OpenRaiser/Intern-Atlas huggingface dataset:
Towards Fact-check Summarization Leveraging on Argumentation Elements Tied to Entity Graphs
Authors: Katerina Haniková, David Chudán, Vojtech Svátek, Peter Vajdecka, Raphaël Troncy, Filip Vencovský, Jana Syrovátková
Venue: WWW
Year: 2024

Fact-check consumers can have different preferences regarding the amount of text being used for explaining the claim veracity verdict. Dynamically adapting the size of a fact-check report is thus an important functionality for systems designed to convey claim verification explainability. Recent works have experimented with applying transformers-based or LLM-based text summarization methods in a zero-shot or few-shot manner, making use of some existing texts available in the summary parts of fact-check reports (e.g., called "justification'' in PolitiFact). However, for complex fact-checks, the purely sub-symbolic summarizers tend to either omit some elements of the fact-checker's argumentation chains or include contextual statements that may not be essential at the given level of granularity. In this paper, we propose a new method for enhancing fact-check summarization with the aim of injecting elements of structured fact-checker argumentation. This argumentation is, in turn, not only captured at the discourse level but tied to an entity graph representing the fact-check, for which we employ the PURO diagrammatic language. We have empirically performed a manual analysis of fact-check reports from two fact-checker websites, yielding (1) textual snippets containing the argumentation essence of the fact-check report and (2) categorized argumentation elements tied to entity graphs. These snippets are then fed to a state-of-the-art hybrid summarizer which has previously produced accurate fact-check summaries, as an additional input. We observe mild improvements on various ROUGE metrics, even if the validity of the results is limited given the small size of the dataset. We also compare the human-provided argumentation element categories with those returned, for the given fact-check ground truth summary, using a pre-trained language model upon both basic and augmented prompting. This yields a moderate accuracy as the model often fails to comply with the explicit given instructions.
Towards Expansive and Adaptive Hard Negative Mining: Graph Contrastive Learning via Subspace Preserving
Authors: Zhezheng Hao, Haonan Xin, Long Wei, Liaoyuan Tang, Rong Wang, Feiping Nie
Venue: WWW
Year: 2024

Graph Neural Networks (GNNs) have emerged as the predominant approach for analyzing graph data on the web and beyond. Contrastive learning (CL), a self-supervised paradigm, not only mitigates reliance on annotations but also has potential in performance. The hard negative sampling strategy that benefits CL in other domains proves ineffective in the context of Graph Contrastive Learning (GCL) due to the message passing mechanism. Embracing the subspace hypothesis in clustering, we propose a method towards expansive and adaptive hard negative mining, referred to as G raph contR astive leA rning via subsP ace prE serving (GRAPE ). Beyond homophily, we argue that false negatives are prevalent over an expansive range and exploring them confers benefits upon GCL. Diverging from existing neighbor-based methods, our method seeks to mine long-range hard negatives throughout subspace, where message passing is conceived as interactions between subspaces. %Empirical investigations back up this strategy. Additionally, our method adaptively scales the hard negatives set through subspace preservation during training. In practice, we develop two schemes to enhance GCL that are pluggable into existing GCL frameworks. The underlying mechanisms are analyzed and the connections to related methods are investigated. Comprehensive experiments demonstrate that our method outperforms across diverse graph datasets and remains competitive across varied application scenarios\footnoteOur code is available at https://github.com/zz-haooo/WWW24-GRAPE. .
History in Making: Political Campaigns in the Era of Artificial Intelligence-Generated Content
Authors: Ehsan ul Haq, Yiming Zhu, Pan Hui, Gareth Tyson
Venue: WWW
Year: 2024

Web 2.0 provided impactful tools, based on user-generated content, for political campaigns and opinion engineering. However, in recent months, AI advances and the ease of access to AI-generated content (AIGC) have led to a paradigm shift in political participation by politicians and electorates alike. This paper aims to explore a historical analysis of this shift. We provide anecdotal evidence of new trends, potential impact, and challenges. We discuss the usage of AIGC in political campaigns, and how AIGC is used as a substitute for incarcerated politicians. Such a usage presents novel ways for leaders to reach the public and keep them politically active. However, AIGC also has risks when used for disinformation, such as DeepFake media and caller bots, to undermine and malign the opponents. On the other hand, the evidence shows that governments can nudge AIGC content by censoring Internet services. We also report challenges facing AIGC usage, such as model bias and hallucinations, along with a governance perspective on ethics and regulations.
CAAW'24: The 3rd International Cryptoasset Analytics Workshop
Authors: Bernhard Haslhofer, Friedhelm Victor, Jiahua Xu, Kwok-Yan Lam
Venue: WWW
Year: 2024

The third iteration of the half-day workshop on Cryptoasset Analytics continues to offer a forum for researchers from diverse fields to share their latest discoveries in the domain of cryptoassets. This workshop retains its importance to the Web research community, motivated by how fundamental concepts of cryptoassets are progressively melding with Web technologies on a technical level, and through the observation that the evolution of socio-technical cryptoasset ecosystems are intricately linked to the Web. The program features a mix of of invited talks alongside a carefully curated selection of peer-reviewed contributions. Workshop topics encompass a variety of themes, including empirical analyses, statistical methodologies, privacy measures, architectural and regulatory insights.
Box2Go: Collaborative Interactive Infobox Filling
Authors: Benjamin Hättasch, Carsten Binnig
Venue: WWW
Year: 2024

Infoboxes can be useful to quickly learn about the contents of text collections, but manually creating them is error-prone and time-consuming, and existing automatic approaches require training data or resources like ontologies that are not available for every domain. Moreover, they lack techniques for adaptation to the user. We therefore propose a system to automatically fill user-defined attributes of infoboxes with the human-in-the-loop which provides this adaptation, and works without training data and domain-specific resources. Our approach generalizes simple user feedback to explore a joint embedding space and find the correct values for the attributes. These structured representations of the texts can be used for collaborative exploration of text collections on the web. We provide a prototypic implementation for such a collaborative web application and demonstrate its usage.
AI Driven Online Advertising: Market Design, Generative AI, and Ethics
Authors: Fengxiang He, Mengnan Du, Aris Filos-Ratsikas, Lu Cheng, Qingquan Song, Min Lin, John Vines
Venue: WWW
Year: 2024

Online advertising contributes a considerable part of the tech sector's revenue, and has been remarkably influencing the public agenda. With evolving developments, AI is playing an increasingly significant role in online advertising. We propose to create a forum for researchers, developers, users, ventures, policymakers, and other stakeholders to exchange ideas, research, innovations, etc. with emphasis on (1) AI driven mechanism design for distributing advertisements, (2) generative AI for creating content in advertisements, such as the promotion images/videos, and (3) ethics issues, especially in political advertisements, such as user privacy, fairness, hating speech, misinformation, etc. Relevant but not mentioned areas are also much encouraged. We plan to organize a half-day workshop.
TrustLOG: The Second Workshop on Trustworthy Learning on Graphs
Authors: Jingrui He, Jian Kang, Fatemeh Nargesian, Haohui Wang, An Zhang, Dawei Zhou
Venue: WWW
Year: 2024

Learning on graphs (LOG) has a profound impact on various high-impact domains, such as information retrieval, social network analysis, computational chemistry and transportation. Despite decades of theoretical development, algorithmic advancements, and open-source systems that answers what the optimal learning results are, concerns about the trustworthiness of state-of-the-art LOG techniques have emerged in practical applications. Consequently, crucial research questions arise: why are LOG techniques untrustworthy with respect to critical social aspects like fairness, transparency, privacy, and security? How can we ensure the trustworthiness of learning algorithms on graphs? To address the increasingly important safety and ethical challenges in learning on graphs, it is essential to achieve a paradigm shift from solely addressing what questions to understanding how and why questions. Building upon the success of the first TrustLOG workshop in 2022, the second TrustLOG workshop aims to bring together researchers and practitioners to present, discuss, and advance cutting-edge research in the realm of trustworthy learning on graphs. The workshop serves as a platform to stimulate the TrustLOG community, fostering the identification of new research challenges, and shedding light on potential future directions.
Brinjal: A Web-Plugin for Collaborative Hate Speech Detection
Authors: Ming Shan Hee, Karandeep Singh, Charlotte Ng Si Min, Kenny Tsu Wei Choo, Roy Ka-Wei Lee
Venue: WWW
Year: 2024

The proliferation of hate speech (HS) has compromised the safety and trustworthiness of the internet, exacerbating social divides by promoting hatred and discrimination. Although recent studies have produced guidelines and developed advanced technologies for the automated detection of HS, their efficacy and adaptability in real-world applications remain unclear. Furthermore, existing guidelines on what constitutes HS might not reflect the perspectives and beliefs of individuals and communities. This paper introduces Brinjal, a multifaceted web plugin designed for the collaborative detection of HS. Brinjal enables individuals to identify instances of HS and engage in discussions to verify such content, thereby enhancing the collective understanding of HS. Additionally, Brinjal serves as a practical platform for deploying and evaluating advanced HS detection models, facilitating user interaction and performance assessment. Lastly, Brinjal includes an analytical tool for analyzing HS, offering insights based on the crowdsourced instances and discussions about HS across various websites. The video demonstration of Brinjal can be viewed here: https://youtu.be/\_JxziIVWBO4. Disclaimer: This paper contains violent and discriminatory content that may be disturbing to some readers.
Mining Interest Diffusion in Online Activity Data Streams
Authors: Shingo Higashiguchi
Venue: WWW
Year: 2024

Modeling and forecasting such data is difficult because online activity data is high-dimensional and composed of multiple time-varying dynamics such as trends, seasonality, and diffusion of interest. In this paper, we propose D-Tracker, designed to capture latent dynamics in online activity data streams and forecast future values. Our proposed method has the following properties: (a) Interpretable: it uses interpretable differential equations to model the latent dynamics in online activity data, which enables us to capture trends and interest diffusion among locations; (b) Automatic: it determines the number of latent dynamics and the number of seasonal patterns fully automatically; (c) Scalable: it incrementally and adaptively detects shifting points of patterns for a semi-infinite collection of tensor streams. (c)Scalable : the computation time of D-Tracker is independent of the time series length. Experiments using web search volume data obtained from GoogleTrends show that the proposed method can achieve higher forecasting accuracy in less computation time than existing methods while extracting the patterns of interest diffusion among locations.
PRINT: Personalized Relevance Incentive Network for CTR Prediction in Sponsored Search
Authors: Zhaolin Hong, Haitao Wang, Chengjie Qian, Wei Chen, Tianqi He, Yajie Zou, Qiang Liu, Xingxing Wang
Venue: WWW
Year: 2024

Click-Through Rate (CTR) prediction plays a critical role in sponsored search. Modeling the semantic relevance between queries and ads is one of the most crucial factors affecting the performance of CTR prediction. However, different users have different sensitivities to semantic relevance due to their personalized relevance preferences. Therefore, semantic relevance may have different incentives on the user's click probability (i.e., stimulative incentive, inhibitive incentive, or irrelevant incentive). Unfortunately, few works have studied the phenomenon, which ignores the complicated incentive effects of semantic relevance and limits the performance of CTR prediction. To this end, we propose a novel Personalized Relevance Incentive N eTwork (PRINT for short) to explicitly model the personalized incentives of query-ad semantic relevance on user's click probability. Specifically, we introduce a User Relevance Preference Module (usertask) to extract the user's personalized relevance preference from historical query-ad interacted sequence. Then, a RElevance Incentive Module (REIM) is designed to discern three incentive types and model the personalized incentive effects on CTR prediction. Experiments on public datasets and industrial datasets demonstrate the significant improvement of our PRINT. Furthermore, PRINT is also deployed in the sponsored search advertising system in Meituan, obtaining an improvement of 1.94% and 2.29% in CTR and Cost Per Mile (CPM) respectively. We publish the source code at https://anonymous.4open.science/r/PRINT-D365/.
Thought Graph: Generating Thought Process for Biological Reasoning
Authors: Chi-Yang Hsu, Kyle Cox, Jiawei Xu, Zhen Tan, Tianhua Zhai, Mengzhou Hu, Dexter Pratt, Tianlong Chen, Ziniu Hu, Ying Ding
Venue: WWW
Year: 2024

We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes. Our framework stands out for its ability to provide a deeper understanding of gene sets, significantly surpassing GSEA by 40.28% and LLM baselines by 5.38% based on cosine similarity to human annotations. Our analysis further provides insights into future directions of biological processes naming, and implications for bioinformatics and precision medicine.
ZipZap: Efficient Training of Language Models for Large-Scale Fraud Detection on Blockchain
Authors: Sihao Hu, Tiansheng Huang, Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, Ling Liu
Venue: WWW
Year: 2024

Language models (LMs) have demonstrated superior performance in detecting fraudulent activities on Blockchains. Nonetheless, the sheer volume of Blockchain data results in excessive memory and computational costs when training LMs from scratch, limiting their capabilities to large-scale applications. In this paper, we present ZipZap, a framework tailored to achieve both parameter and computational efficiency when training LMs on large-scale transaction data. First, with the frequency-aware compression, an LM can be compressed down to a mere 7.5% of its initial size with an imperceptible performance dip. This technique correlates the embedding dimension of an address with its occurrence frequency in the dataset, motivated by the observation that embeddings of low-frequency addresses are insufficiently trained and thus negating the need for a uniformly large dimension for knowledge representation. Second, ZipZap accelerates the speed through the asymmetric training paradigm: It performs transaction dropping and cross-layer parameter-sharing to expedite the pre-training process, while revert to the standard training paradigm for fine-tuning to strike a balance between efficiency and efficacy, motivated by the observation that the optimization goals of pre-training and fine-tuning are inconsistent. Evaluations on real-world, large-scale datasets demonstrate that ZipZap delivers notable parameter and computational efficiency improvements for training LMs. Our implementation is available at: https://github.com/git-disl/ZipZap.
λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing
Authors: Haichuan Hu, Fangming Liu, Qiangyu Pei, Yongjie Yuan, Zichen Xu, Lin Wang
Venue: WWW
Year: 2024

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste. Observing the significant data locality in computation graphs of requests, we propose λGrapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. "Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of λGrapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that λGrapher can achieve an average savings of 61.5% in memory resource and 47.2% in computing resource compared with the state of the arts while ensuring GNN inference latency.
Euphemism Identification via Feature Fusion and Individualization
Authors: Yuxue Hu, Mingmin Wu, Zhongqiang Huang, Junsong Li, Xing Ge, Ying Sha
Venue: WWW
Year: 2024

Euphemisms are widely used on social media and darknet markets to evade supervision. For instance, "ice" serves as a euphemism for the target keyword "methamphetamine" in illicit transactions. Thus, euphemism identification which aims to map the euphemism to its secret meaning (target keyword) is a crucial task in ensuring social network security. However, this task poses significant challenges, including resource limitations due to the unavailable of annotated datasets and linguistic challenges arising from subtle differences in meaning between target keywords. Existing methods employed self-supervised schemes to automatically construct labeled training data, addressing the resource limitations. Yet, these methods rely on static embedding methods that fail to distinguish between target keywords with similar meanings. In addition, we observe that different euphemisms in similar contexts confuse the identification results. To overcome these obstacles, we propose a feature fusion and individualization (FFI) method for euphemism identification. First, we reformulate the task as a cloze task, making it more feasible. Next, we develop a feature fusion module to capture both dynamic global and static local features, enhancing discrimination between different euphemisms in similar contexts. Additionally, we employ a feature individualization module to ensure each target keyword has a unique feature representation by projecting features into their orthogonal space. As a result, FFI can effectively identify similar euphemisms that refer to target keywords with similar meanings. Experimental results demonstrate that our method outperforms state-of-the-art methods and large language models, providing robust support for its effectiveness.
Unlocking the Non-deterministic Computing Power with Memory-Elastic Multi-Exit Neural Networks
Authors: Jiaming Huang, Yi Gao, Wei Dong
Venue: WWW
Year: 2024

With the increasing demand for Web of Things (WoT) and edge computing, the efficient utilization of limited computing power on edge devices is becoming a crucial challenge. Traditional neural networks (NNs) as web services rely on deterministic computational resources. However, they may fail to output the results on non-deterministic computing power which could be preempted at any time, degrading the task performance significantly. Multi-exit NNs with multiple branches have been proposed as a solution, but the accuracy of intermediate results may be unsatisfactory. In this paper, we propose MEEdge, a system that automatically transforms classic single-exit models into heterogeneous and dynamic multi-exit models which enables Memory-Elastic inference at the Edge with non-deterministic computing power. To build heterogeneous multi-exit models, MEEdge uses efficient convolutions to form a branch zoo and High Priority First (HPF)-based branch placement method for branch growth. To adapt models to dynamically varying computational resources, we employ a novel on-device scheduler for collaboration. Further, to reduce the memory overhead caused by dynamic branches, we propose neuron-level weight sharing and few-shot knowledge distillation(KD) retraining. Our experimental results show that models generated by MEEdge can achieve up to 27.31% better performance than existing multi-exit NNs.
A Tale of Two Communities: Exploring Academic References on Stack Overflow
Authors: Run Huang, Souti Chattopadhyay
Venue: WWW
Year: 2024

Stack Overflow is widely recognized by software practitioners as the go-to resource for addressing technical issues and sharing practical solutions. While not typically seen as a scholarly forum, users on Stack Overflow commonly refer to academic sources in their discussions. Yet, little is known about these referenced academic works and how they intersect the needs and interests of the Stack Overflow community. To bridge this gap, we conducted an exploratory large-scale study on the landscape of academic references in Stack Overflow. Our findings reveal that Stack Overflow communities with different domains of interest engage with academic literature at varying frequencies and speeds. The contradicting patterns suggest that some disciplines may have diverged in their interests and development trajectories from the corresponding practitioner community. Finally, we discuss the potential of Stack Overflow in gauging the real-world relevance of academic research.