OpenRaiser/Intern-Atlas huggingface dataset:
Community Notes for Preventing the Spread of Misinformation After the Noto Peninsula Earthquake in Japan
Authors: Masaki Chujyo, Dongwoo Lim, Mikihito Tanaka, Fujio Toriumi
Venue: WWW
Year: 2025

The spread of misinformation on social media poses a significant challenge during crises, where timely and accurate information is crucial for public safety. Community Notes, a feature of X (formerly Twitter), aims to mitigate misinformation by adding supplementary information to posts. Notes become visible only after receiving sufficient ratings from other users. This study examines the creation and evaluation process of Community Notes after the Noto Peninsula earthquake on January 1, 2024. Among the 1,722 notes created, only 11 percent were rated as ''Helpful'' and displayed, while the majority, 88 percent, remained unevaluated due to insufficient user ratings. Notably, helpful notes were displayed promptly, with a median evaluation time of six hours, highlighting their potential for rapid response. A content analysis identified two predominant topics: political and conspiracy theories, with conspiracy-related notes slightly more likely to be rated as helpful. These findings demonstrate that Community Notes can effectively combat misinformation during large-scale disasters but also highlight the need to enhance user participation and improve the evaluation process to maximize their impact.
Exposing Cross-Platform Coordinated Inauthentic Activity in the Run-Up to the 2024 U.S. Election
Authors: Federico Cinus, Marco Minici, Luca Luceri, Emilio Ferrara
Venue: WWW
Year: 2025

Coordinated information operations remain a persistent challenge on social media, despite platform efforts to curb them. While previous research has primarily focused on identifying these operations within individual platforms, this study shows that coordination frequently transcends platform boundaries. Leveraging newly collected data of online conversations related to the 2024 U.S. Election across 𝕏 (formerly, Twitter), Facebook, and Telegram, we construct similarity networks to detect coordinated communities exhibiting suspicious sharing behaviors within and across platforms. Proposing an advanced coordination detection model, we reveal evidence of potential foreign interference, with Russian-affiliated media being systematically promoted across Telegram and 𝕏. Our analysis also uncovers substantial intra- and cross-platform coordinated inauthentic activity, driving the spread of highly partisan, low-credibility, and conspiratorial content. These findings highlight the urgent need for regulatory measures that extend beyond individual platforms to effectively address the growing challenge of cross-platform coordinated influence campaigns.
The Impact of Persona-based Political Perspectives on Hateful Content Detection
Authors: Stefano Civelli, Pietro Bernardelle, Gianluca Demartini
Venue: WWW
Year: 2025

While pretraining language models with politically diverse content has been shown to improve downstream task fairness, such approaches require significant computational resources often inaccessible to many researchers and organizations. Recent work has established that persona-based prompting can introduce political diversity in model outputs without additional training. However, it remains unclear whether such prompting strategies can achieve results comparable to political pretraining for downstream tasks. We investigate this question using persona-based prompting strategies in multimodal hate-speech detection tasks, specifically focusing on hate speech in memes. Our analysis reveals that when mapping personas onto a political compass and measuring persona agreement, inherent political positioning has surprisingly little correlation with classification decisions. Notably, this lack of correlation persists even when personas are explicitly injected with stronger ideological descriptors. Our findings suggest that while LLMs can exhibit political biases in their responses to direct political questions, these biases may have less impact on practical classification tasks than previously assumed. This raises important questions about the necessity of computationally expensive political pretraining for achieving fair performance in downstream tasks.
Linear-Time Algorithms for Representative Subset Selection From Data Streams
Authors: Shuang Cui, Kai Han, Jing Tang
Venue: WWW
Year: 2025

Representative subset selection from data streams is a critical problem with wide-ranging applications in web data mining and machine learning, such as social media marketing, big data summarization, and recommendation systems. This problem is often framed as maximizing a monotone submodular function subject to a knapsack constraint, where each data element in the stream has an associated cost, and the goal is to select elements within a budget B to maximize revenue. However, existing algorithms typically rely on restrictive assumptions about the costs of data elements, and their performance bounds heavily depend on the budget B. As a result, these algorithms are only effective in limited scenarios and have super-linear time complexity, making them unsuitable for large-scale data streams. In this paper, we introduce the first linear-time streaming algorithms for this problem, without any assumptions on the data stream, while also minimizing memory usage. Specifically, our single-pass streaming algorithm achieves an approximation ratio of 1/8-ε under O (n) time complexity and O(k log 1/ε) space complexity, where k is the largest cardinality of any feasible solution. Our multi-pass streaming algorithm improves this to a (1/2-ε)-approximation using only three passes over the stream, with O (n/ε log 1/ε) time complexity and O(k/ε log 1/ε) space complexity. Extensive experiments across various applications related to web data mining and social media marketing demonstrate the superiority of our algorithms in terms of both effectiveness and efficiency.
MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising
Authors: Quanyu Dai, Jiaren Xiao, Zhaocheng Du, Jieming Zhu, Chengxiao Luo, Xiao-Ming Wu, Zhenhua Dong
Venue: WWW
Year: 2025

In online advertising, uncertainty calibration aims to adjust a ranking model's probability predictions to better approximate the true likelihood of an event, e.g., a click or a conversion. However, existing calibration approaches may lack the ability to effectively model complex nonlinear relations, consider context features, and achieve balanced performance across different data subsets. To tackle these challenges, we introduce a novel model called Monotonic Calibration Networks, featuring three key designs: a monotonic calibration function (MCF), an order-preserving regularizer, and a field-balance regularizer. The nonlinear MCF is capable of naturally modeling and universally approximating the intricate relations between uncalibrated predictions and the posterior probabilities, thus being much more expressive than existing methods. MCF can also integrate context features using a flexible model architecture, thereby achieving context awareness. The order-preserving and field-balance regularizers promote the monotonic relationship between adjacent bins and the balanced calibration performance on data subsets, respectively. Experimental results on both public and industrial datasets demonstrate the superior performance of our method in generating well-calibrated probability predictions.
Bias in Humans and AI - What To Do About It?
Authors: Gianluca Demartini
Venue: WWW
Year: 2025

The rise in popularity of general-purpose large language models (LLMs) raises questions around bias and fairness in the decision they make. Do these models reflect the biases and stereotypes present in the data they have been pre-trained on? If so, how should we deal with it? In this talk, we first discuss issues of bias in human data using as an example gender bias in Wikipedia where we looked at how well represented genders are across different categories of articles. We then move on to look at issues of bias in Artificial Intelligence (AI) using as an example political bias in LLMs. We show how it is possible to measure the political standing of different LLMs and to control their standing by telling them to impersonate certain profiles. This also shows what are some of the existing stereotypes (e.g., a museum curator is left-wing and a retired army officer is right-wing) embedded into the LLMs during pre-training. Finally, we discuss how to explore and manage bias existing in LLMs, how these models perform when used for sensitive tasks, and how users tend to trust AI agents for low-risk and high-risks tasks.
Developing a Comprehensive Task Framework for Effective Workforce Analysis
Authors: Rebecca Dew, Mingzhao Li, Weidong Liu, Sandya Baratha Raj
Venue: WWW
Year: 2025

Effective workforce analysis, planning, and management require a deep understanding of the tasks and skills associated with different roles. This paper introduces a novel methodology for developing a comprehensive task framework that leverages Large Language Models (LLMs) and large-scale job ads data. We first propose an innovative approach to task taxonomy design, which involves the decomposition and reconstruction of tasks into a hierarchical structure based on action-object pairings, systematically refined using LLMs. The methodology extends to integrating the taxonomy with occupation and skill linkages derived from job ads, ensuring alignment with real-world workforce dynamics. Finally, we demonstrate the practical value of this framework through a visual analytics system that enables interactive exploration and analysis of tasks, occupations, and associated skills, highlighting its potential to transform workforce analysis. Demo video: https://bit.ly/41txBZK
Uncertainty-Aware Fusion: An Ensemble Framework for Mitigating Hallucinations in Large Language Models
Authors: Prasenjit Dey, Srujana Merugu, Sivaramakrishnan R. Kaveri
Venue: WWW
Year: 2025

Large Language Models (LLMs) are known to hallucinate and generate non-factual outputs which can undermine user trust. Traditional methods to directly mitigate hallucinations, such as representation editing and contrastive decoding, often require additional training data and involve high implementation complexity. While ensemble-based approaches harness multiple LLMs to tap into the ''wisdom of crowds'', these methods overlook uncertainties in individual model responses. Recent studies reveal that uncertainty estimation can enable LLMs to self-assess the likelihood of generating hallucinations. In this work, we focus on factoid question answering (QA) and observe that LLMs accuracy and self-assessment capabilities vary widely with different models excelling in different scenarios. Leveraging this insight, we propose Uncertainty-Aware Fusion (UAF), an ensemble framework to reduces hallucinations by strategically combining multiple LLM based on their accuracy and self-assessment abilities. Empirical results on several public benchmark datasets show that UAF outperforms state-of-the-art hallucination mitigation methods by 8% in factual accuracy, while either narrowing or surpassing the performance gap with GPT-4.
Middleman Bias in Advertising: Aligning Relevance of Keyphrase Recommendations with Search
Authors: Soumik Dey, Wei Zhang, Hansi Wu, Bingfeng Dong, Binbin Li
Venue: WWW
Year: 2025

E-commerce sellers are recommended keyphrases based on their inventory on which they advertise to increase buyer engagement (clicks/sales). Keyphrases must be pertinent to items; otherwise, it can result in seller dissatisfaction and poor targeting - towards that end relevance filters are employed. In this work, we describe the shortcomings of training relevance filter models on biased click/sales signals. We re-conceptualize advertiser keyphrase relevance as interaction between two dynamical systems - Advertising which produces the keyphrases and Search which acts as a middleman to reach buyers. We discuss the bias of search relevance systems (middleman bias) and the need to align advertiser keyphrases with search relevance signals. We also compare the performance of cross encoders and bi-encoders in modeling this alignment and the scalability of such a solution for sellers at eBay.
Parallel Online Similarity Join over Trajectory Streams
Authors: Zhongjun Ding, Ke Li, Lisi Chen, Shuo Shang
Venue: WWW
Year: 2025

Trajectory Similarity Join (TS-Join), as a fundamental operation in trajectory data analytics, has been extensively investigated by existing studies in data science. However, existing solutions are almost designed for offline static trajectories, which cannot ensure real-time feedback. In addition, the join results retrieved from existing solutions generally contain a large proportion of outdated similar pairs, making them inapplicable to evolving trajectories. In this light, we study a novel problem of online time-aware trajectory similarity join: Given a stream of evolving trajectories, we aim to dynamically discover trajectory pairs whose spatio-temporal similarity is no less than a specified threshold in a real-time manner. We innovatively introduce a time-aware exponential-decaying similarity function to eliminate outdated results. To support real-time querying over large populations of trajectories, we develop a Parallel Online Trajectory Similarity Join (POTSJ) framework with workload balancing techniques. We further enhance join efficiency through pruning strategies and tailored approximation techniques. The POTSJ framework we propose, which incorporates these elements, is capable of processing online TS-Join while simultaneously satisfying three key objectives: real-time result updates, comprehensive trajectory evaluation, and scalability. Extensive experiments on real-world datasets validate the efficiency and scalability of our POTSJ framework in processing online TS-Join.
Artificial Intelligence for Complex Network: Potential, Methodology and Application
Authors: Jingtao Ding, Yu Zheng, Huandong Wang, Carlo Vittorio Cannistraci, Jianxi Gao, Yong Li, Chuan Shi
Venue: WWW
Year: 2025

This tutorial will explore the fascinating domain of empirical network modeling through artificial intelligence (AI) techniques, with applications across social media, web systems, and urban environments. Participants will gain valuable insights into incorporating advanced AI methods-such as graph machine learning, deep reinforcement learning, and generative models-within complex network science. The goal is to provide a comprehensive understanding of how these models can effectively represent, predict, and control empirical networked systems with heterogeneous structures and dynamic processes. The tutorial will begin by introducing essential background knowledge, outlining motivations and challenges, exploring recent methodological advances, and highlighting key applications.
SPEAR: A Structure-Preserving Manipulation Method for Graph Backdoor Attacks
Authors: Yuanhao Ding, Yang Liu, Yugang Ji, Weigao Wen, Qing He, Xiang Ao
Venue: WWW
Year: 2025

Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing graph backdoor attacks are susceptible to defense mechanisms or robust classifiers because they rely on subgraph injection or structural perturbations, e.g., creating additional edges to attach backdoor triggers to the original graph. To enhance the stealthiness of graph backdoors, we propose SPEAR, a novel structure-preserving graph backdoor attack that avoids modifying the graph's topology. SPEAR operates within a limited attack budget by selectively perturbing node attributes while ensuring the triggers exert significant influence through a global importance-driven feature selection strategy. Additionally, a neighborhood-aware trigger generator is employed to underpin a high attack success rate by utilizing semantic information from the neighborhood. SPEAR amplifies effectiveness and stealthiness by combining subtle yet impactful attribute manipulation with a refined trigger generation mechanism. Extensive experiments demonstrate that SPEAR achieves state-of-the-art effectiveness in bypassing defenses on real-world datasets, establishing it as a potent and stealthy backdoor attack for graph-based tasks. Code is available at https://github.com/yhDing/SPEAR.
Distributed Secretary Problem: A Case of Two Uneven Queues
Authors: Ke Ding, Bo Li, Fangxiao Wang
Venue: WWW
Year: 2025

Secretary problem is one of the most widely studied online stochastic models, in which an employer wants to hire the best candidate from n candidates who arrive in a random order. It is well-known that the optimal success probability is 1/e. However, in reality, things are more complex because employers often have interviewers in different cities, interviewing candidates in a distributed manner. This motivates us to study the secretary problem with multiple queues. Feldman and Tennenholtz [EC 2012] studied this assuming the candidates are distributed evenly. In particular, when there are two even queues, the optimal success probability is 1/4. In this work, we move to the general problem when the queues are arbitrary and design the optimal online algorithm for the case of two queues. Our results characterize the exact success probability curve, connecting the cases of a single queue and two equal queues. Our technique is grounded on the linear programming framework introduced by Buchbinder et al. [Math. Oper. Res. 2014] and a novel analysis.
eBaaS: AIoT-Enabled eBike Battery-Swap as a Service for Last-Mile Delivery
Authors: Donghui Ding, Zhao Li, Jiarun Zhang, Xuanwu Liu, Ji Zhang, Yuchen Li, Peng Cai, JianXun Liu, Guodong Long
Venue: WWW
Year: 2025

In China, the number of riders in the on-demand delivery industry has surpassed ten million. Ensuring that these riders earn a decent income can enhance their financial security, reduce poverty, and promote social equity and stability. Due to ease of use, lower-cost maintenance and environmental friendliness, electric bicycles (e-bikes) are the primary mode of transportation for delivery riders. However, these riders frequently encounter depleted batteries due to limited capacity and prolonged charging times, necessitating inconvenient swaps or recharges during deliveries. To address this issue, we propose the e-bike Battery Swap-as-a-Service (eBaaS), an innovative battery-swapping system that leverages an intelligent AIoT network for seamless battery swapping at distributed locations across urban areas. eBaaS integrates edge-cloud collaboration, battery resource allocation, battery anomaly detection, and battery range prediction to minimize downtime and reduce unnecessary mileage. While eBaaS's potential benefits are evident, there has been a lack of robust methods to quantify its impact. Thus, we further developed the eBaaS Impact Evaluation Method (EIEM), the first comprehensive model to address this gap. EIEM analyzes data from approximately 260,000 delivery riders and 5 million riding trajectories. Findings indicate that eBaaS reduces average invalid mileage by 6 km and increases the order volume by an average of over 20% daily per e-bike rider. Meanwhile, the annual electricity savings result in a reduction of 2.74 million kilograms of carbon emissions for 260,000 riders. The eBaaS system is therefore significantly beneficial for environmental conservation and sustainable urban development.
GPT4Image: Large Pre-trained Models Help Vision Models Learn Better on Perception Task
Authors: Ning Ding, Yehui Tang, Zhongqian Fu, Chao Xu, Kai Han, Yunhe Wang
Venue: WWW
Year: 2025

The upsurge in pre-trained large models started by ChatGPT has swept across the entire deep learning community. Such powerful models demonstrate advanced generative ability and multimodal understanding capability, which quickly set new state of the arts on a variety of benchmarks. The pre-trained LLM usually plays the role as a universal AI model that can conduct various tasks like article analysis and image comprehension. However, due to the prohibitively high memory and computational cost of implementing such a large model, the conventional models (such as CNN and ViT) are still essential for many visual perception tasks. In this paper, we propose to enhance the representation ability of ordinary vision models on perception tasks (e.g. image classification) by taking advantage of the off-the-shelf large pre-trained models. We present a new learning framework, dubbed GPT4Image, where the knowledge of the large pre-trained models are extracted to help CNNs and ViTs learn better representations and achieve higher performance. Firstly, we curate a high quality description set by prompting a multimodal LLM to generate descriptions for training images. Then, these detailed descriptions are fed into a pre-trained encoder to extract text embeddings that encodes the rich semantics of images. During training, text embeddings will serve as extra supervising signal and be aligned with image representations learned by vision models. The alignment process helps vision models achieve better performance with the aid of pre-trained LLMs. We conduct extensive experiments to verify the effectiveness of the proposed algorithm on various visual perception tasks for heterogeneous model architectures.
The Journey of Action Recognition
Authors: Xi Ding, Lei Wang
Venue: WWW
Year: 2025

Action recognition has evolved from a niche research area into a fundamental aspect of video understanding, driven by the dynamic interplay between data, model architectures, and learning paradigms. Early studies, constrained by limited datasets and handcrafted features, laid the groundwork for the field, but the rapid growth of data and advancements in deep learning techniques ignited a revolution. From 2D- and 3D-CNNs to spatiotemporal graph convolutional networks, these models have advanced the ability to capture complex, multidimensional actions across increasingly diverse and multimodal datasets. Simultaneously, innovative learning paradigms such as self-supervised, few-shot, and zero-shot learning have transformed how we use data, enabling models to generalize across tasks with minimal labeled data. The advent of transformer-based architectures has catalyzed a new era in action recognition, excelling in capturing long-range temporal dependencies and overcoming previous limitations in spatiotemporal modeling. Furthermore, the rise of video masked autoencoders has introduced new ways to balance spatial and temporal information, leading to breakthroughs in understanding motion dynamics. This paper presents a comprehensive exploration of action recognition through three critical lenses: the evolution of model architectures, the expanding diversity of data, and the emergence of innovative learning techniques. By tracing the trajectory of these developments, we highlight how the convergence of these elements has broadened the scope of action recognition to tackle more complex video processing challenges, including anomaly detection, captioning, and video question answering. In particular, we underscore the transformative role of large language models in infusing semantic context, significantly enhancing the performance and versatility of action recognition systems. Our work not only reflects on the past but also provides a roadmap for future advancements. We reveal how action recognition has transcended its original focus, positioning itself at the heart of general video analysis. By synthesizing these insights, we offer a forward-thinking perspective on how the integration of multimodal, temporal, and semantic information will shape the future of AI-powered video understanding. Our paper's GitHub repository can be found at https://github.com/Darcyddx/Video-Action-Recognition.