Skin-toned emojis are crucial for fostering personal identity and social inclusion in online communication. As AI models, particularly Large Language Models (LLMs), increasingly mediate interactions on web platforms, the risk that these systems perpetuate societal biases through their representation of such symbols is a significant concern. This paper presents the first large-scale comparative study of bias in skin-toned emoji representations across two distinct model classes. We systematically evaluate dedicated emoji embedding models (emoji2vec, emoji-sw2v) against four modern LLMs (Llama, Gemma, Qwen, and Mistral). Our analysis first reveals a critical performance gap: while LLMs demonstrate robust support for skin tone modifiers, widely-used specialized emoji models exhibit severe deficiencies. More importantly, a multi-faceted investigation into semantic consistency, representational similarity, sentiment polarity, and core biases uncovers systemic disparities. We find evidence of skewed sentiment and inconsistent meanings associated with emojis across different skin tones, highlighting latent biases within these foundational models. Our findings underscore the urgent need for developers and platforms to audit and mitigate these representational harms, ensuring that AI's role on the web promotes genuine equity rather than reinforcing societal biases.
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A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While participants are typically invited to propose questions for expert panels, only a limited number can be selected due to time constraints. This raises the challenge of how to choose a small set of questions that best represent the interests of all participants. We introduce an auditing framework for measuring the level of representation provided by a slate of questions, based on the social choice concept known as justified representation (JR). We present the first algorithms for auditing JR in the general utility setting, with our most efficient algorithm achieving a runtime of O(mnłog n), where n is the number of participants and m is the number of proposed questions. We apply our auditing methods to historical deliberations, comparing the representativeness of (a) the actual questions posed to the expert panel (chosen by a moderator), (b) participants' questions chosen via integer linear programming, (c) summary questions generated by large language models (LLMs). Our results highlight both the promise and current limitations of LLMs in supporting deliberative processes. By integrating our methods into an online deliberation platform that has been used for over hundreds of deliberations across more than 50 countries, we make it easy for practitioners to audit and improve representation in future deliberations.
Since the first online ad in 1994, advertising has grown into a vast ecosystem delivering billions of ads daily. Advertisements are everywhere on the Web: search engines promote results, most websites display ads, some require users to accept ads as a condition for access, video streaming services fund their infrastructure through an increasing volume of ads, and much of the gaming industry has adopted ad-based revenue models. In exchange for free access to a wide range of content, web users sacrifice their privacy and pay with personal data to enable targeted marketing, a trade-off widely studied in literature. We argue that the ad ecosystem imposes an additional, overlooked, cost on web users: energy consumption. In this paper, we present a large-scale analysis of the client-side energy consumption of ads, including their associated tracking mechanisms. We design a robust methodology aimed at realistically modeling user behavior and monitoring CPU activity. Through measurements of 724,994 website visits across diverse devices, we study the energy implications of consenting to tracking and of blocking ads through browser and network-based software. We find that consent via cookie banners increases energy consumption by a median 2.57% across all websites and devices. We also show that websites relying on real-time bidding increase median energy consumption by 33.98%. Finally, although ad-blocking solutions consume energy due to their filtering processes, we observe a median reduction of 9.62% in client-side energy consumption when using uBlock Origin.
Doxing refers to the disclosure of personal information without consent, has evolved from sporadic acts of online vigilantism into a structured and commodified practice. In Chinese cyberspace, this shift has produced Doxing-as-a-Service (DaaS), a commercial model in which personal data is retrieved, organized, and traded as on-demand products. This industrialization of privacy violation lowers the barriers to doxing and amplifies its social harms, posing new challenges for building a responsible and safe web. Yet little is known about how DaaS operates or sustains itself, motivating our systematic, data-driven examination of its ecosystem and practices. This paper provides the first systematic study of the Chinese DaaS ecosystem. Analyzing 25,972 messages and 13.22 million subscriber links from 100 major channels on Telegram, we demystify its organization, operations, and user engagement. We find that the DaaS ecosystem operates through a three-tier supply chain linking data providers, service operators, and end users. Operators sustain illicit businesses through six major service categories, persistent advertising, and crypto-based payments, while users interact via specialized group spaces that enable real-time matching, large-scale identity exposure, and community-driven fraud mitigation. Our findings reveal a mature, resilient underground data market operating within mainstream messaging platforms, highlighting new challenges for online privacy and exposing critical vulnerabilities in platform governance and content moderation. This study provides empirical evidence for developing effective regulatory frameworks and accountability mechanisms to mitigate commodified online harms on encrypted messaging services.
Space-based Parameter Evolving with Lightweight Optimization for Graph Adaptation to Evolving Shifts
Adapting graph neural networks to evolving domain shifts presents a fundamental challenge: how to acquire new knowledge while preventing catastrophic forgetting. Existing continual learning methods often rely on memory replay or complex regularization schemes, incurring significant computational overhead. We propose STEM (State-based Parameter Evolving with Lightweight Optimization), a replay-free framework that transforms continual adaptation into controlled parameter space evolution via a controller-worker architecture. At its core is a Test-time Evolving State Space (TESS) controller with a selective gating mechanism that recursively updates its hidden state by integrating compact summaries of the current graph domain. Unlike traditional linear state space models, TESS enables nonlinear, input-dependent state transitions that capture temporal dynamics of domain evolution. A lightweight parameter generator decodes this evolving state into domain-specific adapter parameters injected into a frozen base GNN. We employ unsupervised Information Maximization and parameter space stability regularization that penalizes adapter changes across time steps, with theoretical guarantees of forgetting mitigation and stable convergent adaptation. Extensive experiments validate that our method achieves state-of-the-art performance while maintaining minimal computational overhead. The code is available at https://github.com/miaomiao1220/stem
Graph-level anomaly detection (GLAD) is a critical task to identify graphs with abnormal properties in various domains, ranging from fraudulent social networks to malicious botnets on online platforms. The dominant paradigm for existing GLAD detectors has been partially unsupervised, relying on training data composed exclusively of normal samples. However, this partially unsupervised paradigm inevitably requires a costly expert filtering process to ensure the training data is free of anomalies. This creates a significant gap between current approaches and the real-world necessity of a fully unsupervised paradigm, which involves training a model directly on real-world data ''as-is'', with its inherent mix of normal and anomalous samples. To bridge this gap, we incorporate uncertainty learning into GLAD to promote fully unsupervised learning. We propose two frameworks: Score Uncertainty Learning (SUL) and Graph-data Uncertainty Learning (GUL). Specifically, SUL enhances existing GLAD detectors by modeling uncertainty through Gaussian distributions over the detectors' predictions, adaptively attenuating the influence of potential anomalies. GUL is an end-to-end framework that iteratively optimizes anomaly detection and uncertainty modeling via an Expectation-Maximization algorithm. In addition, we develop a dedicated loss that utilizes potential anomalies to enhance the effectiveness and robustness of GUL. Empirical results on sixteen benchmark datasets, covering real-world graphs from social networks and online platforms, demonstrate the superiority of our methods and highlight the promise of incorporating uncertainty into fully unsupervised GLAD.
We consider a novel vertex cover problem on temporal graphs, where the edges in the graph may change over time, and a vertex selected into the solution has a lifespan d. Specifically, a vertex selected at time t can cover all incident edges in graphs from time slot t to t+d-1. This model effectively captures the scenario of monitoring communication links via secure nodes (monitors) with limited lifespan in a dynamic network. We provide a systematic study of this problem from both theoretical and practical perspectives. We analyze its computational complexity, develop approximation and online algorithms with tight ratios, and present a parameterized algorithm and a tight quadratic kernel under fixed d. Experimental results on random and real-world temporal networks demonstrate the effectiveness of our algorithms. We believe that our systematic study not only reveals the nature of the problem itself, but also paves the way for investigating the ''sustained'' version of other problems on temporal graphs.
Reinforcement learning-based recommendation systems (RLRS) are increasingly favored for their ability to leverage online interactive feedback, enabling adaptive and personalized decision-making. In this setting, user feedback serves as both a behavioral signal and an optimization target, making it essential for policy learning. However, collecting such feedback, e.g., clicks, ratings, and engagement traces, raises serious privacy concerns, posing critical challenges for value estimation, online adaptation, and privacy protection. In this paper, we propose FeedGuard, a critic-guided reinforcement learning framework with privacy-preserving feedback. FeedGuard enhances trajectory modeling via critic guidance, enables joint online fine-tuning with effective exploration–exploitation tradeoffs, and enforces end-to-end privacy protection across the feedback lifecycle via split federated learning and differential privacy. We further provide a formal analysis of its differential privacy guarantees. Extensive experiments on four public recommendation datasets and the VirtualTB platform show that FeedGuard performs well in both offline and online settings, while maintaining rigorous privacy guarantees with minimal degradation.
Accurate traffic flow forecasting is crucial for intelligent transportation systems and relies on effectively modeling complex spatio-temporal dependencies. Although recent graph-based deep learning methods have achieved promising results, most focus on pairwise neighbor relationships, limiting their ability to capture higher-order spatio-temporal interactions in the traffic network. To overcome this limitation, we propose a novel Multi-source information driven Spatio-Temporal HyperGraph learning for traffic forecasting (MSTHG), which is designed to capture richer relational and semantic information. MSTHG introduces a multi-source hypergraph fusion strategy that jointly models dynamic high-order spatial and temporal correlations. Specifically, we build a spatial hypergraph based on geographical proximity to represent high-order spatial dependencies, and a temporal-trend hypergraph leveraging mutual information to capture nonlinear similarities among traffic series. To enhance the semantic richness of node representations, we integrate key daily and weekly information along with periodic features derived from Fast Fourier Transform (FFT). Following the obtained hypergraph, node representations are learned through a hypergraph convolutional network and subsequently processed by a GRU-MLP fusion module, which is designed to capture both local and global temporal dependencies. Extensive experiments on real-world benchmark datasets demonstrate that MSTHG outperforms state-of-the-art baselines. The source code is https://github.com/April-leng/MSTHG.git.
Online market platforms play an increasingly powerful role in the economy. An empirical phenomenon is that platforms, such as Amazon, Apple, and DoorDash, also enter their own marketplaces, imitating successful products developed by third-party sellers. We formulate a Stackelberg model, where the platform acts as the leader by committing to an entry policy: when will it enter and compete on a product? We study this model through a theoretical and computational framework. We begin with a single seller, and consider different kinds of policies for entry. We characterize the seller's optimal explore-exploit strategy via a Gittins-index policy, and give an algorithm to compute the platform's optimal entry policy. We then consider multiple sellers, to account for competition and information spillover. Here, the Gittins-index characterization fails, and we employ deep reinforcement learning to examine seller equilibrium behavior. Our findings highlight the incentives that drive platform entry and seller innovation, consistent with empirical evidence from markets such as Amazon and Google Play, with implications for regulatory efforts to preserve innovation and market diversity.
We initiate the study of single-sample bilateral trade with a broker, drawing an analogy to the setting of single-sample bilateral trade without a broker considered in [1] and [5]. Our model captures the three-sided interaction in which a broker mediates trade between a buyer and seller, each described by a valuation distribution from which a single sample can be drawn. We consider two settings in particular: one where the valuation distributions of the buyer and seller are identical and one where the valuation distributions are stochastically ordered. We analyze simple mechanisms that rely only on a single sample from each agent's distribution and show that these mechanisms achieve constant-factor approximations to the first-best gains-from-trade (GFT), first-best social welfare (SW), and optimal profit under the standard monotone-hazard-rate assumption. We then complement these results with matching or nearly matching upper bounds on the GFT and SW of our mechanisms. Notably, in both settings, we observe fairly small losses in the approximation factors to the first-best GFT and first-best SW due to the existence of the broker (benchmarked against the corresponding approximation factors in the setting without a broker). Furthermore, our results stand in stark contrast to those of [12], who show inapproximability results under a strategic broker with full distributional knowledge. Our results provide insight into the design of data-efficient brokerage mechanisms for online marketplaces and decentralized trading platforms, where intermediaries must facilitate trade under severe informational constraints. They highlight how even minimal data can enable robust and incentive-compatible brokerage in uncertain markets for both the broker and the market participants.
Data powers almost everything we experience on the web today---from the recommendations and ads we see to the AI systems and online marketplaces that shape our digital interactions. The increasing demand for high-quality data has given rise to platforms that facilitate the buying and selling of data. A key practical challenge in such markets is determining how to price data. Competitive equilibrium (CE), a foundational concept in classical market economics, determines prices for rivalrous goods by matching their supply and demand. In this work, we initiate the study of CE in data markets, explicitly incorporating the role of data in improving predictive performance in buyers' utility functions, and the non-rival nature of data by adapting the standard market-clearing condition to allow the simultaneous allocation of data records to multiple buyers. We analyze the existence, structure, and computation of CE in such data markets. We establish that CE always exists, and almost all instances admit a unique and rational equilibrium price vector. In general, however, there could be a non-convex set of prices, which rules out convex-programming approaches for finding a CE. Despite these challenges, we design an FPTAS for computing approximate equilibria using a Walrasian-style price adjustment algorithm. Our framework opens avenues for studying richer buyer utilities under correlated data sellers, and deeper structural and algorithmic aspects of data markets.
In the secretary problem, a set of secretary candidates arrive in a uniformly random order and reveal their values one by one. A company, who can only hire one candidate and hopes to maximize the expected value of its hire, needs to make irrevocable online decisions about whether to hire the current candidate. The classical framework of evaluating a policy is to compute its worst-case competitive ratio against the optimal solution in hindsight, and there the best policy -- the ''1/e law'' -- has a competitive ratio of 1/e. We propose an alternative evaluation framework through the lens of regret -- the worst-case additive difference between the optimal hindsight solution and the expected performance of the policy, assuming that each value is normalized between 0 and 1. The 1/e law for the classical framework has a regret of 1 - 1/e ≈ 0.632; by contrast, we show that the class of ''pricing curves'' algorithms can guarantee a regret of at most 1/4 = 0.25 (which is tight within the class), and the class of ''best-only pricing curves'' algorithms can guarantee a regret of at most 0.190 (with a lower bound of 0.171). In addition, we show that in general, no policy can give a regret guarantee better than 0.152. Finally, we discuss other objectives in our regret-minimization framework.
98% of sales on Amazon.com flow through the Buy Box. However, Amazon sometimes decides not to feature any offers for a product and removes the Buy Box from the product page -- a situation known as Buy Box suppression. Suppression may have severe consequences on individual sellers and might affect competition if it disciplines prices across online marketplaces. This paper studies suppression using a new, high-frequency dataset that tracks 17,754 products on Amazon.com at hourly intervals for eight weeks in 2024, along with corresponding offers from 59,301 unique competitors. We open-source both the dataset and collection code. We find that the primary reason for suppression is changes to the offers for a product on Amazon. Lower competitor prices only modestly increase the probability of suppression. Suppression has substantial impacts on sellers: products immediately fall sharply in search, ad placements completely disappear within 12 hours, and sales ranks are 12% worse after 48 hours. In contrast, we find no evidence of impacts on competitor prices in the short run. Our results highlight how Buy Box suppression acts as an important and underexplored mechanism of platform governance.
Online advertising platforms must decide how to allocate multiple ads across limited screen real estate, where each ad's effectiveness depends not only on its own placement but also on nearby ads competing for user attention. Such spatial externalities — arising from proximity, clutter, or crowding — can significantly alter welfare and revenue outcomes, yet existing auction and allocation models typically treat ad slots as independent or ordered along a single dimension. We introduce a new framework for spatial externalities in online advertising, in which the value of an ad depends on both its slot and the configuration of surrounding ads. We model ad slots as points in a metric space, and model an advertiser's value as a function of both their bid and a discount factor determined by the configuration of other displayed ads. Within this framework, we analyze two natural models. For the Nearest-Neighbor model, where the value suppression depends only on the closest neighboring ad, we present a polynomial-time algorithm that achieves a constant approximation for the general case. We show that the allocation rule is monotone and can be implemented as a truthful mechanism. For a structured setting of 2D Euclidean space, we provide a PTAS. In contrast, for the Product-Distance model, where interference is aggregated multiplicatively across all neighbors, we establish a strong (and nearly-tight) hardness of approximation -- no polynomial-time algorithm can achieve any polynomial-factor approximation unless P=NP, via a reduction from Max-Independent-Set. Our results provide a foundation for reasoning about spatial externalities in ad allocation and for designing efficient, truthful mechanisms under such interactions.
We study two online resource allocation problems with reusability in an adversarial setting, namely \problemkRentalFD and \problemkRentalVD. In both problems, a decision-maker manages k identical reusable units and faces a sequence of rental requests over time. We develop theoretically grounded relax-and-round algorithms with provable competitive ratio guarantees for both settings. For \problemkRentalFD, we present an optimal randomized algorithm that achieves the best possible competitive ratio. The algorithm first computes an optimal fractional allocation using a price-based approach, and then applies a novel lossless online rounding scheme to obtain an integral solution. For \problemkRentalVD, we first establish the impossibility of achieving lossless online rounding. We then introduce a limited-correlation rounding technique that treats each unit independently while introducing controlled dependencies across allocation decisions involving the same unit. Combined with a carefully-crafted price-based method for computing the fractional allocation, this approach yields an order-optimal competitive ratio for the variable-duration setting.
In online advertising, the inherent complexity and dynamic nature of advertising environments necessitate the use of auto-bidding services to assist advertisers in bid optimization. The complexity escalates in multi-channel scenarios, where effective allocation of budgets and constraints across channels with distinct behavioral patterns becomes critical for optimizing return on investment. Current approaches predominantly employ either optimization-based strategies or reinforcement learning (RL) techniques. However, optimization-based methods lack the flexibility to adapt to dynamic market conditions, while RL-based approaches struggle to capture essential historical dependencies and observational patterns within the constraints of Markov Decision Process (MDP) frameworks. To address these limitations, we propose AHBid, an Adaptable Hierarchical Bidding framework that integrates generative planning with real-time control. The framework employs a high-level generative planner utilizing diffusion models to dynamically allocate budgets and constraints through effective capture of historical context and temporal patterns. We introduce a constraint enforcement mechanism to ensure compliance with specified constraints, complemented by a trajectory refinement mechanism that enhances adaptability to environmental changes through historical data utilization. The system further incorporates a control-based bidding algorithm that synergistically combines historical knowledge with real-time information, significantly improving both adaptability and operational efficacy. Extensive experiments are conducted using both large-scale offline datasets and online A/B tests, demonstrating the effectiveness of AHBid by yielding a 13.57% increase in overall return compared to existing baselines.
The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers achieve their economic goals. Current auto-bidding methods have evolved to use offline reinforcement learning or generative methods to optimize bidding strategies, but they can sometimes behave counterintuitively due to the black-box training manner and limited mode coverage of datasets, leading to challenges in understanding task status and generalization in dynamic ad environments. Large language models (LLMs) offer a promising solution by leveraging prior human knowledge and reasoning abilities to improve auto-bidding performance. However, directly applying LLMs to auto-bidding faces difficulties due to the need for precise actions in competitive auctions and the lack of specialized auto-bidding knowledge, which can lead to hallucinations and suboptimal decisions. To address these challenges, we propose a hierarchical Large auto-Bidding Model (LBM) to leverage the reasoning capabilities of LLMs for developing a superior auto-bidding strategy. This includes a high-level LBM-Think model for reasoning and a low-level LBM-Act model for action generation. Specifically, we propose a dual embedding mechanism to efficiently fuse two modalities, including language and numerical inputs, for language-guided training of the LBM-Act; then, we propose an offline reinforcement fine-tuning technique termed GQPO for mitigating the LLM-Think's hallucinations and enhancing decision-making performance without simulation or real-world rollout like previous multi-turn LLM-based methods. Experiments demonstrate the superiority of a generative backbone based on our LBM, especially in an efficient training manner and generalization ability.
Optimizing the advertiser's cumulative value of winning impressions under budget constraints poses a complex challenge in online advertising, under the paradigm of AI-Generated Bidding (AIGB). Advertisers often have personalized objectives but limited historical interaction data, resulting in few-shot scenarios where traditional reinforcement learning (RL) methods struggle to perform effectively. Large Language Models (LLMs) offer a promising alternative for AIGB by leveraging their in-context learning capabilities to generalize from limited data. However, they lack the numerical precision required for fine-grained optimization. To address this limitation, we introduce GRPO-Adaptive, an efficient LLM post-training strategy that enhances both reasoning and numerical precision by dynamically updating the reference policy during training. Built upon this foundation, we further propose DARA, a novel dual-phase framework that decomposes the decision-making process into two stages: a few-shot reasoner that generates initial plans via in-context prompting, and a fine-grained optimizer that refines these plans using feedback-driven reasoning. This separation allows DARA to combine LLMs' in-context learning strengths with precise adaptability required by AIGB tasks. Extensive experiments on both real-world and synthetic data environments demonstrate that our approach consistently outperforms existing baselines in terms of cumulative advertiser value under budget constraints.
AI is no longer just a component of Web systems; it is increasingly shaping the experiences users have online. From search and recommendation to conversational and generative interfaces, AI is redefining how people interact with content at Web scale. In this keynote, I reflect on how recent advances in AI, including deep learning and generative models, are reshaping the design space of Web technologies and the systems that support them. Drawing on insights from developing AI-driven systems at Spotify, I discuss how search and recommendation are evolving into interactive, intent-aware, and user-controllable experiences that support exploration and discovery. The talk highlights emerging system paradigms, deployment challenges, and open research questions around building such experiences at scale, and reflects on the implications for the design of future Web systems and interactions.