The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in todays cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance and generalisation issues. In recent years, large language models (LLMs) like BERT and RoBERTa have achieved state-of-the-art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively for CB detection. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed that RoBERTa outperformed other models.
Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modeling and understanding the two defining characteristics of cyberbullying: repetitive behavior and power imbalance. In this survey paper, we define the Session-based Cyberbullying Detection framework that encapsulates the different steps and challenges of the problem. Based on this framework, we provide a comprehensive overview of se…
In most cases, word embeddings are learned only from raw tokens or in some cases, lemmas. This includes pre-trained language models like BERT. To investigate on the potential of capturing deeper relations between lexical items and structures and to filter out redundant information, we propose to preserve the morphological, syntactic and other types of linguistic information by combining them with the raw tokens or lemmas. This means, for example, including parts-of-speech or dependency information within the used lexical features. The word embeddings can then be trained on the combinations ins…
Cyberbullying Detection via Aggression-Enhanced Prompting
NICE Research Group & Institute for People-Centred AI, School of Computer Science & Electronic Engineering, University of Surrey, UK（NICE研究组及以人为本的人工智能研究所、计算机科学与电子工程学院、萨里大学）
Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based Machine Learning Framework
Information Technologies Group - atlanTTic University of Vigo（信息科技组 - atlanTTic大学维戈分校）
Evaluating LLMs Capabilities Towards Understanding Social Dynamics
School of Computing and AI, Arizona State University（亚利桑那州立大学计算与人工智能学院）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Loyola University Chicago（芝加哥洛约拉大学）；School of Social & Behavioral Sciences, Arizona State University（亚利桑那州立大学社会与行为科学学院）
Chinese Cyberbullying Detection: Dataset, Method, and Validation
School of Information Engineering, Yangzhou University（扬州大学信息工程学院）；Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education（大数据知识工程重点实验室（合肥工业大学））；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）
Kitami Institute of Technology（北见工业大学）；Samurai Labs（武士实验室）；Fido Voice（Fido语音公司）；AGH University of Science and Technology（AGH科技大学）
In this research, we study the change in the performance of machine learning (ML) classifiers when various linguistic preprocessing methods of a dataset were used, with the specific focus on linguistically-backed embeddings in Convolutional Neural Networks (CNN). Moreover, we study the concept of Feature Density and confirm its potential to comparatively predict the performance of ML classifiers, including CNN. The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The dataset was re-annotated by objective experts (psychologist…
Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms, proposed models tend to carry and reinforce unintended social biases. In this study, we try to answer the research question of "Can we mitigate the unintended bias of cyberbullying detection models by guiding the model training with fairness constraints?". For this purpose, we propose a model training scheme that can employ fairness constraints and validate ou…
Institute of Data Science（数据科学研究所）；National Cheng Kung University（国立成功大学）
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-networ…
Cyberbullying is a problem in today's ubiquitous online communities. Filtering it out of online conversations has proven a challenge, and efforts have led to the creation of many different datasets, all offered as resources to train classifiers. Through these datasets, we will explore the variety of definitions of cyberbullying behaviors and the impact of these differences on the portability of one classifier to another community. By analyzing the similarities between datasets, we also gain insight on the generalization power of the classifiers trained from them. A study of ensemble models com…
Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, cyberbullying amongst children and other vulnerable groups are only growing over online classes, and increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure minors' safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism,…
Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety
Faculty of Computer Science, University of New Brunswick（新不伦瑞克大学计算机科学学院）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
Kitami Institute of Technology（北见工业大学）；Samurai Labs/Fido Voice；AGH University of Science and Technology（AGH科技大学）
The research described in this paper concerns automatic cyberbullying detection in social media. There are two goals to achieve: building a gold standard cyberbullying detection dataset and measuring the performance of the Samurai cyberbullying detection system. The Formspring dataset provided in a Kaggle competition was re-annotated as a part of the research. The annotation procedure is described in detail and, unlike many other recent data annotation initiatives, does not use Mechanical Turk for finding people willing to perform the annotation. The new annotation compared to the old one seem…
Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms have received less attention. We propose XP-CB, a novel cross-platform framework based on Transformers and adversarial learning. XP-CB can enhance a Transformer leveraging unlabelled data from the source and target platforms to come up with a common representation while preventing platform-specific training. To validate our proposed framework, we experiment on cyberbullying datasets from thr…
A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection
Department of Computer and Communication Engineering, International Islamic University Chittagong, Chattogram, Bangladesh（国际伊斯兰大学奇塔格ong分校计算机与通信工程系）；Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh（诺阿克利科学与技术大学信息与通信工程系）；mPower Social Enterprise（mPower社会企业）；Department of Software Engineering, Daffodil International University, Bangladesh（达福尔国际大学软件工程系）；School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA（密西西比大学南方分校计算机科学与工程学院）
The University of Adelaide（阿德莱德大学）；University of Moratuwa（莫拉图瓦大学）
Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5(HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistilBERT has achieved 0.73 and 0.74 of F1 score in the task of classi…
Kitami Institute of Technology（北见工业大学）；Samurai Labs（武士实验室）
In this research. we analyze the potential of Feature Density (HD) as a way to comparatively estimate machine learning (ML) classifier performance prior to training. The goal of the study is to aid in solving the problem of resource-intensive training of ML models which is becoming a serious issue due to continuously increasing dataset sizes and the ever rising popularity of Deep Neural Networks (DNN). The issue of constantly increasing demands for more powerful computational resources is also affecting the environment, as training large-scale ML models are causing alarmingly-growing amounts o…
Cyberbullying or Online harassment detection on social media for various major languages is currently being given a good amount of focus by researchers worldwide. Being the seventh most speaking language in the world and increasing usage of online platform among the Bengali speaking people urge to find effective detection technique to handle the online harassment. In this paper, 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 f…
University of Moratuwa（莫拉图瓦大学）；The University of Adelaide（阿德莱德大学）
Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our …
Ghent University（根特大学）；University of Antwerp（安特卫普大学）
While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullyi…
Indian Institute of Technology, Guwahati（印度理工学院古瓦哈提分校）
Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring…
Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks
School of Information Technology University of Cincinnati Cincinnati, Ohio, USA；School of Human Services University of Cincinnati Cincinnati, Ohio, USA
Deep Learning Based Cyberbullying Detection in Bangla Language
University of Science and Technology Chittagong（吉大港科技大学）；American International University-Bangladesh（孟加拉国美国国际大学）；Xiamen University Malaysia（厦门大学马来西亚分校）；Wrexham University（雷克瑟姆大学）；University of South Wales（南威尔士大学）
本研究提出基于两层 Bi-LSTM 的孟加拉语网络欺凌检测方法，在 12282 条多平台评论数据集上取得最高 95.08% 准确率和 95.23% F1 分数。
Cyberbullying is a disturbing online misbehaviour with troubling consequences. It appears in different forms, and in most of the social networks, it is in textual format. Automatic detection of such incidents requires intelligent systems. Most of the existing studies have approached this problem with conventional machine learning models and the majority of the developed models in these studies are adaptable to a single social network at a time. In recent studies, deep learning based models have found their way in the detection of cyberbullying incidents, claiming that they can overcome the lim…
Emory University（埃默里大学）；USC Information Sciences Institute（南加州大学信息科学研究所）
Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced p…
Kitami Institute of Technology（北见工业大学）；AGH University of Science and Technology（AGH科技大学）；Samurailabs
We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods in order to estimate dataset complexity, which in turn is used to comparatively estimate the potential performance of machine learning (ML) classifiers prior to any training. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments iterations. This way we can optimize the resource-intensive training of ML models which is becoming a serious issue due to the increases in available dataset sizes and the ever rising popularit…
Mental Health Disturbance has many reasons and cyberbullying is one of the major causes that does exploitation using social media as an instrument. The cyberbullying is done on the basis of Religion, Ethnicity, Age and Gender which is a sensitive psychological issue. This can be addressed using Natural Language Processing with Deep Learning, since social media is the medium and it generates massive form of data in textual form. Such data can be leveraged to find the semantics and derive what type of cyberbullying is done and who are the people involved for early measures. Since deriving semant…
CSAI, Tilburg University（蒂尔堡大学CSAI）；CLiPS, University of Antwerp（安特卫普大学CLiPS）；LT$ˆ3$, Ghent University（根特大学LT$ˆ3$）
The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability. In this paper, we further illustrate these issues, as we (i) evaluate many publicly available resources for this task and demonstrate difficulties w…
Doxing refers to the practice of disclosing sensitive personal information about a person without their consent. This form of cyberbullying is an unpleasant and sometimes dangerous phenomenon for online social networks. Although prior work exists on automated identification of other types of cyberbullying, a need exists for methods capable of detecting doxing on Twitter specifically. We propose and evaluate a set of approaches for automatically detecting second- and third-party disclosures on Twitter of sensitive private information, a subset of which constitutes doxing. We summarize our findi…
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心）；The Hong Kong University of Science and Technology（香港科技大学）
Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models. Using a pre-trained language model such as BERT, we can effectively learn the representations for noisy text in social media. Besides, to boost the performance of offensive language detection, we leverage the supervision signals from other related tasks. In the OffensEval-2020 competition, our model achieves 91.51% F1 score in Engli…
