OpenRaiser/Intern-Atlas huggingface dataset:
Data and AI Markets in a Nutshell
Authors: Jian Pei, Xiaohui Yu
Venue: WWW
Year: 2025

Data and AI model services, often regarded as the driving force of the digital and AI economy, are powering a wide range of applications and creating significant business opportunities. Data and AI model service pipelines are frequently enabled by data and AI markets, yet the landscape of these markets remains relatively unfamiliar to many professionals in data science, machine learning, and AI. This tutorial provides an intuitive, concept-driven, and example-based introduction to this emerging domain. Through practical examples, we will explore the motivations, fundamental principles, key techniques, major challenges, and research and development opportunities in data and AI markets. The tutorial will cover a series of important topics, including the core concepts, market models, and operational mechanisms-such as privacy, security, and deployment-as well as market administration, offering a comprehensive foundation for understanding and advancing this transformative field.
Hypergraph-based Temporal Modelling of Repeated Intent for Sequential Recommendation
Authors: Andreas Peintner, Amir Reza Mohammadi, Michael Müller, Eva Zangerle
Venue: WWW
Year: 2025

In sequential recommendation scenarios, user intent is a key driver of consumption behavior. However, consumption intents are usually latent and hence, difficult to leverage for recommender systems. Additionally, intents can be of repeated nature (e.g. yearly shopping for christmas gifts or buying a new phone), which has not been exploited by previous approaches. To navigate these impediments we propose the HyperHawkes model which models user sessions via hypergraphs and extracts user intents via contrastive clustering. We use Hawkes Processes to model the temporal dynamics of intents, namely repeated consumption patterns and long-term interests of users. For short-term interest adaption, which is more fine-grained than intent-level modeling, we use a multi-level attention mixture network and fuse long-term and short-term signals. We use the generalized expectation-maximization (EM) framework for training the model by alternating between intent representation learning and optimizing parameters of the long- and short-term modules. Extensive experiments on four real-world datasets from different domains show that HyperHawkes significantly outperforms existing state-of-the-art methods.
The Web of Data Quality Workshop
Authors: Maria Angela Pellegrino, Anisa Rula, Anelia Kurteva
Venue: WWW
Year: 2025

Ensuring high-quality data is critical for the reliability and effectiveness of Artificial Intelligence (AI)-driven decision-making, data integration, and web applications. However, challenges such as accessibility, consistency, and interpretability persist across the Web of Data. The Web of Data Quality workshop brings together researchers and practitioners to discuss standardized assessment methodologies, AI-driven quality monitoring, and stakeholder-centric approaches for improving data reliability. The workshop features expert keynotes, paper presentations, interactive discussions, and lightning talks with industry and academic leaders. Our proposed outline aims to advance best practices, foster collaboration, and explore future directions in data quality research and applications.
From Quality Reports to Knowledge Graphs: a Case Study on CSV-to-KG Transformation
Authors: Maria Angela Pellegrino, Gabriele Tuozzo
Venue: WWW
Year: 2025

The construction of Knowledge Graphs (KGs) often demands substantial manual effort and domain expertise, especially when converting structured data formats like CSV files into KGs. Recent advancements in Large Language Models (LLMs) offer promising avenues to simplify this process through prompt engineering. This study investigates various prompting strategies-zero-shot, one-shot, prompt chaining, and a hybrid approach-to enable LLMs to automate the creation of KGs from CSV files. Using a dataset containing quality metrics for 2,026 KGs generated by KGHeartBeat, the paper assesses the performance of GPT-4o, GPT-o1 mini, Claude 3.5 Sonnet, and Gemini 1.5 pro, across different prompt configurations. The findings reveal that the hybrid approach consistently produces the most accurate and complete KGs, effectively addressing challenges related to scalability and complexity.
TEARS: Text Representations for Scrutable Recommendations
Authors: Emiliano Penaloza, Olivier Gouvert, Haolun Wu, Laurent Charlin
Venue: WWW
Year: 2025

Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretability. Moreover, these systems offer limited control to users over their recommendations. Inspired by recent work, we introduce TExtuAl Representations for Scrutable recommendations (TEARS) to address these challenges. Instead of representing a user's interests through a latent embedding, TEARS encodes them in natural text, providing transparency and allowing users to edit them. To do so, TEARS uses a modern LLM to generate user summaries based on user preferences. Using these summaries, we take a hybrid approach where we use an optimal transport procedure to align the summaries' representation with the learned representation of a standard VAE for collaborative filtering. We find this approach can surpass the performance of popular VAE models while providing user-controllable recommendations. We also analyze the controllability of TEARS through three simulated user tasks to evaluate the effectiveness of a user editing its summary. A more detailed version of this manuscript with more experiments, baselines and detail is provided on arXiv.
MedShort: Medicine Shortage Detection on Social Media with Privacy Preservation
Authors: Xueping Peng, Peng Yan, Tao Shen, Ming Xie, Guodong Long
Venue: WWW
Year: 2025

Medicine shortages pose a significant threat to healthcare systems worldwide, disrupting patient care and increasing costs. Social media platforms, with their real-time user-posted content, offer a valuable opportunity to detect early signs of such shortages. This paper introduces MedShort, a novel framework that leverages large language models (LLMs) to monitor, analyze, and detect Medicine Shortages from social media data while preserving user privacy. Key features include filtering relevant posts, extracting specific medicines and geographic insights, and providing actionable analytics for stakeholders. Experimental results show MedShort's effectiveness, with refined LLMs achieving high accuracy, precision, and recall across several medicines. By integrating privacy-preserving mechanisms, MedShort ensures ethical data handling, enabling near real-time and scalable monitoring of medicine shortages.
Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing
Authors: Minod Perera, Sheik Mohammad Mostakim Fattah, Sajib Mistry, Aneesh Krishna
Venue: WWW
Year: 2025

Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation closer to devices to reduce latency and server load. However, optimal performance requires advanced optimization techniques. We propose a novel solution combining Adaptive Particle Swarm Optimization (APSO) with Reinforcement Learning, specifically Soft Actor-Critic (SAC), to enhance task offloading decisions in MEC environments. This hybrid approach uses swarm intelligence and predictive models to adapt to dynamic factors, including human interactions and environmental changes. Our method improves resource management and service quality, achieving optimal task offloading and resource distribution in IIoT edge computing.
Foreword: Towards a Safer Web for Women - First International Workshop on Protecting Women Online
Authors: Ángel Pavón Pérez, Miriam Fernández, Tracie Farrell, Debora Nozza, Christine de Kock
Venue: WWW
Year: 2025

The first international workshop on protecting women online, Towards a Safer Web for Women (TSWW), was held in conjunction with the Web Conference 2025. The workshop addresses the urgent and growing issue of online violence against women and girls, identifying and addressing the current challenges. As online and digital environments become increasingly hostile due to the proliferation of hate speech, deepfake abuse, and algorithmic gender bias, there is an urgent need for international and interdisciplinary collaboration to develop effective solutions and mitigation strategies. This workshop brings together researchers primarily from the computer science domain (e.g. natural language processing and machine learning experts) alongside experts in other domains (e.g. legislation and human behaviour experts), recognising the critical role of interdisciplinary collaboration in addressing this complex issue and encouraging knowledge exchange and innovation across fields. In this paper, we present an overview of the inaugural edition of the TSWW workshop.
Topic-wise Exploration of the Telegram Group-verse
Authors: Alessandro Perlo, Giordano Paoletti, Nikhil Jha, Luca Vassio, Jussara M. Almeida, Marco Mellia
Venue: WWW
Year: 2025

Although Telegram is currently one of the most popular instant messaging apps in the world, previous studies have mainly focused on analysing discussions on specific angles and topics. In this paper, we present a broad analysis of publicly accessible groups that cover a wide range of discussions, including Education, Erotic, Politics, and Cryptocurrencies. How do people interact with different topic groups? Is there any common or peculiar behaviour? We engineer and offer an open-source tool to automate the collection of messages from Telegram groups, a non-straightforward problem. We use it to collect more than 51 million messages from 669 groups. Here, we present a first-of-its-kind, per-topic analysis, contrasting the users' activity patterns from different angles - the language, the presence of bots, the type and volume of shared media content, links to external platforms, etc. Our results confirm some anecdotal evidence, e.g., indications of spamming behaviour, and unveil some unexpected findings, e.g., the different sharing patterns of video and message length in groups of different topics. Our research provides a horizontal analysis of the public group in Telegram across various general topics, establishing a foundation for future studies that can delve deeper into user interactions and content dynamics within this unique messaging environment.
The Massive Problem of Remote Changes in Ontology Reuse
Authors: Romana Pernisch, Daniil Dobriy, Axel Polleres
Venue: WWW
Year: 2025

Reusing existing datasets is a common practice in the Semantic Web, and it is also highly encouraged. Previous work on linking datasets has introduced and analysed different ways of linking but has failed to discuss the meaning and intentions behind the reuse of entities. This problem is aggravated by the fact Knowledge Graphs (KGs) and ontologies change over time. Currently, we lack an analysis of what impact the asymmetric evolution of the reused KGs has. Therefore, in this short paper, we evaluate how severe the problem of impacting remote changes is in practice by analysing the evolution of real-world ontologies. To this end, we collect a large corpus of open biomedical ontologies (759 ontologies) and provide statistics on their evolution, reuse (46.65%) and impacting changes (33.38%). We find that these KGs experience enormous amounts of impacting term reuse (7.59%), and the extent of the problem has been overlooked on a massive scale.
RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data
Authors: Luan Pham, Hongyu Zhang, Huong Ha, Flora Salim, Xiuzhen Zhang
Venue: WWW
Year: 2025

Root cause analysis (RCA) for microservice systems has gained significant attention in recent years. However, there is still no standard benchmark that includes large-scale datasets and supports comprehensive evaluation environments. In this paper, we introduce RCAEval, an open-source benchmark that provides datasets and an evaluation environment for RCA in microservice systems. First, we introduce three comprehensive datasets comprising 735 failure cases collected from three microservice systems, covering various fault types observed in real-world failures. Second, we present a comprehensive evaluation framework that includes fifteen reproducible baselines covering a wide range of RCA approaches, with the ability to evaluate both coarse-grained and fine-grained RCA. We hope that this ready-to-use benchmark will enable researchers and practitioners to conduct extensive analysis and pave the way for robust new solutions for RCA of microservice systems.
Passage: Ensuring Completeness and Responsiveness of Public SPARQL Endpoints with SPARQL Continuation Queries
Authors: Thi Hoang Thi Pham, Gabriela Montoya, Brice Nédelec, Hala Skaf-Molli, Pascal Molli
Venue: WWW
Year: 2025

Being able to query online public knowledge graphs such as Wikidata or DBpedia is extremely valuable. However, these queries can be interrupted due to the fair use policies enforced by SPARQL endpoint providers, leading to incomplete results. While these policies help maintain the responsiveness of public SPARQL endpoints, they compromise the completeness of query results, which limits the feasibility of various downstream tasks. Ideally, we should not have to choose between completeness and responsiveness. To address this issue, we introduce and formalize the concept of SPARQL continuation queries. When a SPARQL endpoint interrupts a query, it returns partial results along with a SPARQL continuation query to retrieve the remaining results. If the continuation query is also interrupted, the process repeats, generating further continuation queries until the complete results are obtained. In our experimentation, we show that our continuation server PASSAGE ensures completeness and responsiveness while delivering high performance.
Social Bots Meet Large Language Model: Political Bias and Social Learning Inspired Mitigation Strategies
Authors: Jinghua Piao, Zhihong Lu, Chen Gao, Yong Li
Venue: WWW
Year: 2025

Recent advances in the large language models (LLM) have empowered traditional bots to gain human-level intelligence and exhibit human-like social behaviors, giving rise to a new form of LLM-driven social agents. However, the inherent limitations in LLMs could potentially result in politically biased behaviors of these agents, posing unexpected risks to human society. While great efforts have been made to examine political bias and related concerns in traditional bots and LLMs, little is known about the existence, unique characteristics, underlying origins, and potential mitigation strategies of this bias in LLM-driven social agents. To address this gap, we systematically assess political bias in LLM-driven social agents, by examining how it emerges as these agents self-reflect, communicate, and understand others during social interactions. Through designing and implementing social experiments, we discover that this bias consistently manifests in the social behaviors of agents driven by diverse LLMs, across nine key political topics. Inspired by the social learning theory, we propose to mitigate political bias by guiding these agents to emulate how humans learn to behave. By incorporating self-regulated and role-model learning processes, we reduce their political bias by 4.89% to 51.26% across diverse LLMs and topics, demonstrating the effectiveness and generalizability of the proposed strategy. This study not only advances the understanding of political bias in emerging LLM-driven agents, but also offers insights into harnessing social bots for social good.
Tracking the 2024 US Presidential Election Chatter on TikTok: A Public Multimodal Dataset
Authors: Gabriela Pinto, Charles Bickham, Tanishq Salkar, Joyston Menezes, Luca Luceri, Emilio Ferrara
Venue: WWW
Year: 2025

This paper presents the TikTok 2024 U.S. Presidential Election Dataset, a large-scale resource designed to advance research into political communication and social media dynamics. The dataset comprises 3.14 million videos published on TikTok between November 1, 2023, and October 16, 2024, including video IDs and transcripts. Data collection was conducted using the TikTok Research API with a comprehensive set of election-related keywords and hashtags, supplemented by third-party tools to address API limitations and expand content coverage. This approach enables the analysis of hashtag co-occurrence networks, revealing politically aligned hashtags based on ideological affiliations, the evolution of top hashtags over time, and summary statistics that highlight the dataset's scale and richness. This dataset offers insights into TikTok's role in shaping electoral discourse by providing a multimodal view of election-related content. It enables researchers to explore critical topics such as coordinated messaging, misinformation spread, audience engagement, and linguistic trends. The TikTok 2024 U.S. Presidential Election Dataset is publicly available on Github and aims to contribute to the broader understanding of social media's impact on democracy and public opinion. The ID of each video and its AI-generated transcript are publicly available on Github: https://github.com/gabbypinto/US2024PresElectionTikToks.
Brick-by-Brick: Cyber-Physical Building Data Classification Challenge
Authors: Arian Prabowo, Xiachong Lin, Imran Razzak, Hao Xue, Matthew Amos, Stephen D. White, Flora D. Salim
Venue: WWW
Year: 2025

optimization essential in combating climate change. Cyber-Physical Buildings, enabled by the integration of Internet-of-Things (IoT) devices and advanced data analytics tools like AI, offer a smart and effective approach to energy management. A key challenge, however, lies in automating the semantic labeling of IoT devices to ensure machine-interpretable data. The ''Brick-by-Brick: Cyber-Physical Building Data Classification Challenge'' aims to tackle this challenge by classifying time-series data from IoT devices within buildings. Participants will engage with a dataset consisting of over 10,000 time-series streams collected over three years across three buildings, representing 91 unique semantic classes. Both the dataset and baselines are established in a published paper. With a total prize pool of 20,000 AUD, the competition is ready to launch in December 2024 and run through February 2025, hosted by AIcrowd. This challenge invites researchers, practitioners, and technologists to drive AI-enabled solutions for advancing the next generation of environmentally sustainable cyber-physical buildings. Additional details on the dataset, benchmark, and code can be found in the official repository ( https://github.com/cruiseresearchgroup/DIEF_BTS). The challenge was published on AIcrowd www.aicrowd.com/challenges/brick-by-brick-2024.
Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
Authors: Deeksha Prahlad, Chanhee Lee, Dongha Kim, Hokeun Kim
Venue: WWW
Year: 2025

The advent of large language models (LLMs) has allowed numerous applications, including the generation of queried responses, to be leveraged in chatbots and other conversational assistants. Being trained on a plethora of data, LLMs often undergo high levels of over-fitting, resulting in the generation of extra and incorrect data, thus causing hallucinations in output generation. One of the root causes of such problems is the lack of timely, factual, and personalized information fed to the LLM. In this paper, we propose an approach to address these problems by introducing retrieval augmented generation (RAG) using knowledge graphs (KGs) to assist the LLM in personalized response generation tailored to the users. KGs have the advantage of storing continuously updated factual information in a structured way. While our KGs can be used for a variety of frequently updated personal data, such as calendar, contact, and location data, we focus on calendar data in this paper. Our experimental results show that our approach works significantly better in understanding personal information and generating accurate responses compared to the baseline LLMs using personal data as text inputs, with a moderate reduction in response time.
Representation Bias in Political Sample Simulations with Large Language Models
Authors: Weihong Qi, Hanjia Lyu, Jiebo Luo
Venue: WWW
Year: 2025

This study explores representation biases in simulating political samples using Large Language Models (LLMs), focusing on vote choice and public opinion. We evaluate seven LLMs from diverse cultural backgrounds with data from the American National Election Studies, German Longitudinal Election Study, and Zuobiao Dataset. The study identifies three dimensions of bias: societal and cultural contexts, demographic groups, and political institutions. Results show higher simulation accuracy for vote choice than public opinion, particularly in English-speaking and democratic countries with bipartisan systems. The cultural backgrounds of development teams significantly influence simulation performance. The findings offer insights into addressing biases in AI-driven computational social science.
Rethink Deep Learning with Invariance in Data Representation
Authors: Shuren Qi, Fei Wang, Tieyong Zeng, Fenglei Fan
Venue: WWW
Year: 2025

Integrating invariance into data representations is a principled design in intelligent systems and web applications. Representations play a fundamental role, where systems and applications are both built on meaningful representations of digital inputs (rather than the raw data). In fact, the proper design/learning of such representations relies on priors w.r.t. the task of interest. Here, the concept of symmetry from the Erlangen Program may be the most fruitful prior - informally, a symmetry of a system is a transformation that leaves a certain property of the system invariant. Symmetry priors are ubiquitous, e.g., translation as a symmetry of the object classification, where object category is invariant under translation. The quest for invariance is as old as pattern recognition and data mining itself. Invariant design has been the cornerstone of various representations in the era before deep learning, such as the SIFT. As we enter the early era of deep learning, the invariance principle is largely ignored and replaced by a data-driven paradigm, such as the CNN. However, this neglect did not last long before they encountered bottlenecks regarding robustness, interpretability, efficiency, and so on. The invariance principle has returned in the era of rethinking deep learning, forming a new field known as Geometric Deep Learning (GDL). In this tutorial, we will give a historical perspective of the invariance in data representations. More importantly, we will identify those research dilemmas, promising works, future directions, and web applications.