OpenRaiser/Intern-Atlas huggingface dataset:
Tracing Bias for Fairer Content-Based Misinformation Detection
Authors: Mayra Russo, Flavio Merenda, José Manuél Gómez-Pérez, Maria-Esther Vidal
Venue: WWW
Year: 2025

Despite the benefits attributed to AI systems, their deployment across domains still present challenges to society. In the case of automated misinformation detection, research has uncovered that benefits derived from their application are unequally distributed amongst different stakeholders, calling to attention the need to audit these AI systems for biases and other sources of harm. We present a hybrid AI system designed to trace biases from input data, enriched with semantic descriptions. Using boxology design patterns, we illustrate the integration of a semantic model with an AI system to enable bias tracing. In our case study, we assess fine-tuned language models for content-based misinformation detection, and adapt existing bias detection and mitigation techniques to transform data based on demographic signifiers and measure model fairness. Our findings show evidence that, on average, the evaluated datasets demonstrate a stark gender and geographical biases. Further, we observe that models trained on demographically transformed data demonstrate higher fairness. These results underscore the importance of curated and diverse data and of managing biases plaguing language models at task level.
Integrating Edge Computing-Based Ethical Decision-Making Framework for AI
Authors: Jimin Ryu, Hyeyoung Kim, Yong Ik Yoon
Venue: WWW
Year: 2025

This paper proposes a novel framework for integrating ethical decision-making into AI systems using edge computing, reinforced by advanced middleware design and reinforcement learning algorithms. The framework addresses the growing need for real-time, ethical decision-making in distributed computing environments, particularly where AI systems must operate across diverse cultural and social contexts. Key components of the framework include a Bias Detection Algorithm(BDA) and a Self-Inspection Algorithm(SIA), which are embedded within the middleware layers to ensure the consistent application of ethical standards throughout the data processing and decision-making pipeline. The BDA operates at the data input stage to filter out biases, while the SIA evaluates and adjusts AI responses during scenario-based training. Reinforcement learning techniques are employed to enable the AI to adapt and improve its ethical decision-making capabilities over time, informed by continuous feedback from human-centered evaluations. The framework is designed to be scalable and adaptable, ensuring that AI systems can maintain high ethical standards even in complex and dynamic environments such as smart cities and autonomous vehicle networks. By integrating ethical considerations directly into the edge computing process, this approach enhances both the transparency and reliability of AI-driven decisions.
Multi-view Topic Modeling Using Multi-text Representations
Authors: Abilasha S, Rafika Boutalbi, Stéphane Delliaux
Venue: WWW
Year: 2025

Topic modeling has been widely used for decades across various applications, particularly in web data analysis. The Latent Dirichlet Allocation (LDA) is based on statistical modeling, where words are modeled as a distribution of words, and documents as a distribution of topics. Today, there are several text ways to represent texts, from the simplest one Bag-of-Words to the recent word embedding. Two main classes of word embeddings exist, static word embeddings (Word2Vec, Glove, etc) and contextual word embedding (BERT, RoBERTa, etc). And, some recent works, showed the interest and the effectiveness of integrating such text representations to improve topic modeling performance. However, in the unsupervised context of topic modeling, there is no prior knowledge that can ensure the effectiveness of a specific text representation. Also, they often face the challenge of topic collapsing, where identified topics become semantically redundant, resulting in overly similar topics, limited topic diversity, and reduced interpretability of the model. In this paper, we propose a new topic modeling approach that can consider multi-text representations simultaneously named Multi-view Topic Modeling (MTM). The MTM algorithm is able to discover topics based on several text representations and generate multi-view topic embeddings allowing us to interpret the results considering several views. We evaluate the MTM algorithm on five real-world datasets and show the effectiveness of the proposed algorithm in terms of topic coherences and clustering.
Dimension Mask Layer: Optimizing Embedding Efficiency for Scalable ID-based Models
Authors: Srijan Saket, Ikuhiro Ihara, Vaibhav Sharma, Md. Danish Kalim
Venue: WWW
Year: 2025

In modern recommendation systems and social media platforms like Meta, TikTok, and Instagram, large-scale ID-based features often require embedding tables that consume significant memory. Managing these embedding sizes can be challenging, leading to bulky models that are harder to deploy and maintain. In this paper, we introduce a method to automatically determine the optimal embedding size for ID features, significantly reducing the model size while maintaining performance. Our approach involves defining a custom Keras layer called the dimension mask layer, which sits directly after the embedding lookup. This layer trims the embedding vector by allowing only the first N dimensions to pass through. By doing this, we can reduce the input feature dimension by more than half with minimal or no loss in model performance metrics. This reduction helps cut down the memory footprint of the model and lowers the risk of overfitting due to multicollinearity. Through offline experiments on public datasets and an online A/B test on a real production dataset, we demonstrate that using a dimension mask layer can shrink the effective embedding dimension by 40-50%, leading to substantial improvements in memory efficiency. This method provides a scalable solution for platforms dealing with a high volume of ID features, optimizing both resource usage and model performance.
MVis4SID: Multimodal Visual Interactive System for Suicide Ideation Detection
Authors: Md. Kowsar Hossain Sakib, Md. Rafiqul Islam, Shanjita Akter Prome, Sadia Jahan, David Asirvatham, Neethiahnanthan Ari Ragavan, Cesar Sanín, Weidong Huang
Venue: WWW
Year: 2025

Suicide has become a global health crisis driven by a complex mix of social, environmental, and psychological factors. The prevalence of suicide has emerged as a worldwide health crisis, presenting a multitude of intricate issues. The correlation between increased concerns about sustainability, climate change, and ecological degradation and the subsequent burden on psychological health is an increasing concern. Although Suicidal Ideation Detection (SID) has been effectively addressed by prior research using Machine Learning (ML) and Deep Learning (DL) models, many studies fail to handle the complexities of real-time detection. To overcome these flaws, we developed an novel system called Multimodal Visual interactive system for Suicide Ideation Detection (MVis4SID), which utilizes visual insights and real-time data to provide a more comprehensive exploration of suicidal thoughts. MVis4SID employs multimodal aspects, including click or touch, text, and voice, to visualize the performance metrics for suicidal ideation using ML and DL models; thus, offering a more holistic solution to this escalating problem. The rigorous evaluation of the user study shows that MVis4SID not only outperforms existing methods in accuracy, but also explains the responsible use of the underlying AI approach, demonstrating its potential to improve SID and intervention strategies. In addition, it provides a user-friendly interface for decision-makers, allowing them to represent the model performance and the efficient visual evaluation.
To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders
Authors: Guillaume Salha-Galvan, Jiaying Xu
Venue: WWW
Year: 2025

This paper investigates the understudied practice of weight sharing (WS) in variational graph autoencoders (VGAE). WS presents both benefits and drawbacks for VGAE model design and node embedding learning, leaving its overall relevance unclear and the question of whether it should be adopted unresolved. We rigorously analyze its implications and, through extensive experiments on a wide range of graphs and VGAE variants, demonstrate that the benefits of WS consistently outweigh its drawbacks. Based on our findings, we recommend WS as an effective approach to optimize, regularize, and simplify VGAE models without significant performance loss.
STGAN: Detecting Host Threats via Fusion of Spatial-Temporal Features in Host Provenance Graphs
Authors: Anyuan Sang, Xuezheng Fan, Li Yang, Yuchen Wang, Lu Zhou, Junbo Jia, Huipeng Yang
Venue: WWW
Year: 2025

As the complexity and frequency of cyberattacks, such as Advanced Persistent Threats (APTs) and ransomware, continue to escalate, traditional anomaly detection methods have proven inadequate in addressing these sophisticated, multi-faceted threats. Recently, Host Provenance Graphs (HPGs) have played a crucial role in analyzing system-level interactions, detecting anomalous behaviors, and tracing attack chains. However, existing provenance-based detection methods primarily rely on single-dimensional feature analysis, which fails to capture the dynamic and multi-dimensional patterns of modern APT attacks, resulting in insufficient detection performance. To overcome this limitation, we introduce STGAN, a model that integrates spatial-temporal graphs into host provenance graph modeling. STGAN applies temporal and spatial encoding to dynamic provenance graphs to extract temporal, spatial, and semantic features, constructing a comprehensive feature representation. This representation is further fused and enhanced using a multi-head self-attention mechanism, followed by anomaly detection. Through extensive evaluations on three widely-used provenance graph datasets, we demonstrate that our approach consistently outperforms current state-of-the-art techniques in terms of detection performance. Additionally, we contribute to the research community by releasing our datasets and code, facilitating further exploration and validation.
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
Authors: Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski, Kun Su, Liam Hebert, James Pine, Hubert Pham, Ambarish Jash, Sukhdeep S. Sodhi
Venue: WWW
Year: 2025

Large Language Models (LLMs) have shown remarkable progress in generating human-quality text and engaging in complex reasoning. This presents a unique opportunity to revolutionize conversational recommender systems by enabling them to generate rich, engaging and personalized narratives that go beyond recommendations. However, the lack of suitable datasets limits research in this area. This paper addresses this challenge by making two key contributions. First, we introduce REGEN Reviews Enhanced with GEnerative Narratives, a new dataset extending the Amazon Product Reviews with rich user narratives. Furthermore, we perform an extensive automated evaluation of the dataset using a rater LLM. Second, the paper introduces a fusion architecture (CF model with an LLM) which serves as a baseline for REGEN. To the best of our knowledge, this represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives. We demonstrate that LLMs can effectively learn from simple fusion architectures utilizing interaction-based CF embeddings, and this can be further enhanced using the metadata and personalization data associated with items. Our experiments show that combining CF and content embeddings leads to improvements of 4-12% in key language metrics compared to using either type of embedding individually. We also provide an analysis to interpret their contributions to this new generative task.
Falling Walls, WWW, Modern AI, and the Future of the Universe
Authors: Jürgen Schmidhuber
Venue: WWW
Year: 2025

Around 1990, the Berlin Wall came down, the WWW was born at CERN, mobile phones became popular, self-driving cars appeared in traffic, and modern AI based on very deep artificial neural networks emerged, including the principles behind the G, P, and T in ChatGPT. I place these events in the history of the universe since the Big Bang, and discuss what's next: not just AI behind the screen in the virtual world, but real AI for real robots in the real world, connected through a WWW of machines. Intelligent (but not necessarily super-intelligent) robots that can learn to operate the tools and machines operated by humans can also build (and repair when needed) more of their own kind. This will culminate in life-like, self-replicating and self-improving machine civilisations, which represent the ultimate form of upscaling, and will shape the long-term future of the entire cosmos. The wonderful short-term side effect is that our AI will continue to make people's lives longer, healthier and easier.
Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessment
Authors: Julian A. Schnabel, Johanne R. Trippas, Falk Scholer, Danula Hettiachchi
Venue: WWW
Year: 2025

The effectiveness of search systems is evaluated using relevance labels that indicate the usefulness of documents for specific queries and users. While obtaining these relevance labels from real users is ideal, scaling such data collection is challenging. Consequently, third-party annotators are employed, but their inconsistent accuracy demands costly auditing, training, and monitoring. We propose an LLM-based modular classification pipeline that divides the relevance assessment task into multiple stages, each utilising different prompts and models of varying sizes and capabilities. Applied to TREC Deep Learning (TREC-DL), one of our approaches showed an 18.4% Krippendorff's α accuracy increase over OpenAI's GPT-4o mini while maintaining a cost of about 0.2 USD per million input tokens, offering a more efficient and scalable solution for relevance assessment. This approach beats the baseline performance of GPT-4o (5 USD). With a pipeline approach, even the accuracy of the GPT-4o flagship model, measured in α, could be improved by 9.7%.
Engineering Prompts for Spatial Questions
Authors: Nicole R. Schneider, Nandini Ramachandran, Kent O&apos, Sullivan, Hanan Samet
Venue: WWW
Year: 2025

Large Language Models (LLMs) are often used for tasks that involve reasoning about the physical world, like recommending travel itineraries. However, success at these tasks requires the LLM to have been exposed to the relevant places, which is not true for lesser-known or alternatively named places, like Indigenous place names. Our prompting technique handles this issue using Retrieval Augmented Generation, encoding a spatial graph of common places and a mapping to their Indigenous alternatives. Our method improves LLM performance on spatial tasks involving lesser-known place names, thus advancing AI fairness.
Explainability-Driven Quality Assessment for Rule-Based Systems
Authors: Oshani Seneviratne, Brendan Capuzzo, William Van Woensel
Venue: WWW
Year: 2025

This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems based on dataset-driven insights. The traditional method for rule induction from data typically requires labor-intensive labeling and data-driven learning. This framework provides an alternative and instead allows for the data-driven refinement of existing rules: it generates explanations of rule inferences and leverages human interpretation to refine rules. It leverages four complementary explanation types-trace-based, contextual, contrastive, and counterfactual-providing diverse perspectives for debugging, validating, and ultimately refining rules. By embedding explainability into the reasoning architecture, the framework enables knowledge engineers to address inconsistencies, optimize thresholds, and ensure fairness, transparency, and interpretability in decision-making processes. Its practicality is demonstrated through a use case in finance.
Exploring Search Volumes of Terms in Web Portals for Accurate Event-Aware Traffic Prediction
Authors: Dong-Hyuk Seo, Hyomin Shin, Sang-Wook Kim
Venue: WWW
Year: 2025

Traffic prediction is quite challenging in periods of special events (e.g., Thanksgiving and Christmas), where traffic patterns significantly differ from those in normal periods. To address this challenge, we propose leveraging the search volumes of terms available in web online portals as auxiliary data to identify and model such events. After finding that search volumes for traffic-related terms spike during events, we confirm their clear potential for enhancing traffic prediction accuracy by exploring their correlation with traffic flow. Based on these findings, we propose a novel traffic prediction framework, named VESTA, that exploits the search volumes of terms as well as traffic flows. Through extensive experiments, we show VESTA significantly and consistently outperforms state-of- the-art traffic prediction models in accuracy. Our code is available at https://github.com/Bigdasgit/VESTA.
Annotation Tool and Dataset for Fact-Checking Podcasts
Authors: Vinay Setty, Adam James Becker
Venue: WWW
Year: 2025

Podcasts are a popular medium on the web, featuring diverse and multilingual content that often includes unverified claims. Fact-checking podcasts is a challenging task, requiring transcription, annotation, and claim verification, all while preserving the contextual details of spoken content. Our tool offers a novel approach to tackle these challenges by enabling real-time annotation of podcasts during playback. This unique capability allows users to listen to the podcast and annotate key elements, such as check-worthy claims, claim spans, and contextual errors, simultaneously. By integrating advanced transcription models like OpenAI's Whisper and leveraging crowdsourced annotations, we create high-quality datasets to fine-tune multilingual transformer models such as XLM-RoBERTa for tasks like claim detection and stance classification. Furthermore, we release the annotated podcast transcripts and sample annotations with preliminary experiments.
Trffc: Efficient Traffic Forecasting through Adaptive Spatio-Temporal Graph Reduction
Authors: Nasrin Shabani, Amin Beheshti, Yuankai Qi, Venus Haghighi, Sahar Moradizeyveh, Quan Z. Sheng
Venue: WWW
Year: 2025

The complex interconnections in modern traffic networks pose significant challenges for storage, noise reduction, and interpretability, all of which are essential for accurate forecasting and analysis-key factors in effective traffic management and planning. Graph sparsification mitigates these challenges by reducing edges while preserving essential structures, enabling more efficient processing. However, existing sparsification algorithms are often static and fail to address the dynamic nature of evolving traffic networks, reflecting changes in traffic flow patterns. This paper introduces, Trffc, a reinforcement learning-based framework to sparsify temporal traffic graphs, enhancing both the scalability of forecasting and the recognition of emerging patterns. Through practical demonstrations, experiments on real-world temporal traffic data show that Trffc preserves key network properties and significantly boosts the performance of traffic forecasting models.
FedKDShap: Enhancing Federated Learning via Shapley Values Driven Knowledge Distillation on Non-IID Data
Authors: Nazmus Shakib Shadin, Xinyue Zhang
Venue: WWW
Year: 2025

Federated Learning (FL) has achieved significant popularity in privacy-preserving distributed learning, wherein data remains on edge devices, ensuring data security and user privacy. Regardless of its advantages, FL has considerable challenges, including the non-independent and identically distributed (non-IID) nature of data across clients, degradation in performance compared to centralized learning methods, and communication efficiency issues due to frequent exchanges of large model updates between clients and the server. To overcome these challenges, FL combined with knowledge distillation (KD), which is a decentralized learning methodology that facilitates collaborative model training across several devices or clients with data privacy. Traditional KD generally focuses on the transfer of knowledge through logits; however, this methodology omits the importance of intermediate feature representations within the model. To address this limitation, we propose incorporating Shapley Additive Explanations (SHAP) or Shapley values into knowledge distillation (KD) methods. Shapley values quantify feature importance, enabling the transfer of critical feature contributions, and thereby enhancing the effectiveness of KD. In this work, we propose a novel decentralized machine learning approach, named FedKDShap, refined federated learning with Shapley values-informed KD which prioritizes performance, interpretability, resource efficiency, and feature importance within the distillation process to optimize knowledge transfer from a high-capacity teacher model to a lightweight student model. This integration not only reduces communication demands on resource-constrained devices but also enhances model convergence in non-IID data settings by embedding Shapley values into the KD loss function. Our experiment leverages benchmark datasets to simulate real-world non-IID data distribution which also demonstrates that the FedKDShap method enhances model accuracy and outperforms state-of-the-art architectures. Our code repository is available at: https://github.com/shadhin39/FedKDShap
Entity-Aware Optimal Transport and Residual Attention for Multimodal Content Moderation
Authors: Siddhant Bikram Shah, Shuvam Shiwakoti, Touhid Bhuiyan, Mohammad Ali Moni, Surendrabikram Thapa, Usman Naseem
Venue: WWW
Year: 2025

The increasing prevalence of memes on social media platforms has amplified both the positive and negative impact of these highly shareable, multimodal artifacts. While memes can be humorous and engaging, they can also serve as vehicles for hateful or harmful content that targets specific social, ethnic, or political groups. In this paper, we propose ImTOTMeme, a novel framework for harmful meme detection that combines an optimal transport-based alignment mechanism with global residual interactions to better capture both local and contextual cues. We leverage CLIP embeddings for initial image and text representations and employ Sinkhorn iteration to learn a minimal-cost matching between fine-grained visual tokens and OCR-extracted text tokens. We further incorporate facial embeddings and entity information, allowing for more nuanced analysis of memes involving human subjects or contextual references. Through experiments on four publicly available datasets: Harm-C, Harm-P, FHM, and MultiOFF, we demonstrate that ImTOTMeme achieves competitive accuracy in both binary and multi-class settings. We further conduct an ablation study to verify the significance of each component in our framework, and use LIME-based visualizations to provide deeper interpretability into the model's classification decisions. Our findings highlight that an approach that balances local token-level alignment with broader contextual modeling can effectively detect harmful memes across diverse topical domains, paving the way for more robust and transparent content moderation on social media.