OpenRaiser/Intern-Atlas huggingface dataset:
Robust Defense Mechanisms for Agentic News Recommenders: Mitigating Data Poisoning Attacks
Authors: Praveen Kumar Myakala, Chiranjeevi Bura
Venue: WWW
Year: 2025

As personalized content delivery becomes central to modern news consumption, agentic news recommenders face rising threats from data poisoning attacks. These attacks inject malicious data into the training pipelines, distorting recommendations, spreading misinformation, and eroding user trust. In this study, we introduce an integrated defense framework that combines robust learning algorithms, data sanitization techniques, and anomaly detection methods to safeguard news recommendation systems. Through extensive experiments on a widely used real-world news dataset, our framework demonstrated significant improvement in both accuracy and resilience, achieving a 10.6% increase in NDCG@10 under attack conditions. We also explore the ethical dimensions of deploying such defenses, emphasizing the balance between fairness, transparency, and robustness. Our findings contribute to the development of secure, trustworthy, and adaptable news recommender systems capable of withstanding evolving adversarial threats.
TransForeCaster: In-and-Cross Categorized Feature Integration in User Representation Learning
Authors: Hyunkee Myung, Junwoo Yun, Wonryeol Kwak, Yoonbyung Lee, Joongsu Kim, Joohyun Kim
Venue: WWW
Year: 2025

This paper introduces TransForeCaster, a novel user representation learning approach to improving prediction accuracy in purchase and churn predictions via a two-stage feature integration process: In-Category Integration (ICI) and Cross-Category Integration (CCI). The ICI stage employs a Time-Series Feature Mixer (TSFM) to capture the temporal dynamics of features within the same categories, resulting in compact and continuous category representations. The CCI stage utilizes a Meta-Conditioned Transformer (MCT) to integrate the representations with multi-task learning, capturing complex relationships across categories and improving interpretability through attention mechanisms. Empirical evaluations of real-world datasets demonstrate significant improvements over conventional models, supported by qualitative analyses using feature importance assessments and UMAP visualizations. TransForeCaster's robustness is validated by its superior performance over other models in multiple in-house deployments across various games and applications. The source code is available at https://github.com/bagelcode-data-science-team/TransForeCaster.
CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems
Authors: Kota Nakamura, Koki Kawabata, Shungo Tanaka, Yasuko Matsubara, Yasushi Sakurai
Venue: WWW
Year: 2025

Cybersecurity systems are continuously producing a huge number of time-stamped events in the form of high-order tensors, such as {count; time, port, flow duration, packet size, . . .,} and so how can we detect anomalies/intrusions in real time? How can we identify multiple types of intrusions and capture their characteristic behaviors? The tensor data consists of categorical and continuous attributes and the data distributions of continuous attributes typically exhibit skew. These data properties require handling skewed infinite and finite dimensional spaces simultaneously. In this paper, we propose a novel streaming method, namely CyberCScope. The method effectively decomposes incoming tensors into major trends while explicitly distinguishing between categorical and skewed continuous attributes. To our knowledge, it is the first to compute hybrid skewed infinite and finite dimensional decomposition. Based on this decomposition, it streamingly finds distinct time-evolving patterns, enabling the detection of multiple types of anomalies. Extensive experiments on large-scale real datasets demonstrate that CyberCScope detects various intrusions with higher accuracy than state-of-the-art baselines while providing meaningful summaries for the intrusions that occur in practice.
DiGrI: Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection
Authors: Usman Naseem, Liang Hu, Qi Zhang, Shoujin Wang, Shoaib Jameel
Venue: WWW
Year: 2025

User-generated content on social media platforms provides a valuable resource for developing automated computational methods to detect mental health issues online leading to suicidal thoughts automatically. Although current fully automated methods show promise, they may produce uncertain predictions, leading to flawed conclusions. To address this, we propose a novel model called DiGrI, or Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection, which reformulates suicide ideation assessment as a selective, prioritized prediction problem. The model incorporates a novel multi-classifier distorted greedy model that is optimized to operate under various levels of automation and abstains from making uncertain predictions with theoretical guarantees. Our results show that DiGrI outperforms strong comparative models including large language models in detecting mental health issues on a publicly available Reddit dataset. We discuss the empirical and practical implications, including the ethical considerations of using DiGrI for online automatic suicide ideation detection involving humans, if it were to be translated for use in clinical and public health practice.
LLMs as Historical Actors: How AI Systems Influence the Web's Evolution
Authors: Usman Naseem, Junaid Rashid, Jungeun Kim, Amin Beheshti, Jian Yang, Mark Dras
Venue: WWW
Year: 2025

The history of the web has been defined by a series of revolutionary developments, from the advent of HTML and web browsers to the explosion of social media and cloud computing. Throughout this evolution, human actors have played pivotal roles, serving as developers, technologists, and innovators who have guided the trajectory of the Internet. However, the emergence of Large Language Models (LLMs), such as OpenAI's GPT series, represents a paradigm shift. These AI systems are not merely tools for humans but are increasingly becoming autonomous agents that shape the flow of information on the web. In this paper, we argue that LLMs can be framed as historical actors, meaning they are not only facilitators of human endeavors but also independent forces that influence the web's evolution. We explore how these AI systems direct the flow of information, shape public discourse, and may influence future interpretations of web-based history.
Third International Workshop on Multimodal Content Analysis for Social Good
Authors: Usman Naseem, Surendrabikram Thapa, Roy Ka-Wei Lee, Mehwish Nasim
Venue: WWW
Year: 2025

The third edition of the International Workshop on Multimedia Content Analysis for Social Good (MM4SG 2025) was held alongside the prestigious Web Conference 2025. This workshop aimed to tackle the critical challenge of analyzing and moderating multimodal content across digital platforms. In today's era, where diverse forms of multimodal data-including memes, text-embedded images, and fabricated content-can rapidly shape public opinion and influence societal narratives, the demand for sophisticated and ethical content moderation strategies has become increasingly urgent. MM4SG 2025 provided a unique forum for interdisciplinary collaboration, bringing together researchers and practitioners from natural language processing, machine learning, computational social science, and ethics to address these pressing concerns. This paper highlights the key themes, discussions, and contributions of the third edition of the MM4SG workshop, with a particular focus on the intersection of computational linguistics and multimodal content analysis. It also explores future directions for the workshop, including expanding its scope and impact in subsequent editions.
MixedSAND: Semantic Annotation of Mixed-unit Numeric Data
Authors: Amir Behrad Khorram Nazari, Davood Rafiei, Mario A. Nascimento
Venue: WWW
Year: 2025

Quantitative information about entities constitutes a significant portion of tabular data in open sources and data lakes. Such tables often lack consistent labeling and proper schema, posing significant challenges for querying and integration. This paper studies the problem of numerical column annotation in scenarios where quantitative data may be gathered from different sources and unit consistency is a concern. For instance, weight measurements may vary between entities, expressed in kilograms for some and pounds for others, with no accompanying unit information. We investigate the conditions for effectively annotating mixed-unit numeric data, introduce a benchmark for such an annotation task, and propose an algorithm that reliably detects semantic types (e.g., height) and links them to the corresponding types present in a knowledge graph. Our evaluation on a diverse set of columns with mixed units and varying levels of annotation difficulty shows that our method significantly outperforms strong baselines such as GPT-4o-mini and SAND in terms of accuracy, excelling in both detecting mixed units and annotating them with appropriate semantic labels.
Spherical Embeddings for Atomic Relation Projection Reaching Complex Logical Query Answering
Authors: Chau D. M. Nguyen, Tim French, Michael Stewart, Melinda Hodkiewicz, Wei Liu
Venue: WWW
Year: 2025

Projecting knowledge graph queries into an embedding space using geometric models (points, boxes and spheres) can help to answer queries for large incomplete knowledge graphs. In this work, we propose a symbolic learning-free approach using fuzzy logic to address the shape-closure problem that restricted geometric-based embedding models to only a few shapes (e.g. ConE) for answering complex logical queries. The use of symbolic approach facilitates non-closure geometric models (e.g. point, box) to handle logical operators (including negation). This enabled our newly proposed spherical embeddings (SpherE) in this work to use a polar coordinate system to effectively represent hierarchical relation. Results show that the SpherE model can answer existential positive first-order logic and negation queries. We show that SpherE significantly outperforms the point and box embeddings approaches while generating semantically meaningful hierarchy-aware embeddings.
CiteArXiv: A Citation-enriched Dataset and Heterogeneous Graph-based Model for Scientific Articles Summarization
Authors: Quoc-An Nguyen, Xuan-Hung Le, Thi-Minh-Thu Vu, Hoang-Quynh Le
Venue: WWW
Year: 2025

The exponential growth of scientific publications creates challenges for researchers in processing information. To improve summarization quality, we present CiteArXiv, a large-scale citation-enriched extension of the ArXiv dataset, where each paper is paired with its citation sentences. We further propose a heterogeneous graph model that integrates internal content and external citation context, using elementary discourse units (EDUs) as extraction granularity. Experimental results show that combining internal and external knowledge enhances scientific summarization performance.
Towards Effective Identification of Attack Techniques in Cyber Threat Intelligence Reports using Large Language Models
Authors: Hoang Cuong Nguyen, Shahroz Tariq, Mohan Baruwal Chhetri, Bao Quoc Vo
Venue: WWW
Year: 2025

This work evaluates the performance of Cyber Threat Intelligence (CTI) extraction methods in identifying attack techniques from threat reports available on the web using the MITRE ATT&CK framework. We analyse four configurations utilising state-of-the-art tools, including the Threat Report ATT&CK Mapper (TRAM) and open-source Large Language Models (LLMs) such as Llama2. Our findings reveal significant challenges, including class imbalance, overfitting, and domain-specific complexity, which impede accurate technique extraction. To mitigate these issues, we propose a novel two-step pipeline: first, an LLM summarises the reports, and second, a retrained SciBERT model processes a rebalanced dataset augmented with LLM-generated data. This approach achieves an improvement in F1-scores compared to baseline models, with several attack techniques surpassing an F1-score of 0.90. Our contributions enhance the efficiency of web-based CTI systems and support collaborative cybersecurity operations in an interconnected digital landscape, paving the way for future research on integrating human-AI collaboration platforms.
Hybrid, Unified and Iterative: A Novel Framework for Text-based Person Anomaly Retrieval
Authors: Tien-Huy Nguyen, Huu-Loc Tran, Huu-Phong Phan-Nguyen, Quang-Vinh Dinh
Venue: WWW
Year: 2025

Text-based person anomaly retrieval has emerged as a challenging task, with most existing approaches relying on complex deep-learning techniques. This raises a research question: How can the model be optimized to achieve greater fine-grained features? To address this, we propose a Local-Global Hybrid Perspective (LHP) module integrated with a Vision-Language Model (VLM), designed to explore the effectiveness of incorporating both fine-grained features alongside coarse-grained features. Additionally, we investigate a Unified Image-Text (UIT) model that combines multiple objective loss functions, including Image-Text Contrastive (ITC), Image-Text Matching (ITM), Masked Language Modeling (MLM), and Masked Image Modeling (MIM) loss. Beyond this, we propose a novel iterative ensemble strategy, by combining iteratively instead of using model results simultaneously like other ensemble methods. To take advantage of the superior performance of the LHP model, we introduce a novel feature selection algorithm based on its guidance, which helps improve the model's performance. Extensive experiments demonstrate the effectiveness of our method in achieving state-of-the-art (SOTA) performance on PAB dataset, compared with previous work, with a 9.70 improvement in R@1, 1.77 improvement in R@5, and 1.01 improvement in R@10.
Breaking the Shield: Analyzing and Attacking Canvas Fingerprinting Defenses in the Wild
Authors: Hoang Dai Nguyen, Phani Vadrevu
Venue: WWW
Year: 2025

Canvas fingerprinting has become one of the most effective techniques for tracking users online, allowing websites to identify and track visitors without their consent. In this paper, we investigate four primary defense techniques designed to counter canvas fingerprinting, systematically analyzing their adoption across 18 browser extensions in Chrome and Firefox, as well as built-in protections from five major browsers: Chrome, Firefox, Brave, Tor, and Safari. Our analysis reveals significant disparities in the implementation and effectiveness of these defenses, with randomization-based techniques being the most widely adopted, particularly across nine extensions and in the privacy-focused browser, Brave. Despite their sophistication, we demonstrate successful attacks on all these randomization mechanisms, revealing that their supposed non-deterministic behavior can, in fact, be predicted and exploited. In summary, we demonstrate that, unfortunately, no fully deployable defense against canvas fingerprinting attacks exists currently. We conclude by proposing recommendations to strengthen existing defenses and enhance their resistance to future attacks.
A Co-Attentive Infusion of Structural Knowledge into Language Models towards Legal Textual Inference
Authors: Tan-Minh Nguyen, Thi-Hai-Yen Vuong, Hoang-Trung Nguyen, Le-Minh Nguyen, Xuan-Hieu Phan
Venue: WWW
Year: 2025

In this paper, we explore the potential and impact of semantic structures in legal textual inference and propose a structural knowledge infusion architecture, AIKLR, which integrates abstract meaning representation (AMR) with pre-trained language models (PLMs). We hypothesize that AMR's structural representations can effectively capture complex logical relationships in sentences, complementing their semantic representations and thereby enhancing the legal reasoning and inference capabilities of language models. Our proposed architecture demonstrates significant accuracy improvements of 16% to 18% over baseline models. Furthermore, AIKLR achieves competitive results against recent large language models (LLMs) on the COLIEE datasets, with a remarkable 32% difference in predicted outputs. These results highlight AIKLR's ability to leverage structural representations for improved logical reasoning, making it a valuable reinforcement for LLMs in legal text processing.
Operationalising Harmlessness in Online AI Systems
Authors: Hongliang Ni
Venue: WWW
Year: 2025

Although the harmlessness techniques for foundation models have been explored in the industry, existing methods struggle to adapt to increasingly stringent legal requirements. We observe that as concerns from governments and the public about the harmfulness of automated decision-making by foundation models continue to grow, the interpretation of legal requirements for explainability and fairness is evolving. This requires the industry to rethink how to achieve compliance, ensuring that technologies and applications can be effectively implemented in practice. In this paper, we propose an operationalising research framework to enhance the harmlessness of foundation models, consisting of three major research questions: exploring how foundation models amplify the harmfulness of datasets, investigating harmlessness techniques under strict black-box conditions, and examining proactive explanations for foundation models. Based on this framework, we discuss related literature and our advocated methodologies, introducing an initial exploration: a method that ensures the effectiveness and fairness of algorithmic predictions without requiring sensitive information. Finally, we outline future work to be conducted.
FG-CIBGC: A Unified Framework for Fine-Grained and Class-Incremental Behavior Graph Classification
Authors: Zhibin Ni, Pan Fan, Shengzhuo Dai, Bo Zhang, Hai Wan, Xibin Zhao
Venue: WWW
Year: 2025

Learning-based Behavior Graph Classification (BGC) is widely used in Internet infrastructure for partitioning and identifying similar behavior graphs, yet its real-world application faces notable challenges. The challenges are: (i) fine-grained emerging behavior graphs, and (ii) incremental model adaptations. To tackle these issues, we propose to (i) mine semantics in multi-source logs using Large Language Models (LLMs) under In-Context Learning (ICL), and (ii) bridge the gap between Out-Of-Distribution (OOD) detection and class-incremental graph learning. Based on these ideas, we develop the first unified framework termed as Fine-Grained and Class-Incremental Behavior Graph Classification (FG-CIBGC ). It consists of two novel modules, i.e., gPartition and gAdapt, that are used for partitioning fine-grained graphs and performing unknown class detection and adaptation, respectively. To validate FG-CIBGC, we introduce a new benchmark, including a 4,992-graph, 32-class dataset from 8 attack scenarios and a novel Edge Intersection over Union (EIoU) metric. Extensive experiments show FG-CIBGC outperforms baselines on fine-grained class-incremental BGC task and generates behavior graphs which enhance downstream tasks.
REST API Access Control - an OPTIONS Based Approach
Authors: Ioannis Nikolaou, Leonidas G. Anthopoulos
Venue: WWW
Year: 2025

The REST API architectural style has become a de-facto standard in the development of web services and applications. In order to secure a REST API, access control methods are being used. The current approaches to REST API authorization controls are not standardized and lead to tight coupling between the REST API provider and client. This paper proposes a HTTP semantics compliant method for enforcing authorization rules in a REST API by combining the HTTP OPTIONS method with the HTTP Authorization request header. The proposed approach is REST API agnostic, achieves the decoupling of the authorization method from the REST API client implementation, improves the security of the REST API clients and promotes code reusability and development efficiency.
SANS: Efficient Densest Subgraph Discovery over Relational Graphs without Materialization
Authors: Yudong Niu, Yuchen Li, Jiaxin Jiang, Laks V. S. Lakshmanan
Venue: WWW
Year: 2025

How can we efficiently identify the densest subgraph over relational graphs? Existing dense subgraph discovery (DSD) approaches assume that a relational graph H is already derived from a heterogeneous data source and they focus on efficient discovery of the densest subgraph on the materialized H. Unfortunately, materializing relational graphs can be resource-intensive, which thus limits the practical usefulness of existing algorithms over large datasets. To mitigate this, we propose a novel Summary-bAsed deNsest Subgraph discovery (SANS) system. Our unique summary-based peeling algorithm forms the core of SANS. Following the peeling paradigm, it utilizes summaries of each node's neighborhood to efficiently estimate peeling coefficients and subgraph densities at each peeling iteration and thus avoids materializing the relational graph completely. Through extensive experiments, we demonstrate the efficacy and efficiency of SANS, reaching orders of magnitude speedups compared to the conventional baselines with materialization, while consistently achieving at least 95% accuracy compared to peeling algorithms based on materialization.
C-MTCSD: A Chinese Multi-Turn Conversational Stance Detection Dataset
Authors: Fuqiang Niu, Yi Yang, Xianghua Fu, Genan Dai, Bowen Zhang
Venue: WWW
Year: 2025

Stance detection has become an essential tool for analyzing public discussions on social media. Current methods face significant challenges, particularly in Chinese language processing and multi-turn conversational analysis. To address these limitations, we introduce C-MTCSD, the largest Chinese multi-turn conversational stance detection dataset, comprising 24,264 carefully annotated instances from Sina Weibo, which is 4.2 times larger than the only prior Chinese conversational stance detection dataset. Our comprehensive evaluation using both traditional approaches and large language models reveals the complexity of C-MTCSD: even state-of-the-art models achieve only 64.07% F1 score in the challenging zero-shot setting, while performance consistently degrades with increasing conversation depth. Traditional models particularly struggle with implicit stance detection, achieving below 50% F1 score. This work establishes a challenging new benchmark for Chinese stance detection research, highlighting significant opportunities for future improvements.
Quantifying Individual Fairness in Continual Federated Learning
Authors: Naima Noor, Afra Mashhadi
Venue: WWW
Year: 2025

Continual Federated Learning (CFL) is a distributed machine learning technique that enables multiple clients to collaboratively train a shared model without sharing their data, while also adapting to new classes without forgetting previously learned ones. This dynamic, adaptive learning process parallels the concept of foundation models in FL, where large, pre-trained models are fine-tuned in a decentralized, federated setting. While foundation models in FL leverage pre-trained knowledge as a starting point, CFL continuously updates the shared model as new tasks and data distributions emerge, requiring ongoing adaptation. Currently, there are limited evaluation models and metrics in measuring fairness in CFL, and ensuring fairness over time can be challenging as the system evolves. To address this challenge, this article explores temporal fairness in CFL, examining how the fairness of the model can be influenced by the selection and participation of clients over time. Based on individual fairness, we introduce a novel fairness metric that captures temporal aspects of client behavior and evaluates different client selection strategies for their impact on promoting fairness.