The rapid growth of video platforms has transformed information dissemination and led to an explosion of multimedia content. However, this widespread reach also introduces risks, as some users exploit these platforms to spread hate speech, which is often concealed through complex rhetoric, making hateful video detection a critical challenge. Existing detection methods rely heavily on unimodal analysis or simple feature fusion, struggling to capture cross-modal interactions and reason through implicit hate in sarcasm and metaphor. To address these limitations, we propose HVGuard, the first reasoning-based hateful video detection framework with multimodal large language models (MLLMs). Our approach integrates Chain-of-Thought (CoT) reasoning to enhance multimodal interaction modeling and implicit hate interpretation. Additionally, we design a Mixture-of-Experts (MoE) network for efficient multimodal fusion and final decision-making. The framework is modular and extensible, allowing flexible integration of different MLLMs and encoders. Experimental results demonstrate that HVGuard outperforms all existing advanced detection tools, achieving an improvement of 6.88% to 13.13% in accuracy and 9.21% to 34.37% in M-F1 on two public datasets covering both English and Chinese.

The growing integration of social media and video games in adolescents’ daily lives has heightened the risk of cyberbullying, contributing to severe mental health challenges such as depression and suicidal ideation. Traditional moderation techniques, including manual review and rule-based filters, often fail to capture contextual nuances. Existing machine learning models also face limitations due to their reliance on large labeled datasets and lack of cross-platform adaptability. Our experiments further reveal that models trained on social media content perform poorly on video game data and vice versa, leading to inefficient moderation. To address these challenges, we propose a novel approach that leverages Large Language Models (LLMs) with Chain-of-Thought (CoT) reasoning for cyberbullying detection across both domains. This method eliminates the need for extensive training data while significantly improving accuracy. It achieves 90.3% and 91.1% accuracy on social media and video game datasets, respectively, offering a scalable and resource-efficient solution for cyberbullying moderation.

Cyberbullying has rapidly evolved with the evolution of online platforms, transcending traditional text-based forms to include images and other multimedia content. Two major challenges are identified in detecting cyberbullying images: recognizing cyberbullying-related visual factors and addressing the context-dependent nature of such images. In this paper, we conduct a comprehensive investigation of the ability of Large Vision-Language Models (LVLMs) to evaluate visual factors related to cyberbullying, and to interpret the context-dependent nature of such images. Furthermore, by proposing a diverse set of prompting strategies, we optimize LVLMs for cyberbullying image detection. In particular, through our carefully crafted Chain-of-Thought (CoT) methodology, we guide the model through structured reasoning pathways to interpret complex visual factors and account for their context. Our results show that the structured reasoning pathways significantly enhance model performance, achieving state-of-the-art accuracy and precision while remaining efficient by eliminating the need for any extensive training process.

Online hate is an escalating problem that negatively impacts the lives of Internet users, and is also subject to rapid changes due to evolving events, resulting in new waves of online hate that pose a critical threat. Detecting and mitigating these new waves present two key challenges: it demands reasoning-based complex decision-making to determine the presence of hateful content, and the limited availability of training samples hinders updating the detection model. To address this critical issue, we present a novel framework called HateGuard for effectively moderating new waves of online hate. HateGuard employs a reasoning-based approach that leverages the recently introduced chain-of-thought (CoT) prompting technique, harnessing the capabilities of large language models (LLMs). HateGuard further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate. To demonstrate the effectiveness of our approach, we compile a new dataset consisting of tweets related to three recently witnessed new waves: the 2022 Russian invasion of Ukraine, the 2021 insurrection of the US Capitol, and the COVID-19 pandemic. Our studies reveal crucial longitudinal patterns in these new waves concerning the evolution of events and the pressing need for techniques to rapidly update existing moderation tools to counteract them. Comparative evaluations against state-of-the-art approaches illustrate the superiority of our framework, showcasing a substantial 10.59% to 88% improvement in detecting the three new waves of online hate. Our work highlights the severe threat posed by the emergence of new waves of online hate and represents a paradigm shift in addressing this threat practically.

Recent studies regarding the COVID-19 pandemic have revealed the widespread propagation of hateful content during this period. While significant research has focused on COVID-19-related online hate in text (e.g., text-based tweets), the role of memes in propagating online hate during the pandemic has been largely overlooked. Memes are a popular mechanism used by Internet users to convey their thoughts and opinions on a variety of topics. However, memes have emerged as an important mechanism through which ideologically potent and hateful content spreads on social media platforms. In this work, we focus on investigating the role of memes in the propagation of online hate during the COVID-19 pandemic. We first collect a novel dataset of 4,001 COVID-19-related hateful memes and their replies over a 3-year period from Twitter. Then, we carry out the first large-scale investigation into the impact of these memes on Twitter users, by studying the psychological reactions of Twitter users to these memes using various text analysis methods. We find that COVID-19-related hateful memes have a significantly greater negative impact on Twitter users in comparison to text-based hateful tweets, and increasing negativity towards such memes over the 3-year period. Our new dataset of COVID-19-related hateful memes and findings from our work pave the way for studying the dissemination and moderation of COVID-19-related online hate through the medium of memes.

Hate speech has emerged as a major problem plaguing our social spaces today. While there have been significant efforts to address this problem, existing methods are still significantly limited in effectively detecting hate speech online. A major limitation of existing methods is that hate speech detection is a highly contextual problem, and these methods cannot fully capture the context of hate speech to make accurate predictions. Recently, large language models (LLMs) have demonstrated state-of-the-art performance in several natural language tasks. LLMs have undergone extensive training using vast amounts of natural language data, enabling them to grasp intricate contextual details. Hence, they could be used as knowledge bases for context-aware hate speech detection. However, a fundamental problem with using LLMs to detect hate speech is that there are no studies on effectively prompting LLMs for context-aware hate speech detection. In this study, we conduct a large-scale study of hate speech detection, employing five established hate speech datasets. We discover that LLMs not only match but often surpass the performance of current benchmark machine learning models in identifying hate speech. By proposing four diverse prompting strategies that optimize the use of LLMs in detecting hate speech. Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech by fully utilizing the knowledge base in LLMs, significantly outperforming existing techniques. Furthermore, although LLMs can provide a rich knowledge base for the contextual detection of hate speech, suitable prompting strategies play a crucial role in effectively leveraging this knowledge base for efficient detection.