OpenRaiser/Intern-Atlas huggingface dataset:
Fake News Detection via Intermediate-Layer Emotional Representations
Authors: Yassine Toughrai, David Langlois, Kamel Smaïli
Venue: WWW
Year: 2025

This paper introduces Intermediate-Layer Emotional Representations (ILER), a novel emotional embedding technique designed to capture nuanced emotional patterns in textual data. While we demonstrate its utility in the domain of fake news detection, ILER was conceived as a generalizable framework to be applied across various tasks requiring emotion or sentiment analysis. We aim to leverage the abstract and rich representations offered by ILER by combining publisher-driven emotions embedded within news content and social emotions derived from user comments. This approach seeks to capture the intricate emotional nuances often associated with fake news dissemination. Evaluations on benchmark datasets emulating live systems show a 5-point increase in F1-score and a 3.9-point increase in accuracy compared to state-of-the-art emotion representations, including Dual Emotion Features. Our findings underscore the potential of abstracted emotional representations to deepen context understanding, enabling more robust and precise emotionally assisted fake news detection.
Understanding and Mitigating Polarization in Community Notes: Factors and Strategies for Improved Consensus
Authors: Keisuke Toyoda, Tomoki Fukuma, Koki Noda, Yoshiharu Ichikawa, Kyosuke Kambe, Yu Masubuchi, Hiroshi Someda, Fujio Toriumi
Venue: WWW
Year: 2025

Polarization in online discourse hinders constructive dialogue and exacerbates social division. Through analysis of over 40,000 Japanese Community Notes on X (formerly Twitter), this study empirically identifies factors that drive and mitigate polarization in collaborative fact-checking. We find that notes on tweets containing political content or high retweet counts, as well as notes that directly address claims, exhibit bias, or lack critical information, are more likely to contribute to polarization. Conversely, clarity and neutrality in notes are crucial for fostering mutual acceptance in highly polarized contexts. Particularly for politically charged or highly engaging tweets, the presentation of clear and fair information proves more significant in improving evaluations than source reliability. This research highlights the potential for strategic information presentation to mitigate polarization and foster constructive online dialogue.
Peripheral Instinct: How External Devices Breach Browser Sandboxes
Authors: Leon Trampert, Lorenz Hetterich, Lukas Gerlach, Mona Schappert, Christian Rossow, Michael Schwarz
Venue: WWW
Year: 2025

Browser APIs such as WebHID, WebUSB, Web Serial, and Web MIDI enable web applications to interact directly with external devices. The support of such APIs in Chromium-based browsers, such as Chrome and Edge, radically changes the threat model for peripherals and increases the attack surface. In the past, devices could assume a trusted host, i.e., the operating system. Now, the host is a potentially malicious website and cannot be trusted. We show how this changed threat model leads to security and privacy problems, up to a complete compromise of the operating system. While the API specifications list initial security considerations, they shift the responsibility to (unprepared) device vendors. We systematically analyze the security implications of external devices exposed by such new APIs. By reverse-engineering peripheral devices of several popular widespread vendors, we show that many vendors allow controlling devices via Web APIs up to reprogramming or even fully replacing the firmware. Consequently, web attackers can reprogram devices with malicious payloads and custom firmware without requiring any physical interaction. To demonstrate the security implications, we build several full-chain exploits, leading to arbitrary code execution on the victim system, circumventing the browser sandbox. Our research shows that browser security should not rely on the secure implementation of third-party hardware.
Explainable Manipulated Videos Detection Using Multimodal Large Language Models
Authors: Khoa-Dang Tran
Venue: WWW
Year: 2025

Misinformation detection is receiving significant attention due to their impact on modern social stability. Extensive research has been conducted on multimodal misinformation detection, employing either deep learning approaches or large language models (LLMs). However, existing literature on explainable LLM-based methods remained unexplored when applied to video data. Furthermore, most of the existing datasets focus on facial manipulation and human speech, whereas social media content encompasses a wide range of manipulations such as altering background context and color, adding or removing entities, editing audio and text. This paper proposed a framework based on multimodal large language models (MLLMs) for explainable manipulated videos detection. The framework involves fine-tuning two MLLMs for rationales generation and making final predictions, respectively. In addition, pre-trained encoders, cross-attention mechanisms, and a fusion gate are utilized to extract deeper features for fine-tuning the second MLLM. The goal is to predict manipulated content, identify the specific attack applied to the input video, and provide detailed explanations. This research discusses preliminary findings in literature, presents initial experimental results, and outlines key directions for future work.
Fairness and Robustness in Machine Unlearning
Authors: Khoa Tran, Simon S. Woo
Venue: WWW
Year: 2025

Machine unlearning poses the challenge of ''how to eliminate the influence of specific data from a pretrained model'' in regard to privacy concerns. While prior research on approximated unlearning has demonstrated accuracy and efficiency in time complexity, we claim that it falls short of achieving exact unlearning, and we are the first to focus on fairness and robustness in machine unlearning algorithms. Our study presents fairness Conjectures for a well-trained model, based on the variance-bias trade-off characteristic, and considers their relevance to robustness. Our Conjectures are supported by experiments conducted on the two most widely used model architectures-ResNet and ViT-demonstrating the correlation between fairness and robustness: the higher fairness-gap is, the more the model is sensitive and vulnerable. In addition, our experiments demonstrate the vulnerability of current state-of-the-art approximated unlearning algorithms to adversarial attacks, where their unlearned models suffer a significant drop in accuracy compared to the exact-unlearned models. We claim that our fairness-gap measurement and robustness metric should be used to evaluate the unlearning algorithm. Furthermore, we demonstrate that unlearning in the intermediate and last layers is sufficient and cost-effective for time and memory complexity.
Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search
Authors: Ekaterina Trimbach, Badr Abdallaoui, Paul Missault
Venue: WWW
Year: 2025

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extraction, pose significant challenges for large-scale deployment. This paper investigates how different configurations of feature selection and document filtering in neural cascade ranking systems influence the trade-off between computational cost and ranking performance. We propose a two-stage neural cascade architecture where both stages utilize Multi-Layer Perceptrons (MLPs). The first stage processes all documents using a reduced feature set, while the second stage applies a more sophisticated model to only the top-ranked documents. This design allows us to systematically explore the impact of feature selection and document filtering on both computational cost and ranking performance. Through extensive experiments on three large-scale datasets (Yahoo, Istella, and Microsoft MSLR-WEB30K), we demonstrate significant opportunities for cost reduction with minimal impact on ranking quality. Our results show that optimal cascade configurations can achieve cost reductions in feature extraction of up to 40.37% on the Yahoo dataset and 16% on the MSLR-WEB30K dataset while maintaining nearly identical NDCG@10. Furthermore, we identify clear patterns of diminishing returns in ranking performance as computational resources increase, providing valuable insights for developing resource-efficient ranking systems in large-scale web search environments.
Harmful Terms and Where to Find Them: Measuring and Modeling Unfavorable Financial Terms and Conditions in Shopping Websites at Scale
Authors: Elisa Tsai, Neal Mangaokar, Boyuan Zheng, Haizhong Zheng, Atul Prakash
Venue: WWW
Year: 2025

Terms and conditions for online shopping websites often contain terms that can have significant financial consequences for customers. Despite their impact, there is currently no comprehensive understanding of the types and potential risks associated with unfavorable financial terms. Furthermore, there are no publicly available detection systems or datasets to systematically identify or mitigate these terms. In this paper, we take the first steps toward solving this problem with three key contributions. First, we introduce TermMiner, an automated data collection and topic modeling pipeline to understand the landscape of unfavorable financial terms. Second, we create ShopTC-100K, a dataset of terms and conditions from shopping websites in the Tranco top 100K list, comprising 1.8 million terms from 8,251 websites. Consequently, we develop a taxonomy of 22 types from 4 categories of unfavorable financial terms---spanning purchase, post-purchase, account termination, and legal aspects. Third, we build TermLens, an automated detector that uses Large Language Models (LLMs) to identify unfavorable financial terms. Fine-tuned on an annotated dataset, TermLens achieves an F1 score of 94.6% and a false positive rate of 2.3% using GPT-4o. When applied to shopping websites from the Tranco top 100K, we find that 42.06% of these sites contain at least one unfavorable financial term, with such terms being more prevalent on less popular websites. Case studies further highlight the financial risks and customer dissatisfaction associated with unfavorable financial terms, as well as the limitations of existing ecosystem defenses.
SupportPlay: A Multi-Agent Role-Playing System for Personalized and Sustained Multimodal Emotional Support Conversation
Authors: Geng Tu, Bingbing Wang, Erik Cambria, Wenjie Li, Ruifeng Xu
Venue: WWW
Year: 2025

In this demo, we present SupportPlay, a multi-agent role-playing system for emotional support conversation (ESC) that addresses the limitations of existing methods in providing personalized, sustained, and multimodal support. SupportPlay generates potential seeker profiles from existing ESC datasets and employs GPT-powered agents to play various seeker roles, enabling the supporter to learn personalized memories for each seeker. Subsequently, the supporter can induce general memory from these memories for real user interactions while learning the user's personalized memory in a similar manner. Through continuous memory management-including retrieval, storage, reflection, and forgetting-SupportPlay delivers tailored emotional support across interactions. By integrating text, speech, and video, SupportPlay creates immersive emotional support experiences.
Navigating the LOD Subclouds: Assessing Linked Open Data Quality by Domain
Authors: Gabriele Tuozzo
Venue: WWW
Year: 2025

The Linked Open Data (LOD) Cloud, a catalog of datasets adhering to Linked Data principles, serves as a critical resource for semantic web applications. It is structured into domains, known as subclouds. While the overall quality of the LOD Cloud has been extensively examined in prior research, the quality of its individual subclouds has received limited attention. This study addresses this gap by evaluating the quality of the LOD Cloud by domain, using the November 2024 snapshot as a reference. The assessment employs KGHeartBeat, a Knowledge Graph (KG) quality assessment tool that evaluates six quality categories: Accessibility, Contextual, Intrinsic, Dataset-Dynamicity, Trust, and Representational aspects. The analysis encompasses 1,289 LD datasets spanning nine subclouds, including Cross Domain, Government, Life Sciences, and User Generated content. Results reveal significant quality variation across domains, likely influenced by domain-specific community priorities. For example, the Linguistic subcloud excels in Representational quality, while the Life Sciences subcloud demonstrates strength in the Intrinsic category. In contrast, the User Generated domain faces notable challenges in the Trust and Representational quality categories. A general trend observed includes improvements in Licensing information and Data Dump availability across most subclouds, contrasted by a decline in the availability of VoID files and SPARQL endpoints. This study enhances our understanding of how domain-specific factors impact dataset quality and identifies areas requiring targeted efforts to ensure consistent quality enhancements across the LOD Cloud.
Using Large Language Models for Hypotheses and Claims Extraction from Scientific Literature
Authors: Sagar Uprety, Boyana Buyuklieva, Prayag Tiwari
Venue: WWW
Year: 2025

We present a novel dataset for hypothesis and claim extraction from research papers in the interdisciplinary domain of social infertility. We use OpenAI's o1-mini reasoning LLM to create the dataset by extracting hypotheses and claims, which are then manually validated. Then we also use smaller closed and open-weight LLMs to create a benchmark for hypothesis and claim extraction.
FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking
Authors: Venktesh V, Vinay Setty
Venue: WWW
Year: 2025

The field of automated fact-checking increasingly depends on retrieving web-based evidence to determine the veracity of claims in real-world scenarios. A significant challenge in this process is not only retrieving relevant information, but also identifying evidence that can both support and refute complex claims. Traditional retrieval methods may return documents that directly address claims or lean toward supporting them, but often struggle with more complex claims requiring indirect reasoning. While some existing benchmarks and methods target retrieval for fact-checking, a comprehensive real-world open-domain benchmark has been lacking. In this paper, we present a real-world retrieval benchmark FactIR, derived from Factiverse production logs, enhanced with human annotations. We rigorously evaluate state-of-the-art retrieval models in a zero-shot setup on FactIR and offer insights for developing practical retrieval systems for fact-checking. Code and data are available at https://github.com/factiverse/factIR.
4th International Workshop on Natural Language Processing for Knowledge Graph Construction
Authors: Edlira Vakaj, Nandana Mihindukulasooriya, Sanju Tiwari, Sergio José Rodríguez Méndez
Venue: WWW
Year: 2025

Knowledge graphs (KG) are becoming increasingly popular, at the heart of Gartner's emerging tech impact radar, especially as a complementary theme for addressing the challenges of recent advances in natural language processing (NLP) with large language models related to responsible AI such as fairness, transparency, accountability, and explainability. Sir Tim Berners-Lee's seminal work ''Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web'', envisioned a World Wide Web where information is not only accessible but structured, allowing machines to interpret data meaningfully. This vision laid the groundwork for technologies such as RDF (Resource Description Framework) and OWL (Web Ontology Language), which serve as foundational components for modern KGs. However, the process of building domain-specific KGs from extensive text corpora is highly complex and resource-intensive, requiring careful task design for entity recognition, disambiguation, and relationship extraction, among others. These tasks are essential to ensure accuracy and relevance in knowledge representation, but they pose considerable challenges. Addressing these complexities is crucial for the continued advancement and application of KGs across domains. In this context, the 4th NLP4KGC workshop is held to create a collaborative platform for researchers, practitioners, and industry experts in NLP and KG construction. Following the success and growing community engagement in the previous three editions, this year's workshop aims to deepen collaboration and encourage innovative solutions in this rapidly evolving field. The 4th NLP4KGC will continue to bridge academia and industry, fostering the exchange of insights, tools, and methodologies at the intersection of NLP and KG development. The 4th NLP4KGC will consist of five accepted papers and three keynotes from distinguished speakers.
Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training
Authors: Kaibin Wang, Qiang He, Zeqian Dong, Rui Chen, Chuan He, Caslon Chua, Feifei Chen, Yun Yang
Venue: WWW
Year: 2025

In an edge-assisted federated learning (FL) system, edge servers aggregate the local models from the clients within their coverage areas to produce intermediate models for the production of the global model. This significantly reduces the communication overhead incurred during the FL process. To accelerate model convergence, FedEdge, the state-of-the-art edge-assisted FL system, trains clients' models in local federations when they wait for the global model in each training round. However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. It introduces a model-contrastive loss to facilitate personalized local federated training by driving clients' local models away from the global model and close to their corresponding intermediate models. In addition, Maverick includes anomalous models in contrastive local training as negative samples to accelerate the convergence of clients' local models. Extensive experiments are conducted on three widely-used models trained on three datasets to comprehensively evaluate the performance of Maverick. Compared to state-of-the-art edge-assisted FL systems, Maverick accelerates model convergence by up to 16.2x and improves model accuracy by up to 12.7%.
FiGraph: A Dynamic Heterogeneous Graph Dataset for Financial Anomaly Detection
Authors: Xiaoguang Wang, Chenxu Wang, Huanlong Liu, Mengqin Wang, Tao Qin, Pinghui Wang
Venue: WWW
Year: 2025

Graph anomaly detection (GAD) detects anomalous nodes in real-world networks by capturing topological and attributive information. Although a few of benchmark datasets are publicly available, there is a lack of dynamic heterogeneous graph datasets for advanced GAD research. To address this issue, this paper presents FiGraph, a real-world dynamic heterogeneous graph with ground-truth labels for financial anomaly detection. It consists of nine graph snapshots from 2014 to 2022 and comprises 730,408 nodes and 1,040,997 edges. There are five types of nodes and four types of edges. Only partial nodes (target nodes) need to be identified, and these nodes have multimodal attributes that incorporate tabular data and textual input. The target nodes that correspond to the same entity in different snapshots may have different labels. The remaining nodes do not need to be categorized, serving as background nodes without attributes. In addition, multiple relations can exist simultaneously between the same pair of nodes. For example, two companies may share investment and supply chain relations, while a company and a human may share both investment and related-party transaction relations. These characteristics make FiGraph more realistic and complex than existing GAD datasets, encouraging the development of more effective GAD models. This paper details the construction and properties of FiGraph and discusses promising use cases. The dataset is publicly available at: https://github.com/XiaoguangWang23/FiGraph.
Learning Feasible Causal Algorithmic Recourse: A Prior Structural Knowledge Free Approach
Authors: Haotian Wang, Hao Zou, Xueguang Zhou, Shangwen Wang, Wenjing Yang, Peng Cui
Venue: WWW
Year: 2025

Algorithmic recourse (AR) has made significant progress by identifying small perturbations in input features that can alter predictions, which provide a data-centric approach to understand decisions from diverse black-box models on the Web. Towards the feasibility issue, i.e., whether the recoursed examples provides actionable and reliable recommendations to end-users, causal algorithmic recourse have incorporated structural causal model (SCM) to preserve the realistic constraints among input features. For instance, preserving structural causal knowledge between "age" and "educational level" can avoid generating samples with decreasing age and increasing educational level. However, previous causal AR methods suffer from the requirement of prior structural causal knowledge, e.g., prior causal graph or the whole SCM, which restricts the realistic application of causal AR methods. To bridge this gap, we aim to develop a novel framework for causal algorithmic recourse that does not rely on neither prior causal graph or prior SCM. Since identifying counterfactuals without causal graph is impossible, we instead propose to approximate and constrain the variation of the perturbed components, i.e., the exogenous noise variables, by formulating the generation of AR as the structure-preserving intervention. With the aid of development in non-linear Independent Component Analysis (ICA), our method can further achieve theoretically guaranteed constraints on such variation of exogeneous variables. Experimental results on synthetic, semi-synthetic, and real-world data demonstrate the effectiveness of our proposed methods without any prior causal graph or SCM knowledge.
Debias Can Be Unreliable: Mitigating Bias in Evaluating Debiasing Recommendation
Authors: Chengbing Wang, Wentao Shi, Jizhi Zhang, Wenjie Wang, Hang Pan, Fuli Feng
Venue: WWW
Year: 2025

Recent work has improved recommendation models remarkably by equipping them with debiasing methods. Due to the unavailability of fully-exposed datasets, most existing approaches resort to randomly-exposed datasets as a proxy for evaluating debiased models, employing traditional evaluation scheme to represent the recommendation performance. However, in this study, we reveal that traditional evaluation scheme is not suitable for randomly-exposed datasets, leading to inconsistency between the Recall performance obtained using randomly-exposed datasets and that obtained using fully-exposed datasets. To bridge the gap, we propose the Unbiased Recall Evaluation (URE) scheme, which adjusts the utilization of randomly-exposed datasets to unbiasedly estimate the true Recall performance on fully-exposed datasets. We provide theoretical evidence to demonstrate the rationality of URE and perform extensive experiments on real-world datasets to validate its soundness. Our code and datasets are available at https://anonymous.4open.science/r/URE-6B64/.