Deep Learning Based Cyberbullying Detection in Bangla Language

• URL: http://arxiv.org/abs/2401.06787v1
• Date: Sun, 7 Jan 2024 04:58:59 GMT
• Title: Deep Learning Based Cyberbullying Detection in Bangla Language
• Authors: Sristy Shidul Nath, Razuan Karim and Mahdi H. Miraz
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Internet is currently the largest platform for global communication including expressions of opinions, reviews, contents, images, videos and so forth. Moreover, social media has now become a very broad and highly engaging platform due to its immense popularity and swift adoption trend. Increased social networking, however, also has detrimental impacts on the society leading to a range of unwanted phenomena, such as online assault, intimidation, digital bullying, criminality and trolling. Hence, cyberbullying has become a pervasive and worrying problem that poses considerable psychological and emotional harm to the people, particularly amongst the teens and the young adults. In order to lessen its negative effects and provide victims with prompt support, a great deal of research to identify cyberbullying instances at various online platforms is emerging. In comparison to other languages, Bangla (also known as Bengali) has fewer research studies in this domain. This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali, using a dataset of 12282 versatile comments from multiple social media sites. In this study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying, using a variety of optimisers as well as 5-fold cross validation. To evaluate the functionality and efficacy of the proposed system, rigorous assessment and validation procedures have been employed throughout the project. The results of this study reveals that the proposed model's accuracy, using momentum-based stochastic gradient descent (SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31% in 5-fold cross validation.

Related papers

• Detecting LGBTQ+ Instances of Cyberbullying [3.5723815685584013]
  Cyberbullying poses a significant threat to adolescents globally. The LGBTQ+ community is particularly at risk, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. It is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members.
  arXiv  Detail & Related papers  (2024-09-18T18:49:55Z)
• Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks [0.0]
  This paper focuses on detecting cyberbullying in adversarial attack content within social networking site text data, specifically emphasizing hate speech. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.15%, and 91% respectively.
  arXiv  Detail & Related papers  (2024-05-30T21:44:15Z)
• Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying [1.8749305679160366]
  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.
  arXiv  Detail & Related papers  (2024-04-01T20:41:28Z)
• 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)
• Locally Differentially Private Document Generation Using Zero Shot Prompting [61.20953109732442]
  We propose a locally differentially private mechanism called DP-Prompt to counter author de-anonymization attacks. When DP-Prompt is used with a powerful language model like ChatGPT (gpt-3.5), we observe a notable reduction in the success rate of de-anonymization attacks.
  arXiv  Detail & Related papers  (2023-10-24T18:25:13Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
  Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
  arXiv  Detail & Related papers  (2022-12-27T16:08:49Z)
• 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)
• Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking [58.14267480293575]
  We propose a simple yet effective online learning approach for few-shot online adaptation without requiring offline training. It allows an in-built memory retention mechanism for the model to remember the knowledge about the object seen before. We evaluate our approach based on two networks in the online learning families for tracking, i.e., multi-layer perceptrons in RT-MDNet and convolutional neural networks in DiMP.
  arXiv  Detail & Related papers  (2021-12-28T06:51:18Z)
• Cyberbullying Detection Using Deep Neural Network from Social Media Comments in Bangla Language [0.0]
  We have proposed binary and multiclass classification model using hybrid neural network for bully expression detection in Bengali language. We have used 44,001 users comments from popular public Facebook pages, which fall into five classes - Non-bully, Sexual, Threat, Troll and Religious. Our binary classification model gives 87.91% accuracy, whereas introducing ensemble technique after neural network for multiclass classification, we got 85% accuracy.
  arXiv  Detail & Related papers  (2021-06-08T16:47:22Z)
• Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media [4.932130498861987]
  In our work, we explore binary classification by using a combination of datasets from various social media platforms. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus.
  arXiv  Detail & Related papers  (2021-04-23T04:20:19Z)

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.