arXiv subjects

Deborah L. Hall

Publications and source records attributed to Deborah L. Hall.

5 recordsLinked to original sources

arXiv2025

Network Analysis of Cyberbullying Interactions on Instagram

Cyberbullying continues to grow in prevalence and its impact is felt by thousands worldwide. This study seeks a network science perspective on cyberbullying interaction patterns on the popular photo and video-sharing platform, Instagram. Using an annotated cyberbullying dataset containing over 400 Instagram posts, we outline a set of heuristics for building Session Graphs, where nodes represent users and their cyberbullying role, and edges represent their exchanged communications via comments. Over these graphs, we compute the Bully Score, a measure of the net malice introduced by bullies as they attack victims (attacks minus pushback), and the Victim Score, a measure of the net support victims receive from their defenders (support minus attacks). Utilizing small subgraph (motif) enumeration, our analysis uncovers the most common interaction patterns over all cyberbullying sessions. We also explore the prevalence of specific motif patterns across different ranges of Bully and Victim Scores. We find that a majority of cyberbullying sessions have negative Victim Scores (attacks outweighing support), while the Bully Score distribution has a slight positive skew (attacks outweighing pushback). We also observe that while bullies are the most common role in motifs, defenders are also consistently present. This suggests that bullying mitigation is a recurring structural feature of many interactions. To the best of our knowledge, this is the first study to explore this granular scale of network interactions using human annotations at the session and comment levels on Instagram.

cs.SI↗

arXiv2024

Identifying Cyberbullying Roles in Social Media

Social media has revolutionized communication, allowing people worldwide to connect and interact instantly. However, it has also led to increases in cyberbullying, which poses a significant threat to children and adolescents globally, affecting their mental health and well-being. It is critical to accurately detect the roles of individuals involved in cyberbullying incidents to effectively address the issue on a large scale. This study explores the use of machine learning models to detect the roles involved in cyberbullying interactions. After examining the AMiCA dataset and addressing class imbalance issues, we evaluate the performance of various models built with four underlying LLMs (i.e., BERT, RoBERTa, T5, and GPT-2) for role detection. Our analysis shows that oversampling techniques help improve model performance. The best model, a fine-tuned RoBERTa using oversampled data, achieved an overall F1 score of 83.5%, increasing to 89.3% after applying a prediction threshold. The top-2 F1 score without thresholding was 95.7%. Our method outperforms previously proposed models. After investigating the per-class model performance and confidence scores, we show that the models perform well in classes with more samples and less contextual confusion (e.g., Bystander Other), but struggle with classes with fewer samples (e.g., Bystander Assistant) and more contextual ambiguity (e.g., Harasser and Victim). This work highlights current strengths and limitations in the development of accurate models with limited data and complex scenarios.

cs.LG↗

arXiv2024

Evaluating LLMs Capabilities Towards Understanding Social Dynamics

Social media discourse involves people from different backgrounds, beliefs, and motives. Thus, often such discourse can devolve into toxic interactions. Generative Models, such as Llama and ChatGPT, have recently exploded in popularity due to their capabilities in zero-shot question-answering. Because these models are increasingly being used to ask questions of social significance, a crucial research question is whether they can understand social media dynamics. This work provides a critical analysis regarding generative LLM's ability to understand language and dynamics in social contexts, particularly considering cyberbullying and anti-cyberbullying (posts aimed at reducing cyberbullying) interactions. Specifically, we compare and contrast the capabilities of different large language models (LLMs) to understand three key aspects of social dynamics: language, directionality, and the occurrence of bullying/anti-bullying messages. We found that while fine-tuned LLMs exhibit promising results in some social media understanding tasks (understanding directionality), they presented mixed results in others (proper paraphrasing and bullying/anti-bullying detection). We also found that fine-tuning and prompt engineering mechanisms can have positive effects in some tasks. We believe that a understanding of LLM's capabilities is crucial to design future models that can be effectively used in social applications.

cs.LG↗

arXiv2024

Detecting LGBTQ+ Instances of Cyberbullying

Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. The aim of this study is to compare the efficacy of several transformer models in identifying cyberbullying targeting LGBTQ+ individuals. We seek to determine the relative merits and demerits of these existing methods in addressing complex and subtle kinds of cyberbullying by assessing their effectiveness with real social media data.

cs.LG↗

arXiv2020

Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model

Social media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing cyberbullying detection methods are supervised and, thus, have two key drawbacks: (1) The data labeling process is often time-consuming and labor-intensive; (2) Current labeling guidelines may not be generalized to future instances because of different language usage and evolving social networks. To address these limitations, this work introduces a principled approach for unsupervised cyberbullying detection. The proposed model consists of two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time. (2) A multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The proposed model jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. Our core contribution is an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.

cs.SI↗