OpenRaiser/Intern-Atlas huggingface dataset:
Pirates of Charity: Exploring Donation-based Abuses in Social Media Platforms
Authors: Bhupendra Acharya, Dario Lazzaro, Antonio Emanuele Cinà, Thorsten Holz
Venue: WWW
Year: 2025

With the widespread use of social media, organizations, and individuals use these platforms to raise funds and support causes. Unfortunately, this has led to the rise of scammers in soliciting fraudulent donations. In this study, we conduct a large-scale analysis of donation-based scams on social media platforms. More specifically, we studied profile creation and scam operation fraudulent donation solicitation on X, Instagram, Facebook, YouTube, and Telegram. By collecting data from 151,966 accounts and their 3,053,333 posts related to donations between March 2024 and May 2024, we identified 832 scammers using various techniques to deceive users into making fraudulent donations. Analyzing the fraud communication channels such as phone number, email, and external URL linked, we show that these scamming accounts perform various fraudulent donation schemes, including classic abuse such as fake fundraising website setup, crowdsourcing fundraising, and asking users to communicate via email, phone, and pay via various payment methods. Through collaboration with industry partners PayPal and cryptocurrency abuse database Chainabuse, we further validated the scams and measured the financial losses on these platforms. Our study highlights significant weaknesses in social media platforms' ability to protect users from fraudulent donations. Additionally, we recommended social media platforms, and financial services for taking proactive steps to block these fraudulent activities. Our study provides a foundation for the security community and researchers to automate detecting and mitigating fraudulent donation solicitation on social media platforms.
Multi-Platform Autobidding with and without Predictions
Authors: Gagan Aggarwal, Anupam Gupta, Xizhi Tan, Mingfei Zhao
Venue: WWW
Year: 2025

We study the problem of finding the optimal bidding strategy for an advertiser in a multi-platform auction setting. The competition on a platform is captured by a value and a cost function, mapping bidding strategies to value and cost respectively. We assume a diminishing returns property, whereby the marginal cost is increasing in value. The advertiser uses an autobidder that selects a bidding strategy for each platform, aiming to maximize total value subject to budget and return-on-spend constraint. The advertiser has no prior information and learns about the value and cost functions by querying a platform with a specific bidding strategy. Our goal is to design algorithms that find the optimal bidding strategy with a small number of queries. We first present an algorithm that requires (O(m log (mn) log n)) queries, where m is the number of platforms and n is the number of possible bidding strategies in each platform. Moreover, we adopt the learning-augmented framework and propose an algorithm that utilizes a (possibly erroneous) prediction of the optimal bidding strategy. We provide a O(m log (mη) log η) query-complexity bound on our algorithm as a function of the prediction error η. This guarantee gracefully degrades to (O(m log (mn) log n)). This achieves a "best-of-both-worlds" scenario: (O(m)) queries when given a correct prediction, and (O(m log (mn) log n)) even for an arbitrary incorrect prediction.
Open Urban Innovation Ecosystems - Integration of Data, Services, and Stakeholders: Discussion Paper
Authors: Dirk Ahlers
Venue: WWW
Year: 2025

This paper argues for a broader understanding of the role of integration within urban system development ranging from technical to collaboration and governance challenges. It is intended to support an evolution from specific urban data platforms to broader cross-stakeholder integrative urban innovation ecosystems. This fulfils a need arising from the design and development of complex multi-stakeholder urban transitions. These are the next steps to move from smaller demonstration projects and open data pilots to large-scale deployment. They focus on long-term impacts and sustainability of urban and governance interventions. Topics are multi-stakeholder innovation, Enterprise Architecture, Open Innovation, Living Labs, multi-stakeholder innovation, building local partnerships, developing technical expertise, rolling out infrastructure, planning support, stakeholder and citizen engagement, partnerships, contractual needs, new business models, and the challenges associated with them within sustainable city development and evolution.
Temporal Aspects in Process Support for Urban Digital Twins
Authors: Dirk Ahlers
Venue: WWW
Year: 2025

In this position paper, we follow the concept of timescapes in smart cities to explore the use of temporal features and data. Our main case is the use of digital twins, complex urban systems, and processes around them, in particular towards spatio-temporal information access and their use towards urban planning and climate transitions.
M2-VLP: Enhancing Multilingual Vision-Language Pre-Training via Multi-Grained Alignment
Authors: Ahtamjan Ahmat, Lei Wang, Yating Yang, Bo Ma, Rui Dong, Kaiwen Lu, Rong Ma, Xinyue Wang
Venue: WWW
Year: 2025

Recently, multilingual Vision-Language Pre-training (mVLP) has shown remarkable progress in learning joint representations across different modalities and languages. However, most existing methods learn semantic alignment at a coarse-grained level and fail to capture fine-grained correlations between different languages and modalities. To address this, we propose a Multi-grained Multilingual Vision-Language Pre-training (M2-VLP) model, which aims to learn cross-lingual cross-modal alignment at different semantic granular levels. In cross-lingual interaction, the model learns the global alignment of parallel sentence pairs and the word-level correlations. In cross-modal interaction, the model aligns images with captions and image regions with corresponding words. To integrate the cross-lingual and cross-modal alignment above, we propose a unified multi-grained contrastive learning paradigm. Under zero-shot cross-lingual and fine-tuned multilingual settings, extensive experiments on vision-language downstream tasks across twenty languages demonstrate the effectiveness of M2-VLP over competitive contrastive models. Code and models are available at https://github.com/ahtamjan/M2-VLP.
Common Foundations for SHACL, ShEx, and PG-Schema
Authors: Shqiponja Ahmetaj, Iovka Boneva, Jan Hidders, Katja Hose, Maxime Jakubowski, José Emilio Labra Gayo, Wim Martens, Fabio Mogavero, Filip Murlak, Cem Okulmus, Axel Polleres, Ognjen Savkovic, Mantas Simkus, Dominik Tomaszuk
Venue: WWW
Year: 2025

Graphs have emerged as a foundation for a variety of applications, including capturing factual knowledge, semantic data integration, social networks, and informing machine learning algorithms. Formalising properties of the data and ensuring data quality requires describing schemas of such graphs. Driven by diverse applications, the Semantic Web and database communities developed not only different graph data models-RDF and property graphs-but also different graph schema languages-SHACL, ShEx, and PG-Schema. Each language has its unique approach to defining constraints and validating graph data, leaving potential users in the dark about their commonalities and differences. In this paper, we provide concise formal definitions of the core components of these languages, employ a uniform framework to facilitate a comprehensive comparison between them, and identify a common set of functionalities, shedding light on both overlapping and distinctive features.
Broken Access: On the Challenges of Screen Reader Assisted Two-Factor and Passwordless Authentication
Authors: Md Mojibur Rahman Redoy Akanda, Ahmed Tanvir Mahdad, Nitesh Saxena
Venue: WWW
Year: 2025

In today's technology-driven world, web services have opened up new opportunities for blind and visually impaired people to interact independently. Securing interactions with these services is crucial; however, currently deployed methods of web authentication mainly concentrate on sighted users, overlooking the specific needs of the blind and visually impaired community. In this paper, we address this critical gap by investigating the security and accessibility aspects of these web authentication methods when adopted by blind and visually impaired users. We model web authentication for such users as screen reader assisted authentication and introduce an evaluation framework called Authentication Workflows Accessibility Review and Evaluation (AWARE). Using AWARE, we then systematically assessed popular PC-based and smartphone-based screen readers against different types of deployed web authentication methods, including variants of 2FA and passwordless schemes, to simulate real-world scenarios for blind and visually impaired individuals. We analyzed these screen reader assisted authentication interactions with authentication methods in three settings: using a terminal (PC) with screen readers, a combination of the terminal (PC) and smartphone with screen readers, and smartphones with integrated screen readers. The results of our study underscore significant weaknesses in all of our observed screen reader assisted authentication scenarios for real-life authentication methods. These weaknesses, encompassing specific accessibility issues caused by imprecise screen reader instructions, highlight vulnerability concerning observed scenarios for both real-world and research literature based attacks, including phishing, concurrency, fatigue, cross-service, and shoulder surfing. Broadly, our AWARE framework can be used by authentication system designers as a precursor to user studies which are typically time-consuming and tedious to perform, independently allowing to unfold security and accessibility problems early which designers can address prior to full-fledged user testing of more isolated issues.
Few Labels with Active Learning: From Weak to Strong Labels for Misinformation Detection
Authors: Abdulrahman Abdullah Alalawi, Abdullah Alsuhaibani, Usman Naseem, Basem Suleiman, Shoaib Jameel, Imran Razzak
Venue: WWW
Year: 2025

Social media has evolved into a platform where individuals can freely disseminate content online. However, this unrestricted environment has unfortunately led to its misuse, with these platforms increasingly being used to circulate inappropriate content, misinformation, and disinformation. Misinformation detection becomes a crucial task in seeking social safety. In this work, we propose a framework called Few Labels with Active Learning (FLAL), which leverages a margin-sampling technique within the active learning paradigm. This enables the model to prioritize and learn from the most informative yet uncertain instances, enhancing its performance by focusing iteratively on examples that contribute the most to learning. As a result, the need for extensive labelling efforts is reduced. We evaluate FLAL across four multilingual benchmark datasets, where it demonstrates competitive results despite utilizing only a few labelled samples. To the best of our knowledge, this is the first study to apply active learning in conjunction with the few-label paradigm to data derived from an Arabic-language context.
Maelis4Skrid: An Approximate Query Engine for an Online Graph-Based Musical Score Library
Authors: Adel Aly, Olivier Pivert, Virginie Thion
Venue: WWW
Year: 2025

Digital Score Libraries are libraries dedicated to the storing, the management and the dissemination of musical scores. While document-oriented sheet musical scores have been the traditional way to preserve and share Western musical works, the emergence of modern digital formats opens the way to services that leverage the musical content itself, including that of retrieving music pieces based on their musical content. In this demonstration, we present the Skrid platform, an online Digital Score Library (DSL) that utilizes a graph-based storage for the scores' musical content. We also present Maelis, a flexible querying module implemented inside Skrid, that enables melodic pattern approximate search, ranks the results by relevance, and provides a detailed explanation of the answers. The demonstration is composed of two scenarios that showcase the key features of Maelis embedded in Skrid. The scenarios are accompanied by an online interaction inviting the audience to engage with the system.
Roles of Network and Identity in Hashtag Diffusion
Authors: Aparna Ananthasubramaniam, Yufei &apos, Louise&apos, Zhu, David Jurgens, Daniel M. Romero
Venue: WWW
Year: 2025

The diffusion of culture online is theorized to be influenced by many interacting social factors (e.g., network and identity). However, most existing computational cascade models consider just a single factor (e.g., network or identity). This work offers a new framework for teasing apart the mechanisms underlying hashtag cascades. We curate a new dataset of 1,337 hashtags representing cultural innovation online, develop a 10-factor evaluation framework for comparing empirical and simulated cascades, and show that a combined network+identity model better simulates hashtag cascades than network- or identity-only counterfactuals. We also explore heterogeneity in performance: While a combined network+identity model best predicts the popularity of cascades, a network-only model best predicts cascade growth and an identity-only model best predicts adopter composition. The network+identity model has the highest comparative advantage among hashtags used for expressing racial or regional identity and talking about sports or news. In fact, we are able to predict what combination of network and/or identity best models each hashtag and use this to further improve performance. Our results show the utility of models incorporating the interactions of network, identity, and other social factors in the diffusion of hashtags in social media.
Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions
Authors: Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro, Igor Bilogrevic
Venue: WWW
Year: 2025

Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research on the prevalence of fingerprinting heavily relied on automated web crawls, which inherently struggle to replicate the nuances of human-computer interactions. This raises concerns about the accuracy of current understandings of real-world fingerprinting deployments. As a result, this paper presents a user study involving 30 participants over 10 weeks, capturing telemetry data from real browsing sessions across 3,000 top-ranked websites. Our evaluation reveals that automated crawls miss almost half (45%) of the fingerprinting websites encountered by real users. This discrepancy mainly stems from the crawlers' inability to access authentication-protected pages, circumvent bot detection, and trigger fingerprinting scripts activated by specific user interactions. We also identify potential new fingerprinting vectors present in real user data but absent from automated crawls. Finally, we evaluate the effectiveness of federated learning for training browser fingerprinting detection models on real user data, yielding improved performance than models trained solely on automated crawl data.
11th WebAndTheCity: The Responsible Web and AI for Smart Cities
Authors: Leonidas G. Anthopoulos, Marijn Janssen, Vishanth Weerakkody
Venue: WWW
Year: 2025

This is the 11th edition of the workshop series labeled ''Web and the City - The Web and Smart Cities'', which started back in Florence in 2015 and kept on taking place every year in conjunction with the WWW conference series. Last year the workshop was held in Singapore. Each year the focus of the workshop is actualized, and this year the workshop focuses on the Responsible web and Responsible AI in cities and communities. In the era of digital twinning, AI, and the metaverse (so-called citiverse for cities), and under the UN 2030 Agenda for sustainable growth and resilience, cities are being transformed into virtual spaces that enable service automation and value generation to their communities and enterprises. Moreover, AI and web intelligence generate new types of opportunities, including generating automated transactions in these virtual spaces, which must preserve human-perceived consequences, fairness, accountability, transparency, and ethics. This workshop aims to focus on how the Web transforms cities into responsibly intelligent virtual environments.
Retrieval Augmented Multi-agent Recommender
Authors: Xiao Ao, Shuxi Han, Yeming Li, Heli Ma, Pengfei Zhang, Jie Zou
Venue: WWW
Year: 2025

Online review platforms are vital for shaping consumer choices and experiences in today's digital world. The AgentSociety Challenge focuses on advancing personalized recommendations with large language model (LLM) agents, aiming to enhance web interactions and user satisfaction. This paper presents the first-place solution for the AgentSociety Challenge Recommendation Track, by the team of the University of Electronic Science and Technology of China. We propose a recommendation framework based on large language models, integrating retrieval-augmented feature extraction and a data-driven multi-agent strategy. The core advantages of our approach lie in two key aspects. First, to optimize the representation of both products and users, we design a retrieval-augmented feature extraction mechanism that combines structured product attributes with unstructured user reviews, capturing both the objective characteristics of products and users' subjective preferences. Second, we introduce a data-driven multi-agent system that adaptively adjusts processing strategies based on the heterogeneous characteristics of different datasets, thereby effectively uncovering valuable latent information. Our method is built upon the Qwen-72B-Instruct model, with further optimizations in recommendation accuracy and system stability. Experimental results on three widely used datasets-Amazon, Goodreads, and Yelp-demonstrate significant improvements in recommendation performance. Ultimately, our solution achieved an overall hit rate score of 0.4744, representing a 101% improvement over the baseline, showcasing its outstanding effectiveness in personalized recommendation tasks.
Topo Goes Political: TDA-Based Controversy Detection in Imbalanced Reddit Political Data
Authors: Arvindh Arun, Karuna K. Chandra, Akshit Sinha, Balakumar Velayutham, Jashn Arora, Manish Jain, Ponnurangam Kumaraguru
Venue: WWW
Year: 2025

The detection of controversial content in political discussions on the Internet is a critical challenge in maintaining healthy digital discourse. Unlike much of the existing literature that relies on synthetically balanced data, our work preserves the natural distribution of controversial and non-controversial posts. This real-world imbalance highlights a core challenge that needs to be addressed for practical deployment. Our study re-evaluates well-established methods for detecting controversial content. We curate our own dataset focusing on the Indian political context that preserves the natural distribution of controversial content, with only 12.9% of the posts in our dataset being controversial. This disparity reflects the true imbalance in real-world political discussions and highlights a critical limitation in the existing evaluation methods. Benchmarking on datasets that model data imbalance is vital for ensuring real-world applicability. Thus, in this work, (i) we release our dataset, with an emphasis on class imbalance, that focuses on the Indian political context, (ii) we evaluate existing methods from this domain on this dataset and demonstrate their limitations in the imbalanced setting, (iii) we introduce an intuitive metric to measure a model's robustness to class imbalance, (iv) we also incorporate ideas from the domain of Topological Data Analysis, specifically Persistent Homology, to curate features that provide richer representations of the data. Furthermore, we benchmark models trained with topological features against established baselines.
Augmented Intelligence for Research Capability Mapping
Authors: Amir Aryani
Venue: WWW
Year: 2025

Identifying and mapping research expertise within an organisation or research ecosystem is challenging due to the complexity and fragmentation of available research information. Researchers have diverse expertise across fields such as biology and engineering, making uniform categorisation difficult. Additionally, overlapping skills across disciplines blur boundaries, further complicating classification. This talk explores the use of Generative AI in transforming disconnected research information into structured Research Capability Maps and Research Knowledge Graphs. Recent developments in GenAI have introduced new capabilities for topic classification, subject mapping, and knowledge mining from vast amounts of literature, datasets, and other research outputs. We examine the concept of Augmented Intelligence, which integrates domain expertise with AI-assisted processes to enhance informed dialogue among researchers. This approach significantly improves the identification of research capabilities within an organisation and the assessment of research capacity within its collaboration networks. We have implemented this approach as part of Research Link Australia project within the health sector, involving health research institutions, medical research institutes (MRIs), universities, and not-for-profit organisations. This talk will present findings from this case study, demonstrating how the Augmented Intelligence approach provided a platform for fostering a shared understanding of research capabilities and capacities within this network. This work builds upon the Research Graph framework, which connects research entities including research publications, research data collections, researcher profiles, organisations and funded projects. Leveraging this framework and use of GenAI, this case study shows how fragmented research information can be transformed to a connected graph and lead to insights into research capabilities within institutions and identifying potential research collaboration opportunities in Australia ad overseas. This is a demonstration of human and AI collaboration based on a human-in-the-loop intelligence approach.
Analyzing the Sensitivity of Prompt Engineering Techniques in Natural Language Interfaces for 2.5D Software Visualization
Authors: Daniel Atzberger, Adrian Jobst, Mariia Tytarenko, Willy Scheibel, Jürgen Döllner, Tobias Schreck
Venue: WWW
Year: 2025

Natural Language Interfaces (NLIs) backed by Large Language Models (LLMs) are used to interact with visualizations through natural language queries. Using the specific example of 2.5D treemaps, the Delphi tool was recently presented, introducing an interactive 2.5D visualization with an accompanying chat interface, where the LLM can react to user input and adapt the visualization at its own discretion. While Delphi has demonstrated effectiveness, the authors have not included an evaluation of the LLM's performance with respect to its prompt and specific task types. In this study, we systematically evaluate the impact of prompt engineering on Delphi's ability to answer factual questions related to data and visualization. Specifically, we investigate the effect of the Chain-of-Thought prompting technique by employing a questionnaire comprising 40 questions across ten low-level analytic tasks. Our findings aim to refine prompt design methodologies and enhance the usability and effectiveness of NLIs in advanced visualization systems.