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1. Personalized Learning for Cyberbullying Detection

   Cheng, Lu; Silva, Yasin N.; Hall, Deborah L.; Liu, Huan (July 2018, Doctoral Consortium of the International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation)

   Cyberbullying has become one of the most pressing online risks for adolescents and has raised serious concerns in society. Traditional efforts are primarily devoted to building a single generic classification model for all users to differentiate bullying behaviors from the normal content [6, 3, 1, 2, 4]. Despite its empirical success, these models treat users equally and inevitably ignore the idiosyncrasies of users. Recent studies from psychology and sociology suggest that the occurrence of cyberbullying has a strong connection with the personality of victims and bullies embedded in the user-generated content, and the peer influence from like-minded users. In this paper, we propose a personalized cyberbullying detection framework PI-Bully with peer influence in a collaborative environment to tailor the prediction for each individual. In particular, the personalized classifier of each individual consists of three components: a global model that captures the commonality shared by all users, a personalized model that expresses the idiosyncratic personality of each specific user, and a third component that encodes the peer influence received from like-minded users. Most of the existing methods adopt a two-stage approach: they first apply feature engineering to capture the cyberbullying patterns and then employ machine learning classifiers to detect cyberbullying behaviors. However, building a personalized cyberbullying detection framework that is customized to each individual remains a challenging task, in large part because: (1) Social media data is often sparse, noisy and high-dimensional (2) It is important to capture the commonality shared by all users as well as idiosyncratic aspects of the personality of each individual for automatic cyberbullying detection; (3) In reality, a potential victim of cyberbullying is often influenced by peers and the influences from different users could be quite diverse. Hence, it is imperative to develop a way to encode the diversity of peer influence for cyberbullying detection. To summarize, we study a novel problem of personalized cyberbullying detection with peer influence in a collaborative environment, which is able to jointly model users' common features, unique personalities and peer influence to identify cyberbullying cases. 

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2. Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks

   https://doi.org/10.1145/3441141  

   Cheng, Lu; Guo, Ruocheng; Silva, Yasin N.; Hall, Deborah; Liu, Huan (April 2021, ACM/IMS Transactions on Data Science)

   null (Ed.)

   Cyberbullying is rapidly becoming one of the most serious online risks for adolescents. This has motivated work on machine learning methods to automate the process of cyberbullying detection, which have so far mostly viewed cyberbullying as one-off incidents that occur at a single point in time. Comparatively less is known about how cyberbullying behavior occurs and evolves over time. This oversight highlights a crucial open challenge for cyberbullying-related research, given that cyberbullying is typically defined as intentional acts of aggression via electronic communication that occur repeatedly and persistently . In this article, we center our discussion on the challenge of modeling temporal patterns of cyberbullying behavior. Specifically, we investigate how temporal information within a social media session, which has an inherently hierarchical structure (e.g., words form a comment and comments form a session), can be leveraged to facilitate cyberbullying detection. Recent findings from interdisciplinary research suggest that the temporal characteristics of bullying sessions differ from those of non-bullying sessions and that the temporal information from users’ comments can improve cyberbullying detection. The proposed framework consists of three distinctive features: (1) a hierarchical structure that reflects how a social media session is formed in a bottom-up manner; (2) attention mechanisms applied at the word- and comment-level to differentiate the contributions of words and comments to the representation of a social media session; and (3) the incorporation of temporal features in modeling cyberbullying behavior at the comment-level. Quantitative and qualitative evaluations are conducted on a real-world dataset collected from Instagram, the social networking site with the highest percentage of users reporting cyberbullying experiences. Results from empirical evaluations show the significance of the proposed methods, which are tailored to capture temporal patterns of cyberbullying detection. 

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3. Improving Cyberbullying Detection with User Interaction

   https://doi.org/10.1145/3442381.3449828  

   Ge, S.; Cheng, L.; Liu, H. (April 2021, The Web Conference)

   null (Ed.)

   Cyberbullying, identified as intended and repeated online bullying behavior, has become increasingly prevalent in the past few decades. Despite the significant progress made thus far, the focus of most existing work on cyberbullying detection lies in the independent content analysis of different comments within a social media session. We argue that such leading notions of analysis suffer from three key limitations: they overlook the temporal correlations among different comments; they only consider the content within a single comment rather than the topic coherence across comments; they remain generic and exploit limited interactions between social media users. In this work, we observe that user comments in the same session may be inherently related, e.g., discussing similar topics, and their interaction may evolve over time. We also show that modeling such topic coherence and temporal interaction are critical to capture the repetitive characteristics of bullying behavior, thus leading to better predicting performance. To achieve the goal, we first construct a unified temporal graph for each social media session. Drawing on recent advances in graph neural network, we then 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. Our code is released to public. 

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4. You Don't Know How I Feel: Insider-Outsider Perspective Gaps in Cyberbullying Risk Detection

   https://doi.org/https://ojs.aaai.org/index.php/ICWSM/article/view/18061  

   Kim, S. (May 2021, the Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2021))

   null (Ed.)

   Cyberbullying is a prevalent concern within social computing research that has led to the development of several supervised machine learning (ML) algorithms for automated risk detection. A critical aspect of ML algorithm development is how to establish ground truth that is representative of the phenomenon of interest in the real world. Often, ground truth is determined by third-party annotators (i.e., “outsiders”) who are removed from the situational context of the interaction; therefore, they cannot fully understand the perspective of the individuals involved (i.e., “insiders”). To understand the extent of this problem, we compare “outsider” versus “insider” perspectives when annotating 2,000 posts from an online peer-support platform. We interpolate this analysis to a corpus containing over 2.3 million posts on bullying and related topics, and reveal significant gaps in ML models that use third-party annotators to detect bullying incidents. Our results indicate that models based on the insiders’ perspectives yield a significantly higher recall in identifying bullying posts and are able to capture a range of explicit and implicit references and linguistic framings, including person-specific impressions of the incidents. Our study highlights the importance of incorporating the victim’s point of view in establishing effective tools for cyberbullying risk detection. As such, we advocate for the adoption of human-centered and value-sensitive approaches for algorithm development that bridge insider-outsider perspective gaps in a way that empowers the most vulnerable. 

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5. Evaluating LLMs Capabilities Towards Understanding Social Dynamics

   Tahir, A; Cheng, L; Sandoval, M; Silva, Y N; Hall, D; Liu, H (September 2024, The 2024 International Conference on Advances in Social Networks Analysis and Mining (ASONAM))

   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. 

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