Exploration and Evaluation of Bias in Cyberbullying Detection with Machine Learning

• URL: http://arxiv.org/abs/2412.00609v1
• Date: Sat, 30 Nov 2024 23:18:49 GMT
• Title: Exploration and Evaluation of Bias in Cyberbullying Detection with Machine Learning
• Authors: Andrew Root, Liam Jakubowski, Mounika Vanamala,
• Abstract summary: This study uses three popular cyberbullying datasets to explore the effects of data, how it's collected, and how it's labeled, on the resulting machine learning models. As hypothesized, the models have a significant drop in the Macro F1 Score, with an average drop of 0.222.
• Score: 0.0
• License:
• Abstract: It is well known that the usefulness of a machine learning model is due to its ability to generalize to unseen data. This study uses three popular cyberbullying datasets to explore the effects of data, how it's collected, and how it's labeled, on the resulting machine learning models. The bias introduced from differing definitions of cyberbullying and from data collection is discussed in detail. An emphasis is made on the impact of dataset expansion methods, which utilize current data points to fetch and label new ones. Furthermore, explicit testing is performed to evaluate the ability of a model to generalize to unseen datasets through cross-dataset evaluation. As hypothesized, the models have a significant drop in the Macro F1 Score, with an average drop of 0.222. As such, this study effectively highlights the importance of dataset curation and cross-dataset testing for creating models with real-world applicability. The experiments and other code can be found at https://github.com/rootdrew27/cyberbullying-ml.

Related papers

• A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data [9.57464542357693]
  This paper demonstrates that model-centric evaluations are biased, as real-world modeling pipelines often require dataset-specific preprocessing and feature engineering. We select 10 relevant datasets from Kaggle competitions and implement expert-level preprocessing pipelines for each dataset. After dataset-specific feature engineering, model rankings change considerably, performance differences decrease, and the importance of model selection reduces.
  arXiv  Detail & Related papers  (2024-07-02T09:54:39Z)
• On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection [50.38534263407915]
  Network Intrusion Detection Systems (NIDS) are a fundamental tool in cybersecurity. Their ability to generalize across diverse networks is a critical factor in their effectiveness and a prerequisite for real-world applications. In this study, we conduct a comprehensive analysis on the generalization of machine-learning-based NIDS through an extensive experimentation in a cross-dataset framework.
  arXiv  Detail & Related papers  (2024-02-15T14:39:58Z)
• Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics [0.0]
  This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted.
  arXiv  Detail & Related papers  (2023-12-27T01:09:11Z)
• Machine Unlearning for Causal Inference [0.6621714555125157]
  It is important to enable the model to forget some of its learning/captured information about a given user (machine unlearning) This paper introduces the concept of machine unlearning for causal inference, particularly propensity score matching and treatment effect estimation. The dataset used in the study is the Lalonde dataset, a widely used dataset for evaluating the effectiveness of job training programs.
  arXiv  Detail & Related papers  (2023-08-24T17:27:01Z)
• Stubborn Lexical Bias in Data and Models [50.79738900885665]
  We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data. We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations. Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
  arXiv  Detail & Related papers  (2023-06-03T20:12:27Z)
• Hidden Biases in Unreliable News Detection Datasets [60.71991809782698]
  We show that selection bias during data collection leads to undesired artifacts in the datasets. We observed a significant drop (>10%) in accuracy for all models tested in a clean split with no train/test source overlap. We suggest future dataset creation include a simple model as a difficulty/bias probe and future model development use a clean non-overlapping site and date split.
  arXiv  Detail & Related papers  (2021-04-20T17:16:41Z)
• A Note on Data Biases in Generative Models [16.86600007830682]
  We investigate the impact of dataset quality on the performance of generative models. We show how societal biases of datasets are replicated by generative models. We present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes.
  arXiv  Detail & Related papers  (2020-12-04T10:46:37Z)
• Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles [66.15398165275926]
  We propose a method that can automatically detect and ignore dataset-specific patterns, which we call dataset biases. Our method trains a lower capacity model in an ensemble with a higher capacity model. We show improvement in all settings, including a 10 point gain on the visual question answering dataset.
  arXiv  Detail & Related papers  (2020-11-07T22:20:03Z)
• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics [118.75207687144817]
  We introduce Data Maps, a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
  arXiv  Detail & Related papers  (2020-09-22T20:19:41Z)
• Are Labels Always Necessary for Classifier Accuracy Evaluation? [28.110519483540482]
  We aim to estimate the classification accuracy on unlabeled test datasets. We construct a meta-dataset comprised of datasets generated from the original images. As the classification accuracy of the model on each sample (dataset) is known from the original dataset labels, our task can be solved via regression.
  arXiv  Detail & Related papers  (2020-07-06T17:45:39Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.