HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media

• URL: http://arxiv.org/abs/2010.04576v1
• Date: Fri, 9 Oct 2020 13:44:34 GMT
• Title: HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media
• Authors: Hsin-Yu Chen, Cheng-Te Li
• Abstract summary: We propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors.
• Score: 11.443698975923176
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying. In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors. Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.

Related papers

• 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)
• Investigating Human-Identifiable Features Hidden in Adversarial Perturbations [54.39726653562144]
  Our study explores up to five attack algorithms across three datasets. We identify human-identifiable features in adversarial perturbations. Using pixel-level annotations, we extract such features and demonstrate their ability to compromise target models.
  arXiv  Detail & Related papers  (2023-09-28T22:31:29Z)
• 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)
• CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network [52.85130555886915]
  CoSyn is a context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations. We show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.
  arXiv  Detail & Related papers  (2023-03-02T17:30:43Z)
• Session-based Cyberbullying Detection in Social Media: A Survey [16.39344929765961]
  We define the Session-based Cyberbullying Detection framework that encapsulates the different steps and challenges of the problem. Our review leads us to propose evidence-based criteria for a set of best practices to create session-based cyberbullying datasets.
  arXiv  Detail & Related papers  (2022-07-14T18:56:54Z)
• Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection [54.83707803301847]
  The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The study confirmed the effectiveness of Neural Networks in cyberbullying detection and the correlation between classifier performance and Feature Density.
  arXiv  Detail & Related papers  (2022-06-04T03:17:15Z)
• Searching for the Essence of Adversarial Perturbations [73.96215665913797]
  We show that adversarial perturbations contain human-recognizable information, which is the key conspirator responsible for a neural network's erroneous prediction. This concept of human-recognizable information allows us to explain key features related to adversarial perturbations.
  arXiv  Detail & Related papers  (2022-05-30T18:04:57Z)
• Improving Cyberbully Detection with User Interaction [34.956581421295]
  We propose a principled graph-based approach for modeling the temporal dynamics and topic coherence throughout user interactions. We empirically evaluate the effectiveness of our approach with the tasks of session-level bullying detection and comment-level case study.
  arXiv  Detail & Related papers  (2020-11-01T08:47:33Z)
• Understanding the Role of Individual Units in a Deep Neural Network [85.23117441162772]
  We present an analytic framework to systematically identify hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes.
  arXiv  Detail & Related papers  (2020-09-10T17:59:10Z)
• 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.