OpenRaiser/Intern-Atlas huggingface dataset:
RAG-Enhanced Evidence Recommendation in Financial Legal Resolutions
Authors: Hsiu-Hung Lee, Chung-Chi Chen, An-Zi Yen
Venue: WWW
Year: 2025

The complexity of legal documents, particularly in financial legal disputes, poses significant challenges for both experts and automated systems. This study introduces a system leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology to recommend key evidence in financial advisor dispute cases. Unlike traditional legal AI tasks focused on outcome prediction, our approach emphasizes supporting judicial reasoning by recommending key evidence relevant to adjudication. We constructed a dataset of 371 annotated cases from Taiwan, spanning 25 years, including claims, judicial opinions, and final judgments, with annotations highlighting key evidence. Using RAG, our system retrieves and generates evidence recommendations grounded in analogous past cases while maintaining temporal and contextual consistency. This methodology enhances judicial efficiency and supports equitable legal decision-making by streamlining the recommendation of critical evidence.
7 Days Later: Analyzing Phishing-Site Lifespan After Detected
Authors: Kiho Lee, Kyungchan Lim, Hyoungshick Kim, Yonghwi Kwon, Doowon Kim
Venue: WWW
Year: 2025

Phishing attacks continue to be a major threat to internet users, causing data breaches, financial losses, and identity theft. This study provides an in-depth analysis of the lifespan and evolution of phishing websites, focusing on their survival strategies and evasion techniques. We analyze 286,237 unique phishing URLs over five months using a custom web crawler based on Puppeteer and Chromium. Our crawler runs on a 30-minute cycle, systematically checking the operational status of phishing websites by collecting their HTTP status codes, screenshots, HTML, and HTTP data. Temporal and survival analyses, along with statistical tests, are used to examine phishing website lifecycles, evolution, and evasion tactics. Our findings show that the average lifespan of phishing websites is 54 hours (2.25 days) with a median of 5.46 hours, indicating rapid takedown of many sites while a subset remains active longer. Interestingly, logistic-themed phishing websites (e.g., USPS) operate within a compressed timeframe (1.76 hours) compared to other brands (e.g., Facebook). We further analyze detection effectiveness using Google Safe Browsing (GSB). We find that GSB detects only 18.4% of phishing websites, taking an average of 4.5 days. Notably, 83.93% of phishing sites are already taken down before GSB detection, meaning GSB requires more prompt detection. Moreover, 16.07% of phishing sites persist beyond this point, surviving for an additional 7.2 days on average, resulting in an average total lifespan of approximately 12 days. We reveal that DNS resolution error is the main cause (67%) of phishing website takedowns. Finally, we uncover that phishing sites with extensive visual changes (more than 100 times) exhibit a median lifespan of 17 days, compared to 1.93 hours for those with minimal modifications. These results highlight the dynamic nature of phishing attacks, the challenges in detection and prevention, and the need for more rapid and comprehensive countermeasures against evolving phishing tactics.
Collaborative Diffusion Model for Recommender System
Authors: Gyuseok Lee, Yaochen Zhu, Hwanjo Yu, Yao Zhou, Jundong Li
Venue: WWW
Year: 2025

Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities. However, existing DR face two central limitations: (i) a trade-off between enhancing generative capacity via noise injection and retaining the loss of personalized information. (ii) the underutilization of rich item-side information. To address these challenges, we present a C ollaborative Diff usion model for Rec ommender System (CDiff4Rec). Specifically, CDiff4Rec generates pseudo-users from item features and leverages collaborative signals from both real and pseudo personalized neighbors identified through behavioral similarity, thereby effectively reconstructing nuanced user preferences. Experimental results on three public datasets show that CDiff4Rec outperforms competitors by effectively mitigating the loss of personalized information through the integration of item content and collaborative signals.
Achieving Personalized Privacy-Preserving Graph Neural Network via Topology Awareness
Authors: Dian Lei, Zijun Song, Yanli Yuan, Chunhai Li, Liehuang Zhu
Venue: WWW
Year: 2025

Graph neural networks (GNNs) with differential privacy (DP) offer a reliable solution for safeguarding sensitive information within graph data. Nonetheless, existing DP-based privacy-preserving GNN learning frameworks generally overlook the local topological heterogeneity of graph nodes and tailor the same privacy budget for all nodes, which may lead to either overprotection or underprotection of some nodes, potentially diminishing model utility or posing privacy leakage risks. To address this issue, we propose a Topology-aware Differential Privacy Graph Neural Network learning framework, termed TDP-GNN, which can achieve personalized privacy protection for each node with improved privacy-utility guarantees. Specifically, TDP-GNN first identifies the topological importance of each node via an adjacency information entropy method. Then, the personalized topology-aware privacy budget is designed to quantify the privacy sensitivity of each node and adaptively allocate the privacy protection strength. Besides, a weighted neighborhood aggregation mechanism is proposed during the message-passing process of GNN training, which can eliminate the impact of the introduced differentiated DP noise on the utility of the GNN model. Since TDP-GNN is based on node-level local DP, it can be seamlessly integrated into any GNN architecture in a plug-and-play manner while ensuring formal privacy guarantees. Theoretical analysis indicates that TDP-GNN achieves ε-differential privacy over the entire graph nodes while providing personalized privacy protection. Extensive experiments demonstrate that TDP-GNN consistently yields better utilities when applied to various GNN architectures (e.g., GCN and GraphSAGE) across a diverse set of benchmarks.
From Retrieval to Reasoning: Advancing AI Agents for Knowledge Discovery and Collaboration
Authors: Jure Leskovec
Venue: WWW
Year: 2025

The web is the world's largest knowledge repository, yet as AI systems become increasingly integrated into our digital infrastructure, the ability to retrieve, reason, and collaborate effectively has become paramount. Large Language Models (LLMs) are evolving from passive responders to active knowledge agents that can retrieve complex information, validate hypotheses, and optimize interactions over multiple turns. In this talk, I will explore the frontiers of AI-driven knowledge retrieval and reasoning, drawing from recent research on knowledge graphs, semi-structured retrieval, adaptive tool use, and multi-turn AI collaboration. I will also discuss how agentic frameworks enable rigorous, automated hypothesis validation through sequential falsifications. Together, these advancements push beyond traditional search and QA systems, unlocking new capabilities for knowledge discovery, scientific research, and human-AI collaboration. Finally, I will highlight key challenges and opportunities in building AI systems that are not just accurate, but also interactive, explainable, and aligned with human needs.
Are Copywriters the Best Humans for the Job? The Case of Health-Related Ads
Authors: Liat Levontin, Baillie Shuster, Elad Yom-Tov, Brit Youngmann
Venue: WWW
Year: 2025

Health authorities who wish to design a health-promoting advertising campaign would like to create high quality, trustworthy ads that cause behavior change. Here we compare human copywriters' ads to those created by other methods with different levels of expertise and trustworthiness, including Large Language Models, non-specialists, and students of a marketing class (with some expertise). To do so, we ran an online search ads campaign for 10 health topics with 142 ads from five sources and measured ad performance through the Click-through rate (CTR). Our results show that the copywriter ads' CTR was no better than the CTR of ads created by a Large Language Model, by marketing students, or by nonspecialists. The CTR of the best marketing student's ad was higher than that of the copywriter ad. Interestingly, through a crowdsourcing experiment, we show that consumers judge health ads written by copywriters as more persuasive than ads from other sources (algorithm and users). These results suggest that algorithms can be trained to write health ads that are, in practice, as good as those of copywriters.
Dual Graph Denoising Model for Social Recommendation
Authors: Anchen Li, Bo Yang
Venue: WWW
Year: 2025

Graph-based social recommender systems utilize user-item interaction graphs and user-user social graphs to model user preferences. However, their performance can be limited by redundant and noisy information in these two graphs. Although several recommender studies on data denoising exist, most either rely on heuristic assumptions, which limit their adaptability, or use a single model that combines denoising and recommendation, potentially imposing substantial demands on the model capacity. To address these issues, we propose a dual Graph Denoising Social Recommender (GDSR), which consists of two steps: graph denoising and user preference prediction. First, we design a denoising module which exploits a dual diffusion model to alleviate noises in the interaction and social graphs by performing multi-step noise diffusion and removal. We develop three kinds of conditions to guide our dual graph diffusion paradigm and propose a cross-domain signal guidance mechanism to enhance the structure of denoised graphs. Second, we devise a recommender module that employs a dual graph learning structure on denoised graphs to generate recommendations. Moreover, we use additional supervision signals from the diffusion-enhanced data augmentation to introduce a graph contrastive learning task, enhancing the recommender module's representation quality and robustness. Experiment results show the effectiveness of our GDSR.
Responsible Diffusion Models via Constraining Text Embeddings within Safe Regions
Authors: Zhiwen Li, Die Chen, Mingyuan Fan, Cen Chen, Yaliang Li, Yanhao Wang, Wenmeng Zhou
Venue: WWW
Year: 2025

The remarkable ability of diffusion models to generate high-fidelity images has led to their widespread adoption. However, concerns have also arisen regarding their potential to produce Not Safe for Work (NSFW) content and exhibit social biases, hindering their practical use in real-world applications. In response to this challenge, prior work has focused on employing security filters to identify and exclude toxic text, or alternatively, fine-tuning pre-trained diffusion models to erase sensitive concepts. Unfortunately, existing methods struggle to achieve satisfactory performance in the sense that they can have a significant impact on the normal model output while still failing to prevent the generation of harmful content in some cases. In this paper, we propose a novel self-discovery approach to identifying a semantic direction vector in the embedding space to restrict text embedding within a safe region. Our method circumvents the need for correcting individual words within the input text and steers the entire text prompt towards a safe region in the embedding space, thereby enhancing model robustness against all possibly unsafe prompts. In addition, we employ Low-Rank Adaptation (LoRA) for semantic direction vector initialization to reduce the impact on the model performance for other semantics. Furthermore, our method can also be integrated with existing methods to improve their social responsibility. Extensive experiments on benchmark datasets demonstrate that our method can effectively reduce NSFW content and mitigate social bias generated by diffusion models compared to several state-of-the-art baselines. WARNING:This paper contains model-generated images that may be potentially offensive.
An Unbiased Entire-Space Causal Framework for Click-Through Rate Estimation in Pre-Ranking
Authors: Xuanlin Li, Xiangyu Cai, Hao Peng, Jia Duan, Wei Wang, Zehua Zhang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao
Venue: WWW
Year: 2025

Online advertising systems increasingly depend on advanced recommendation algorithms to optimize click-through rates (CTR) and maximize revenue. However, these systems frequently grapple with sample selection bias (SSB) at the pre-ranking stage, where CTR estimation models are trained on feedback from the ranking stage but applied to datasets with contrasting distributions. This disparity in data distributions not only leads to inaccurate estimations but also significantly undermines the effectiveness of advertising strategies. Most existing approaches, which rely on empirical label generation and random sampling of unexposed samples, introduce additional distortions in CTR estimation without adequately addressing sample biases. To tackle the SSB issue in the pre-ranking stage, we propose a comprehensive unbiased counterfactual causal framework for CTR estimation. Specifically, to fully leverage the entire- space samples, we introduce a feature-level unbiased label generation module for unexposed samples. Additionally, while modeling the propensity of unexposed samples, we present an unbiased automatic sample selection module that strikes a balance between model accuracy and computational efficiency over large-scale datasets. Furthermore, we formally demonstrate that the proposed framework maintains theoretical unbiasedness across the entire-space. Extensive experimental results validate the superior performance and effectiveness of the proposed framework. Compared to current state-of-the-art (SOTA) empirical and causal methods, our framework achieves a maximum AUC improvement of 4.47% on offline datasets and a 1.11% increase in CTR during online A/B testing. This framework has been successfully deployed in large-scale industrial advertising systems, yielding reliable enhancements in prediction accuracy and revenue outcomes.
Reembedding and Reweighting are Needed for Tail Item Sequential Recommendation
Authors: Zihao Li, Yakun Chen, Tong Zhang, Xianzhi Wang
Venue: WWW
Year: 2025

Applying large vision models (LVMs) and large language models (LLMs) for item embedding is becoming cutting-edge for sequential recommendation, given their success in broad applications. Despite their advantages over traditional approaches, these models suffer more significant performance degradation on tail items against conventional ID-based solutions, which are largely overlooked by recent research. In this paper, we substantiate the above challenges as (1) all-in ground-truth, i.e., the standard cross-entropy (CE) loss focuses solely on the target items while treating all non-ground-truth equally, causing insufficient optimization for tail items, and (2) knowledge transfer tax, i.e., the knowledge encapsulated in LLMs and LVMs dominates the optimization process due to insufficient training for tail items. We propose Rewarding and reembedding, a simple yet efficient method to address the above challenges. Specifically, we reinitialize tail item embedding via a Gaussian distribution to alleviate knowledge transfer tax; besides, a rewarding function is incorporated in the CE loss, which adaptively adjusts item rewards during training to encourage the model to pay more attention to tail items rather than exclusively optimizing for ground-truth. Overall, our method enables a more nuanced optimization and is mathematically comparable to the direct preference optimization (DPO) in LLMs. Our extensive experiments on three public datasets show our method outperforms fourteen baselines in overall performance and improves the performance on tail items by a large margin. Our code is available at https://github.com/Yuhanleeee/R2Rec.
ETS-MM: A Multi-Modal Social Bot Detection Model Based on Enhanced Textual Semantic Representation
Authors: Wei Li, Jiawen Deng, Jiali You, Yuanyuan He, Yan Zhuang, Fuji Ren
Venue: WWW
Year: 2025

Social bots are becoming increasingly common in social networks, and their activities affect the security and authenticity of social media platforms. Current state-of-the-art social bot detection methods leverage multimodal approaches that analyze various modalities, such as user metadata, text, and social network relationships. However, these methods may not always extract additional dimensions of semantic feature information that could offer a deeper understanding of users' social patterns. To address this issue, we propose ETS-MM, a multimodal detection framework designed to augment multidimensional information from text and extract the semantic feature representation of user text information. We first analyze the user's tweeting behavior based on topic preference and emotion tendency, integrating them into the textual data. Then, we try to extract enhanced semantic representations that reveal the latent relationship between tweeting behavior and tweet content while identifying potential contextual associations and emotional changes. Additionally, to capture the complex interaction between users, we integrate the user's multimodal information, including metadata, textual features, enhanced semantic features, and social network relationships to propagate and aggregate information across various modalities. Experimental results demonstrate that ETS-MM significantly outperforms existing methods across two widely used social bot detection benchmark datasets, validating its effectiveness and superiority.
The First International Workshop on Large Language Models for Social Media (SocialLLM 2025)
Authors: Cheng-Te Li, Lun-Wei Ku
Venue: WWW
Year: 2025

The rapid evolution of Large Language Models (LLMs) has profoundly impacted social media, transforming how information is generated, disseminated, and analyzed. With their ability to process vast amounts of data, grasp contextual nuances, and engage in human-like dialogue, LLMs present new opportunities and challenges for understanding online interactions. The SocialLLM 2025 workshop builds on the foundation laid by SocialNLP, expanding the scope to explore the capabilities and implications of LLMs in social media research. This year's workshop at TheWebConf 2025 focuses on three pivotal themes: leveraging LLMs for mental health support, enhancing emotion detection in textual interactions, and improving misinformation detection through active learning. The selected papers illustrate cutting-edge advancements in these areas, demonstrating how LLMs can be fine-tuned for therapeutic dialogue generation, assessed for their emotional intelligence, and optimized for misinformation detection with minimal labeled data. These contributions highlight the growing interdisciplinary nature of LLM research, merging insights from natural language processing, social computing, and artificial intelligence ethics. By bringing together researchers and practitioners from diverse backgrounds, SocialLLM 2025 aims to foster meaningful discussions on the opportunities and risks associated with LLM-driven social media applications. The workshop serves as a platform for exploring novel methodologies, addressing ethical concerns, and shaping future directions for responsible AI deployment in social media environments. Through collaborative efforts, we seek to advance the field and ensure that LLMs contribute positively to the digital ecosystem.
Pontus: A Memory-Efficient and High-Accuracy Approach for Persistence-Based Item Lookup in High-Velocity Data Streams
Authors: Weihe Li, Zukai Li, Beyza Bütün, Alec F. Diallo, Marco Fiore, Paul Patras
Venue: WWW
Year: 2025

In today's web-scale, data-driven environments, real-time detection of persistent items that consistently recur over time is essential for maintaining system integrity, reliability, and security. Persistent items often signal critical anomalies, such as stealthy DDoS and botnet attacks in web infrastructures. Although various methods exist for identifying such items as well as for determining their frequency, they require recording every item for processing, which is impractical at very high data rates achieved by modern data streams. In this paper, we introduce Pontus, a novel approach that uses an approximate data structure (sketch) specifically designed for the efficient and accurate detection of persistent items. Our method not only achieves fast and precise lookup but is also flexible, allowing for minor modifications to accommodate other types of persistence-based item detection tasks, such as detecting persistent items with low frequency. We rigorously validate our approach through formal methods, offering detailed proofs of time/space complexity and error bounds to demonstrate its theoretical soundness. Our extensive trace-driven evaluations across various persistence-based tasks further demonstrate Pontus's effectiveness in significantly improving detection accuracy and enhancing processing speed compared to existing approaches. We implement Pontus in an experimental platform with industry-grade Intel Tofino switches and demonstrate the practical feasibility of our approach in a real-world memory-constrained environment.
Hyper-Relational Knowledge Representation Learning with Multi-Hypergraph Disentanglement
Authors: Jiecheng Li, Xudong Luo, Guangquan Lu, Shichao Zhang
Venue: WWW
Year: 2025

Hyper-relational knowledge graphs (HKGs) extend the traditional triplet-based knowledge graph by adding qualifiers to the relationships, making HKGs particularly useful for tasks that require more profound understanding and inference from relationships between entities. However, existing hyper-relational knowledge representation learning methods (HKRL) focus on direct neighbourhood information of entities only by neglecting the relational similarity of the main triple in hyper-relational facts and the attribute details in the qualifiers. In addition, few works extract common and private information across multiple views to minimize noise and interference. This paper proposes a multi-hypergraph disentanglement method for HKRL to address the above issues. Specifically, we first construct four hypergraphs to mine and utilise the inherent structure information of HKGs, and then propose to extract common representations among hypergraphs and private representations within individual hypergraphs to mine the semantic information and the task-relevant information, respectively. Experiment results on four real datasets demonstrate the effectiveness of the proposed method compared to SOTA methods in link prediction tasks on HKGs.