Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying

• URL: http://arxiv.org/abs/2404.03686v1
• Date: Mon, 1 Apr 2024 20:41:28 GMT
• Title: Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying
• Authors: Rohan Biswas, Kasturi Ganguly, Arijit Das, Diganta Saha,
• Abstract summary: cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects indi-viduals exposed to it. It stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safer.
• Score: 1.8749305679160366
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In today's digital world, cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects indi-viduals exposed to it. It also stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safer. Plus, it talks about how making more accurate tools to spot cyberbullying will be really helpful in the future. Our paper introduces a deep learning-based ap-proach, primarily employing BERT and BiLSTM architectures, to effective-ly address cyberbullying. This approach is designed to analyse large vol-umes of posts and predict potential instances of cyberbullying in online spaces. Our results demonstrate the superiority of the hateBERT model, an extension of BERT focused on hate speech detection, among the five mod-els, achieving an accuracy rate of 89.16%. This research is a significant con-tribution to "Computational Intelligence for Social Transformation," prom-ising a safer and more inclusive digital landscape.

Related papers

• Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations [48.82168723932981]
  We introduce em MultiBully-Ex, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes. A Contrastive Language-Image Pretraining (CLIP) approach has been proposed for visual and textual explanation of a meme.
  arXiv  Detail & Related papers  (2024-01-18T11:24:30Z)
• Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation [52.3781496277104]
  Cyberbullying has become a big issue with the popularity of different social media networks and online communication apps. Recent laws like "right to explanations" of General Data Protection Regulation have spurred research in developing interpretable models. We develop first interpretable multi-task model called em mExCB for automatic cyberbullying detection from code-mixed languages.
  arXiv  Detail & Related papers  (2024-01-17T07:36:22Z)
• Deep Learning Based Cyberbullying Detection in Bangla Language [0.0]
  This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali. A two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying.
  arXiv  Detail & Related papers  (2024-01-07T04:58:59Z)
• A Secure Open-Source Intelligence Framework For Cyberbullying Investigation [0.0]
  This paper proposes an open-source intelligence pipeline using data from Twitter to track keywords relevant to cyberbullying in social media. An OSINT dashboard with real-time monitoring empowers law enforcement to swiftly take action, protect victims, and make significant strides toward creating a safer online environment.
  arXiv  Detail & Related papers  (2023-07-27T23:03:57Z)
• 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)
• Cyberbullying in Text Content Detection: An Analytical Review [0.0]
  Online social networks increase the user's exposure to life-threatening situations such as suicide, eating disorder, cybercrime, compulsive behavior, anxiety, and depression. To tackle the issue of cyberbullying, most existing literature focuses on developing approaches to identifying factors and understanding the textual factors associated with cyberbullying. This paper conducts a comprehensive literature review to provide an understanding of cyberbullying detection.
  arXiv  Detail & Related papers  (2023-03-18T21:23:06Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• DISARM: Detecting the Victims Targeted by Harmful Memes [49.12165815990115]
  DISARM is a framework that uses named entity recognition and person identification to detect harmful memes. We show that DISARM significantly outperforms ten unimodal and multimodal systems. It can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
  arXiv  Detail & Related papers  (2022-05-11T19:14:26Z)
• Res-CNN-BiLSTM Network for overcoming Mental Health Disturbances caused due to Cyberbullying through Social Media [3.1871776847712523]
  cyberbullying is done on the basis of Religion, Ethnicity, Age and Gender. Social media is the medium and it generates massive form of data in textual form.
  arXiv  Detail & Related papers  (2022-04-20T18:40:39Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification [4.945634077636197]
  We study the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.
  arXiv  Detail & Related papers  (2020-04-04T00:35:16Z)

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.