OpenRaiser/Intern-Atlas huggingface dataset:
FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket Platform Sellers
Authors: Dexin Kong, Xu Yan, Ming Chen, Shuguang Han, Jufeng Chen, Fei Huang
Venue: WWW
Year: 2025

Different from traditional Business-to-Consumer e-commerce platforms (e.g., Amazon), online fleamarket platforms (e.g., Craigslist) mainly focus on individual sellers who are lack of time investment and business proficiency. Individual sellers often struggle with the bargaining process and thus the deal is unaccomplished. Recent advancements in Large Language Models(LLMs) demonstrate huge potential in various dialogue tasks, but those tasks are mainly in the form of passively following user's instruction. Bargaining, as a form of proactive dialogue task, represents a distinct art of dialogue considering the dynamism of environment and uncertainty of adversary strategies. In this paper, we propose an LLM-empowered bargaining agent designed for online fleamarket platform sellers, named as FishBargain. Specifically, FishBargain understands the chat context and product information, chooses both action and language skill considering possible adversary actions and generates utterances. FishBargain has been tested by thousands of individual sellers on one of the largest online fleamarket platforms (Xianyu) in China. Both qualitative and quantitative experiments demonstrate that FishBargain can effectively help sellers make more deals.
Preserving Label Correlation for Multi-label Text Classification by Prototypical Regularizations
Authors: Fanshuang Kong, Richong Zhang, Xiaohui Guo, Junfan Chen, Ziqiao Wang
Venue: WWW
Year: 2025

Multi-label text classification (MLTC) assigns multiple labels to a sentence, with the key challenge being capturing label correlations. Existing models prioritize leveraging correlations but often overlook overfitting, while plug-and-play regularization methods fail to preserve correlations effectively. In this paper, we distinguish two types of label correlations: explicit co-occurring correlations and implicit semantic correlations, and propose regularizations on prototypical label embeddings for correlation preservation. Specifically, we first generate the prototypical embedding of multiple co-occurred labels as an intermediate. We then apply a prototypical regularization on the distance between the sentence embedding and corresponding prototypical embedding to alleviate the over-alignment issue caused by binary cross entropy loss and facilitate explicit correlation preservation. We finally extend the vanilla Mixup, which solely mixes multi-hot labels, on prototypical embedding mixing to promote implicit correlation preservation. Empirical studies show the effectiveness of our regularization methods.
WWW 2025 Workshop Chairs' Welcome
Authors: Piotr Koniusz, Weizhi Ma
Venue: WWW
Year: 2025

It is our great pleasure to welcome you to the Two Days Workshops at the ACM International World Wide Web Conference 2025, Sydney, Australia. This year, 156 workshop organizers have prepared 26 high-quality workshops, marking a significant increase from 16 workshops in the previous year. Of these 26 workshops, 8 will run as full-day events and 18 as half-day sessions. For the first time, the Workshop Chairs introduced a fast-track submission process, aimed at helping high-quality papers (short or long) that narrowly missed acceptance for the main conference due to minor issues. In this process, workshop organizers acted as meta-reviewers and leveraged existing reviews to expedite decisions.
Social Influence Distorts Ratings in Online Interfaces
Authors: Marina Kontalexi, Alexandros Gelastopoulos, Pantelis P. Analytis
Venue: WWW
Year: 2025

Theoretical work on sequential choice and large-scale experiments in online ranking and voting systems has demonstrated that social influence can have a drastic impact on social and technological systems. Yet, the effect of social influence on online rating systems remains understudied and the few existing contributions suggest that online ratings would self-correct given enough users. Here, we propose a new framework for studying the effect of social influence on online ratings. We start from the assumption that people are influenced linearly by the observed average rating, but postulate that their propensity to be influenced varies. When the weight people assign to the observed average depends only on their own latent rating, the resulting system is linear, but the long-term rating may substantially deviate from the true mean rating. When the weight people put on the observed average depends on both their own latent rating and the observed average rating, the resulting system is non-linear, and may support multiple equilibria, suggesting that ratings might be path-dependent and deviations dramatic. Our results highlight potential limitations in crowdsourced information aggregation and can inform the design of more robust online rating systems.
Data-Driven Democracy? Using Social Media and AI for Knowledge Co-production in Energy Transition Research
Authors: Ilona M. Kousa
Venue: WWW
Year: 2025

As global challenges grow increasingly difficult to address, the need for relevant knowledge in policymaking has become more critical than ever. In fields such as sustainability research, discussions about the co-production of knowledge emphasise the involvement of diverse stakeholders in generating relevant information for democratic decision-making processes. Particularly in technology-intensive areas like energy, the voices of experts have traditionally dominated, often marginalising the perspectives of citizens affected by energy policies and leading to epistemic injustices. Social media serves as a central forum for political activity and civic engagement, making it an important research environment for understanding the complexities of knowledge production and sharing. In my dissertation, I explore the opportunities and challenges of integrating citizen voices into the co-production of knowledge through social media using artificial intelligence (AI) based methods. My approach includes analysing stakeholder perspectives across social media, policymakers' speeches, and traditional media, using the discourse on energy justice in the context of sustainable energy transition as empirical material. Additionally, I conduct a comparative analysis of a rule-based ontological classification tool and a large language model (LLM) chatbot as qualitative content analysis tools. I demonstrate that social media data can reveal critical perspectives and marginalised discourses that might otherwise be overlooked. However, social media platforms are also used to spread disinformation and incite polarisation, which undermines their reliability as accurate reflections of prevailing attitudes and opinions in society. In addition, although AI methods can make information processing more effective and support the use of large datasets, it is important to consider their inherent limitations. I address the biases and ethical concerns associated with social media data and AI-based analysis tools and discuss potential solutions to mitigate these challenges.
Towards a Robust Framework for Multimodal Hate Detection: A Study on Video vs. Image-based Content
Authors: Girish A. Koushik, Diptesh Kanojia, Helen Treharne
Venue: WWW
Year: 2025

Social media platforms enable the propagation of hateful content across different modalities such as textual, auditory, and visual, necessitating effective detection methods. While recent approaches have shown promise in handling individual modalities, their effectiveness across different modality combinations remains unexplored. Our paper presents a systematic analysis of fusion-based approaches for multimodal hate detection, focusing on performance across video and image-based content. Our comprehensive evaluation reveals significant modality-specific limitations: while simple embedding fusion achieves state-of-the-art performance on video content with a 9.9% points F1-score improvement, it struggles with complex image-text relationships in memes. Through detailed ablation studies and error analysis, we demonstrate how current fusion approaches fail to capture nuanced cross-modal interactions, particularly in cases involving benign confounders. Our findings provide crucial insights for developing more robust hate detection systems and highlight the need for modality-specific architectural considerations. The code is available at: https://github.com/surrey-nlp/Video-vs-Meme-Hate.
Datasets for Navigating Sensitive Topics in Recommendation Systems
Authors: Amelia Kovacs, Jerry Chee, Sarah Dean
Venue: WWW
Year: 2025

Personalized AI systems, from recommendation systems to chatbots, are a prevalent method for distributing content to users based on their learned preferences. However, there is growing concern about the adverse effects of these systems, including their potential tendency to expose users to sensitive or harmful material, negatively impacting overall well-being. To address this concern quantitatively, it is necessary to create datasets with relevant sensitivity labels for content, enabling researchers to evaluate personalized systems beyond mere engagement metrics. To this end, we introduce two novel datasets that include a taxonomy of sensitivity labels alongside user-content ratings: one that integrates MovieLens rating data with content warnings from the Does the Dog Die? community ratings website, and another that combines fan-fiction interaction data and user-generated warnings from Archive of Our Own.
CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction
Authors: Yingjie Kuang, Tianchen Zhang, Zhen-Wei Huang, Zhongjie Zeng, Zhe-Yuan Li, Ling Huang, Yuefang Gao
Venue: WWW
Year: 2025

Accurately predicting customers' purchase intentions is critical to the success of a business strategy. Current researches mainly focus on analyzing the specific types of products that customers are likely to purchase in the future, little attention has been paid to the critical factor of whether customers will engage in repurchase behavior. Predicting whether a customer will make the next purchase is a classic time series forecasting task. However, in real-world purchasing behavior, customer groups typically exhibit imbalance - i.e., there are a large number of occasional buyers and a small number of loyal customers. This head-to-tail distribution makes traditional time series forecasting methods face certain limitations when dealing with such problems. To address the above challenges, this paper proposes a unified Clustering and Attention mechanism GRU model (CAGRU) that leverages multi-modal data for customer purchase intention prediction. The framework first performs customer profiling with respect to the customer characteristics and clusters the customers to delineate the different customer clusters that contain similar features. Then, the time series features of different customer clusters are extracted by GRU neural network and an attention mechanism is introduced to capture the significance of sequence locations. Furthermore, to mitigate the head-to-tail distribution of customer segments, we train the model separately for each customer segment, to adapt and capture more accurately the differences in behavioral characteristics between different customer segments, as well as the similar characteristics of the customers within the same customer segment. We constructed four datasets and conducted extensive experiments to demonstrate the superiority of the proposed CAGRU approach.
GRIT: Graph-based Recall Improvement for Task-oriented E-commerce Queries
Authors: Hrishikesh Kulkarni, Surya Kallumadi, Sean MacAvaney, Nazli Goharian, Ophir Frieder
Venue: WWW
Year: 2025

Many e-commerce search pipelines have four stages, namely: retrieval, filtering, ranking, and personalized-reranking. The retrieval stage must be efficient and yield high recall because relevant products missed in the first stage cannot be considered in later stages. This is challenging for task-oriented queries (queries with actionable intent) where user requirements are contextually intensive and difficult to understand. To foster research in the domain of e-commerce, we created a novel benchmark for Task-oriented Queries (TQE) by using LLM, which operates over the existing ESCI product search dataset. Furthermore, we propose a novel method 'Graph-based Recall Improvement for Task-oriented queries' (GRIT) to address the most crucial first-stage recall improvement needs. GRIT leads to robust and statistically significant improvements over state-of-the-art lexical, dense, and learned-sparse baselines. Our system supports both traditional and task-oriented e-commerce queries, yielding up to 6.3% recall improvement. In the indexing stage, GRIT first builds a product-product similarity graph using user clicks or manual annotation data. During retrieval, it locates neighbors with higher contextual and action relevance and prioritizes them over the less relevant candidates from the initial retrieval. This leads to a more comprehensive and relevant first-stage result set that improves overall system recall. Overall, GRIT leverages the locality relationships and contextual insights provided by the graph using neighboring nodes to enrich the first-stage retrieval results. We show that the method is not only robust across all introduced parameters, but also works effectively on top of a variety of first-stage retrieval methods.
Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach
Authors: Param Kulkarni, Yingchi Liu, Hao-Ming Fu, Shaohua Yang, Isuru Gunasekara, Matt Peloquin, Noah Spitzer-Williams, Xiaotian Zhou, Xiaozhong Liu, Zhengping Ji, Yasser Ibrahim
Venue: WWW
Year: 2025

Achieving a delicate balance between fostering trust in law enforcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and product challenge in the world today. In the pursuit of fairness and transparency, this study presents an innovative AI-driven system designed to generate police report drafts from complex, noisy, and multi-role dialogue data. Our approach intelligently extracts key elements of law enforcement interactions and includes them in the draft, producing structured narratives that are not only high in quality but also reinforce accountability and procedural clarity. This framework holds the potential to transform the reporting process, ensuring greater oversight, consistency, and fairness in future policing practices. A demonstration video of our system can be accessed at https://drive.google.com/file/d/1kBrsGGR8e3B5xPSblrchRGj-Y-kpCHNO/view?usp=sharing
Benchmarking Near-Duplicate Detection in the era of Pay-walled News
Authors: Alok Kumar, Siddharth Tumre, Sangameshwar Patil
Venue: WWW
Year: 2025

Redundancy and access restriction due to paywalls are emerging as key challenges faced by web-enabled digital news ecosystem. Over the past few years, newspapers and other news sources have increasingly put-up subscription-based paywalls for accessing their content. Further, copyright, and other legal issues have curtailed the amount of text content that can be freely shared in a news feed. Well-known near-duplicate detection algorithms such as Locality Sensitive Hashing (LSH) and its variants have been used on full length versions of documents such as webpages and news articles. So, unlike traditional input of full text articles, we now get access to significantly less quantity of text available as input. This has added to the challenges and complexities to be tackled by the near-duplicate detection algorithms. There are few, if any, gold-standard datasets available for this variation of the news duplicate detection problem. In this paper, we present NDD-NS, a dataset of business news snippets which have been annotated with near-duplicates. These news snippets are representative of challenges in the content received in the news feed nowadays. Further, we benchmark the Minhash-LSH, Simhash and sentence transformer based neural approaches. We observe that hashing based LSH approaches achieve significantly less accuracy than what it achieved on the SpotSigs dataset. This highlights the challenging nature of the NDD-NS dataset. We believe this dataset will pave way for novel approaches and further research for near-duplicate detection task on this new text genre of news snippets due to access-restrictions.
Causal Insights into Parler's Content Moderation Shift: Effects on Toxicity and Factuality
Authors: Nihal Kumarswamy, Mohit Singhal, Shirin Nilizadeh
Venue: WWW
Year: 2025

Social media platforms employ various content moderation techniques to remove harmful, offensive, and toxic content, with moderation levels varying across platforms and evolving over time. Parler, a fringe platform popular among conservative users, initially had minimal moderation, promoting itself as a space for open discussion. However, in 2021, it was removed from the Apple and Google App Stores and suspended from Amazon Web Services due to inadequate moderation of harmful content. After a month-long suspension, Parler returned with stricter guidelines, offering a unique opportunity to study the impact of platform-wide policy changes on user behavior and content outcomes. In this paper, we analyzed Parler data to assess the causal associations of these moderation changes on content toxicity and factuality. Using a longitudinal dataset of 17M posts from 432K users, who were active both before and after replatforming, we employed quasi-experimental analysis, controlling for confounding factors. We introduced a novel approach by using data from another social media platform, Twitter, to account for a critical confounding factor: offline events. This allowed us to isolate the effects of Parler's replatforming policies from external real-world influences. Our findings demonstrate that Parler's moderation changes are causally associated with a significant reduction in all forms of toxicity (p < 0.001). Additionally, we observed an increase in the factuality of the news sites shared and a reduction in the number of conspiracy/ pseudoscience sources.
Crowdsourced Fact-Checking or Biased Commentary? Analyzing Political Bias in Twitter's Community Notes
Authors: Simon Fox Kuuse, Uku Kangur, Roshni Chakraborty, Rajesh Sharma
Venue: WWW
Year: 2025

The rise of social media as a primary source of news has amplified concerns about bias and misinformation, with platforms like Twitter playing a major role in shaping public opinion. Unchecked bias can distort public perception, influence elections, and deepen societal polarization. As traditional fact-checking struggles to keep pace with the flood of online content, crowd-sourced solutions like Twitter's Community Notes offer a promising yet understudied approach to addressing these challenges. This paper provides an exploratory analysis of political bias in Twitter's Community Notes. We analyze 323,382 Community Notes from January 2021 to November 2023, using bias labels based on linked sources. We investigate lexical, sentiment, and temporal features to uncover the bias patterns. Our results show that right-leaning Community Notes have lower readability while left-leaning Notes exhibit more negative sentiment and are more connected to real-world events.
Diffusion Graph Model for Time Series Anomaly Detection via Anomaly-aware Graph Sparsification and Augmentation
Authors: Disen Lan, Guibin Zhang, Rongjin Guo
Venue: WWW
Year: 2025

Unsupervised methods, particularly reconstruction-based methods have become the dominant approach for multivariate time series anomaly detection (TSAD), which distinguish between normal and abnormal series based on the magnitude of the reconstruction error. However, in this process, the heterophilic connections (normal łeftrightarrow abnormal datapoints) often lead the model to simultaneously capture the distributions of both normal and abnormal data, impeding effective anomaly detection based on reconstruction error. To address this challenge, we introduce a novel diffusion graph model framework, dubbed øurmethod, which jointly models spatial-temporal correlations and explicitly severs heterophilic connections for improved reconstruction. Specifically, øurmethod first transforms multivariate time series into a spatial-temporal joint graph and utilizes graph diffusion to progressively denoise and learn anomaly-free node representations. Through the tailored anomaly-aware graph sparsification and contrastive augmentation, øurmethod effectively captures anomaly patterns and eliminates anomaly-related heterophily correlations on the spatio-temporal joint graph. Extensive experiments on TSAD datasets demonstrate that øurmethod achieves state-of-the-art performance, showcasing the expressiveness of our framework.
Temporal Aspects of CLIP Embeddings
Authors: Nicholas Largey, Mandy Ho, Janna Ahrndt, Behrooz Mansouri
Venue: WWW
Year: 2025

Image embedding models such as OpenAI's CLIP do not in- herently store any temporal information in the embeddings they generate. This paper argues that while such embeddings capture rich semantic relationships, they lack an innate ca- pacity to encode chronological details, requiring fine-tuning or additional layers to infer temporal attributes
I Am Not a Caveman: An Eye-tracking Study of How Users are Influenced to Search in the Era of GenAI
Authors: Sara Fahad Dawood Al Lawati
Venue: WWW
Year: 2025

This paper explores the evolving nature of information-seeking behavior in the era of Generative AI (GenAI), questioning whether we are witnessing a generational shift in how people search for information. We propose a methodology for the first phase of our research focusing specifically on user interfaces for information access with Large Language Models (LLMs). We plan to utilize eye-tracking to analyze user interactions with search engines displaying AI-generated content before the traditional ten-blue links, followed by an engagement scale to capture the user experience. The aim of conducting the first phase is to understand how users interact with new search engine interfaces that incorporate GenAI content, assess their willingness to scroll past GenAI content to view the traditional ''10 blue links' and explore how these interactions differ from existing literature on scanning search engine interfaces. Finally, we outline future directions to deepen our understanding of search behavior in the age of GenAI.
'Only ChatGPT gets me': An Empirical Analysis of GPT versus other Large Language Models for Emotion Detection in Text
Authors: Florian Lecourt, Madalina Croitoru, Konstantin Todorov
Venue: WWW
Year: 2025

This work investigates the capabilities of large language models (LLMs) in detecting and understanding human emotions through text. Drawing upon emotion models from psychology, we adopt an interdisciplinary perspective that integrates computational and affective sciences insights. The main goal is to assess how accurately they can identify emotions expressed in textual interactions and compare different models on this specific task. This research contributes to broader efforts to enhance human-computer interaction, making artificial intelligence technologies more responsive and sensitive to users' emotional nuances. By employing a methodology that involves comparisons with a state-of-the-art model on the GoEmotions dataset, we aim to gauge LLMs' effectiveness as a system for emotional analysis, paving the way for potential applications in various fields that require a nuanced understanding of human language.