mario Castro, Edmond Awad, Gregorio L\'opez
• Abstract summary: This study explores an innovative approach, employing a serious game as a non-intrusive tool for data collection and education. Preliminary pilot studies with the serious game show promising results, surpassing the informative capacity of traditional demographic and psychological questionnaires.
• Score: 0.9246281666115259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cyberbullying among minors is a pressing concern in our digital society, necessitating effective prevention and intervention strategies. Traditional data collection methods often intrude on privacy and yield limited insights. This study explores an innovative approach, employing a serious game - designed with purposes beyond entertainment - as a non-intrusive tool for data collection and education. In contrast to traditional correlation-based analyses, we propose a causality-based approach using Bayesian Networks to unravel complex relationships in the collected data and quantify result uncertainties. This robust analytical tool yields interpretable outcomes, enhances transparency in assumptions, and fosters open scientific discourse. Preliminary pilot studies with the serious game show promising results, surpassing the informative capacity of traditional demographic and psychological questionnaires, suggesting its potential as an alternative methodology. Additionally, we demonstrate how our approach facilitates the examination of risk profiles and the identification of intervention strategies to mitigate this cybercrime. We also address research limitations and potential enhancements, considering the noise and variability of data in social studies and video games. This research advances our understanding of cyberbullying and showcase the potential of serious games and causality-based approaches in studying complex social issues.

Related papers

• Cyberbully and Online Harassment: Issues Associated with Digital Wellbeing [0.0]
  This research synthesizes empirical findings from diverse studies to evaluate how innovative technological interventions contribute to reducing the prevalence of cyberbullying. The study focuses on the effectiveness of these interventions in various settings, highlighting the need for adaptive strategies that respond to the dynamic digital landscape.
  arXiv  Detail & Related papers  (2024-04-29T17:49:49Z)
• Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects [17.44202934049009]
  We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework. We demonstrate our method is accurate, robust, and scalable via an extensive simulation study.
  arXiv  Detail & Related papers  (2024-03-17T20:23:42Z)
• Data protection psychology using game theory [0.0]
  The research aims to explore how individuals perceive and interact with data protection practices. The study employs a game theoretical approach to investigate the psychological factors that influence individuals' awareness and comprehension of data protection measures.
  arXiv  Detail & Related papers  (2024-01-03T13:07:30Z)
• Stepping out of Flatland: Discovering Behavior Patterns as Topological Structures in Cyber Hypergraphs [0.7835894511242797]
  We present a novel framework based in the theory of hypergraphs and topology to understand data from cyber networks. We will demonstrate concrete examples in a large-scale cyber network dataset.
  arXiv  Detail & Related papers  (2023-11-08T00:00:33Z)
• Designing an attack-defense game: how to increase robustness of financial transaction models via a competition [69.08339915577206]
  Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical. We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input. We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data. The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
  arXiv  Detail & Related papers  (2023-08-22T12:53:09Z)
• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [76.47138162283714]
  Forgetting refers to the loss or deterioration of previously acquired information or knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases.
  arXiv  Detail & Related papers  (2023-07-16T16:27:58Z)
• Re-thinking Data Availablity Attacks Against Deep Neural Networks [53.64624167867274]
  In this paper, we re-examine the concept of unlearnable examples and discern that the existing robust error-minimizing noise presents an inaccurate optimization objective. We introduce a novel optimization paradigm that yields improved protection results with reduced computational time requirements.
  arXiv  Detail & Related papers  (2023-05-18T04:03:51Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning [114.9857000195174]
  A major challenge to widespread industrial adoption of deep reinforcement learning is the potential vulnerability to privacy breaches. We propose an adversarial attack framework tailored for testing the vulnerability of deep reinforcement learning algorithms to membership inference attacks.
  arXiv  Detail & Related papers  (2021-09-08T23:44:57Z)
• Cyberbullying Detection with Fairness Constraints [0.0]
  We propose a model training scheme that can employ fairness constraints and validate our approach with different datasets. We believe our work contributes to the pursuit of unbiased, transparent, and ethical machine learning solutions for cyber-social health.
  arXiv  Detail & Related papers  (2020-05-09T13:04:26Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.