OpenRaiser/Intern-Atlas huggingface dataset:
Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners' Perspective
Authors: Jessie J. Smith, Lex Beattie, Henriette Cramer
Venue: WWW
Year: 2023

Measuring and assessing the impact and “fairness’’ of recommendation algorithms is central to responsible recommendation efforts. However, the complexity of fairness definitions and the proliferation of fairness metrics in research literature have led to a complex decision-making space. This environment makes it challenging for practitioners to operationalize and pick metrics that work within their unique context. This suggests that practitioners require more decision-making support, but it is not clear what type of support would be beneficial. We conducted a literature review of 24 papers to gather metrics introduced by the research community for measuring fairness in recommendation and ranking systems. We organized these metrics into a ‘decision-tree style’ support framework designed to help practitioners scope fairness objectives and identify fairness metrics relevant to their recommendation domain and application context. To explore the feasibility of this approach, we conducted 15 semi-structured interviews using this framework to assess which challenges practitioners may face when scoping fairness objectives and metrics for their system, and which further support may be needed beyond such tools.
The More Things Change, the More They Stay the Same: Integrity of Modern JavaScript
Authors: Johnny So, Michael Ferdman, Nick Nikiforakis
Venue: WWW
Year: 2023

The modern web is a collection of remote resources that are identified by their location and composed of interleaving networks of trust. Supply chain attacks compromise the users of a target domain by leveraging its often large set of trusted third parties who provide resources such as JavaScript. The ubiquity of JavaScript, paired with its ability to execute arbitrary code on client machines, makes this particular web resource an ideal vector for supply chain attacks. Currently, there exists no robust method for users browsing the web to verify that the script content they receive from a third party is the expected content. In this paper, we present key insights to inform the design of robust integrity mechanisms, derived from our large-scale analyses of the 6M scripts we collected while crawling 44K domains every day for 77 days. We find that scripts that frequently change should be considered first-class citizens in the modern web ecosystem, and that the ways in which scripts change remain constant over time. Furthermore, we present analyses on the use of strict integrity verification (e.g., Subresource Integrity) at the granularity of the script providers themselves, offering a more complete perspective and demonstrating that the use of strict integrity alone cannot provide satisfactory security guarantees. We conclude that it is infeasible for a client to distinguish benign changes from malicious ones without additional, external knowledge, motivating the need for a new protocol to provide clients the necessary context to assess the potential ramifications of script changes.
CoTel: Ontology-Neural Co-Enhanced Text Labeling
Authors: Miao-Hui Song, Lan Zhang, Mu Yuan, Zichong Li, Qi Song, Yijun Liu, Guidong Zheng
Venue: WWW
Year: 2023

The success of many web services relies on the large-scale domain-specific high-quality labeled dataset. Insufficient public datasets motivate us to reduce the cost of data labeling while maintaining high accuracy in support of intelligent web applications. The rule-based method and the learning-based method are common techniques for labeling. In this work, we study how to utilize the rule-based and learning-based methods for resource-effective text labeling. We propose CoTel, the first ontology-neural co-enhanced framework for text labeling. We propose critical ontology extraction in the rule-based module and ontology-enhanced loss prediction in the learning-based module. CoTel can integrate explicit labeling rules and implicit labeling models and make them help each other to improve resource efficiency in text labeling tasks. We evaluate CoTel on both public datasets and real applications with three different tasks. Compared with the baseline, CoTel can reduce the time cost by 64.75% (a 2.84× speedup) and the number of labeling by 62.07%.
xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction
Authors: Xiran Song, Jianxun Lian, Hong Huang, Zihan Luo, Wei Zhou, Xue Lin, Mingqi Wu, Chaozhuo Li, Xing Xie, Hai Jin
Venue: WWW
Year: 2023

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable.
13th Temporal Web Analytics Workshop (TempWeb) Overview
Authors: Marc Spaniol, Ricardo Baeza-Yates, Omar Alonso
Venue: WWW
Year: 2023

The TempWeb workshop series has a long-standing tradition as a co-located event at The Web Conference. Its main objectives are to provide a venue for researchers of all domains (IE/IR, Web mining, etc.) where the temporal dimension opens an entirely new range of challenges and possibilities. For this, research published at TempWeb covers (and has covered in its previous editions) the full spectrum of longitudinal studies using web content. This ranges from “low-level” network analysis (e.g., Internet traffic) and graph analysis (e.g., in social media) up to “high-level” content and concept analysis ranging from micro-content (e.g., tweets) up to entire web archives. As such, TempWeb has become a potential object of longitudinal studies on its own. Aiming at the investigation of infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at Web scale, TempWeb has developed as a forum for a community from science and industry. Since longitudinal aspects in web content analysis is becoming more and more relevant for analysts from various domains, the studies, tools, and demonstrations are not only relevant for computer science, but also potentially interesting for sociology, marketing, environmental studies, politics, etc. Keeping up with its “tradition”, the current edition covers again the full spectrum of temporal Web analytics at various levels of granularity.
Evaluating the Emergence of Collective Identity using Socio-Computational Techniques
Authors: Billy Spann, Nitin Agarwal, David Stafford, Obianuju Okeke
Venue: WWW
Year: 2023

Social media platforms provide fertile ground for investigating the processes of identity creation and communication that shape individual and public opinion. The computational methods used in social network analysis have opened the way for new approaches to be used to understand the psychological and social processes that occur when users take part in online social movements or digital activism. The research in this paper takes an interdisciplinary approach bridging social identity and deindividuation theories to show how shared, individual social identities merge into a collective identity using computational techniques. We demonstrate a novel approach to evaluating the emergence of collective identity by measuring: 1) the statistical similarity of discussion topics within online communities and 2) the strength of these communities by examining network modularity and assortative properties of the network. To accomplish this, we examined the online connective action campaign of the #stopthesteal movement that emerged during the 2020 U.S. Presidential Election. Our dataset consisted of 838,395 tweets posted by 178,296 users collected from January 04, 2020, to January 31, 2021. The results show that the network becomes more cohesive and topic similarity increases within communities leading up to and just after the elections (event 1) and the U.S. Capitol riot (event 2). Taking this multi-method approach of measuring content and network structure over time helps researchers and social scientists understand the emergence of a collective community as it is being constructed. The use of computational methods to study collective identity formation can help researchers identify the behaviors and social dynamics emerging from this type of cyber-collective movement that often serve as catalysts for these types of events. Finally, this research offers a new way to assess the psycho-social drivers of participant behaviors in cyber collective action.
Graph and Embedding based Approach for Text Clustering: Topic Detection in a Large Multilingual Public Consultation
Authors: Nicolas Stefanovitch, Guillaume Jacquet, Bertrand De Longueville
Venue: WWW
Year: 2023

We present a novel algorithm for multilingual text clustering built upon two well studied techniques: multilingual aligned embedding and community detection in graphs. The aim of our algorithm is to discover underlying topics in a multilingual dataset using clustering. We present both a numerical evaluation using silhouette and V-measure metrics, and a qualitative evaluation for which we propose a new systematic approach. Our algorithm presents robust overall performance and its results were empirically evaluated by an analyst. The work we present was done in the context of a large multilingual public consultation, for which our new algorithm was deployed and used on a daily basis.
The Capable Web
Authors: Thomas Steiner
Venue: WWW
Year: 2023

In this paper, I discuss arguments in favor and in disfavor of building for the Web. I look at three extraordinary examples of apps built for the Web, and analyze reasons their creators provided for doing so. In continuation, I look at the decline of interest in cross-platform app frameworks with the exception of Flutter, which leads me to the two research questions RQ1 "Why do people not fully bet on PWA" and RQ2 "Why is Flutter so popular". My hypothesis for why developers don’t more frequently set on the Web is that in many cases they (or their non-technical reporting lines) don’t realize how powerful it has become. To counter that, I introduce a Web app and a browser extension that demonstrate the Web’s capabilities.
Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation
Authors: Jiajie Su, Chaochao Chen, Weiming Liu, Fei Wu, Xiaolin Zheng, Haoming Lyu
Venue: WWW
Year: 2023

Session-based Recommendation aims at predicting the next interacted item based on short anonymous behavior sessions. However, existing solutions neglect to model two inherent properties of sequential representing distributions, i.e., hierarchy structures resulted from item popularity and collaborations existing in both intra- and inter-session. Tackling with these two factors at the same time is challenging. On the one hand, traditional Euclidean space utilized in previous studies fails to capture hierarchy structures due to a restricted representation ability. On the other hand, the intuitive apply of hyperbolic geometry could extract hierarchical patterns but more emphasis on degree distribution weakens intra- and inter-session collaborations. To address the challenges, we propose a Hierarchy-Aware Dual Clustering Graph Network (HADCG) model for session-based recommendation. Towards the first challenge, we design the hierarchy-aware graph modeling module which converts sessions into hyperbolic session graphs, adopting hyperbolic geometry in propagation and attention mechanism so as to integrate chronological and hierarchical information. As for the second challenge, we introduce the deep dual clustering module which develops a two-level clustering strategy, i.e., information regularizer for intra-session clustering and contrastive learner for inter-session clustering, to enhance hyperbolic representation learning from collaborative perspectives and further promote recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed HADCG.
Automatic Discovery of Emerging Browser Fingerprinting Techniques
Authors: Junhua Su, Alexandros Kapravelos
Venue: WWW
Year: 2023

With the progression of modern browsers, online tracking has become the most concerning issue for preserving privacy on the web. As major browser vendors plan to or already ban third-party cookies, trackers have to shift towards browser fingerprinting by incorporating novel browser APIs into their tracking arsenal. Understanding how new browser APIs are abused in browser fingerprinting techniques is a significant step toward ensuring protection from online tracking. In this paper, we propose a novel hybrid system, named BFAD, that automatically identifies previously unknown browser fingerprinting APIs in the wild. The system combines dynamic and static analysis to accurately reveal browser API usage and automatically infer browser fingerprinting behavior. Based on the observation that a browser fingerprint is constructed by pulling information from multiple APIs, we leverage dynamic analysis and a locality-based algorithm to discover all involved APIs and static analysis on the dataflow of fingerprinting information to accurately associate them together. Our system discovers 231 fingerprinting APIs in Alexa top 10K domains, starting with only 35 commonly known fingerprinting APIs and 17 data transmission APIs. Out of 231 APIs, 161 of them are not identified by state-of-the-art detection systems. Since our approach is fully automated, we repeat our experiments 11 months later and discover 18 new fingerprinting APIs that were not discovered in our previous experiment. We present with case studies the fingerprinting ability of a total of 249 detected APIs.
Templet: A Collaborative System for Knowledge Graph Question Answering over Wikidata
Authors: Francisca Suárez, Aidan Hogan
Venue: WWW
Year: 2023

We present Templet: an online question answering (QA) system for Wikidata. Templet is based on the collaboratively-edited repository QAWiki, which collects questions in multiple natural languages along with their corresponding structured queries. Templet generates templates from question–query pairs on QAWiki by replacing key entities with identifiers. Using autocompletion, the user can type a question in natural language, select a template, and again using autocompletion, select the entities they wish to insert into the template’s placeholders, generating a concrete question, query and results. The main objectives of Templet are: (i) to enable users to answer potentially complex questions over Wikidata using natural language templates and autocompletion; (ii) to encourage users to collaboratively create new templates via QAWiki, which in turn can benefit not only Templet, but other QA systems.
SCTAP: Supporting Scenario-Centric Trigger-Action Programming based on Software-Defined Physical Environments
Authors: Bingkun Sun, Liwei Shen, Xin Peng, Ziming Wang
Venue: WWW
Year: 2023

The physical world we live in is accelerating digitalization with the vigorous development of Internet of Things (IoT). Following this trend, Web of Things (WoT) further enables fast and efficient creation of various applications that perceive and act on the physical world using standard Web technologies. A popular way for creating WoT applications is Trigger-Action Programming (TAP), which allows users to orchestrate the capabilities of IoT devices in the form of “if trigger, then action”. However, existing TAP approaches don’t support scenario-centric WoT applications which involve abstract modeling of physical environments and complex spatio-temporal dependencies between events and actions. In this paper, we propose an approach called SCTAP which supports Scenario-Centric Trigger-Action Programming based on software-defined physical environments. SCTAP defines a structured and conceptual representation for physical environments, which provides the required programming abstractions for WoT applications. Based on the representation, SCTAP defines a grammar for specifying scenario-centric WoT applications with spatio-temporal dependencies. Furthermore, we design a service-based architecture for SCTAP which supports the integration of device access, event perception, environment representation, and rule execution in a loosely-coupled and extensible way. We implement SCTAP as a WoT infrastructure and evaluate it with two case studies including a smart laboratory and a smart coffee house. The results confirm the usability, feasibility and efficiency of SCTAP and its implementation.
Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs
Authors: Susheel Suresh, Mayank Shrivastava, Arko Mukherjee, Jennifer Neville, Pan Li
Venue: WWW
Year: 2023

Temporal graph representation learning (T-GRL) aims to learn representations that model how graph edges evolve over time. While recent works on T-GRL have improved link prediction accuracy in temporal settings, their methods optimize a point-wise loss function independently over future links rather than optimize jointly over a candidate set per node. In applications where resources (e.g., attention) are allocated based on ranking links by likelihood, the use of a ranking loss is preferred. However it is not straightforward to develop a T-GRL method to optimize a ranking loss due to a tradeoff between model expressivity and scalability. In this work, we address these issues and propose a Temporal Graph network for Ranking (TGRank), which significantly improves performance for link prediction tasks by (i) optimizing a list-wise loss for improved ranking, and (ii) incorporating a labeling approach designed to allow for efficient inference over the candidate set jointly, while provably boosting expressivity. We extensively evaluate TGRank over six real networks. TGRank outperforms the state-of-the-art baselines on average by 14.21%↑ (transductive) and 16.25% ↑ (inductive) in ranking metrics while being more efficient (up-to 65 × speed-up) to make inference on large networks.
Cultural Differences in Signed Ego Networks on Twitter: An Investigatory Analysis
Authors: Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti
Venue: WWW
Year: 2023

Human social behaviour has been observed to adhere to certain structures. One such structure, the Ego Network Model (ENM), has been found almost ubiquitously in human society. Recently, this model has been extended to include signed connections. While the unsigned ENM has been rigorously observed for decades, the signed version is still somewhat novel and lacks the same breadth of observation. Therefore, the main aim of this paper is to examine this signed structure across various categories of individuals from a swathe of culturally distinct regions. Minor differences in the distribution of signs across the SENM can be observed between cultures. However, these can be overwhelmed when the network is centred around a specific topic. Indeed, users who are engaged with specific themes display higher levels of negativity in their networks. This effect is further supported by a significant negative correlation between the number of "general" topics discussed in a network and that network’s percentage of negative connections. These findings suggest that the negativity of communications and relationships on Twitter are very dependent on the topics being discussed and, furthermore, these relationships are more likely to be negative when they are based around a specific topic.
Worst-Case Welfare of Item Pricing in the Tollbooth Problem
Authors: Zihan Tan, Yifeng Teng, Mingfei Zhao
Venue: WWW
Year: 2023

We study the worst-case welfare of item pricing in the tollbooth problem. The problem was first introduced by Guruswami et al. [27], and is a special case of the combinatorial auction in which (i) each of the m items in the auction is an edge of some underlying graph; and (ii) each of the n buyers is single-minded and only interested in buying all edges of a single path. We consider the competitive ratio between the hindsight optimal welfare and the optimal worst-case welfare among all item-pricing mechanisms, when the order of the arriving buyers is adversarial. We assume that buyers own the tie-breaking power, i.e. they can choose whether or not to buy the demand path at 0 utility. We prove a tight competitive ratio of 3/2 when the underlying graph is a single path (also known as the highway problem), whereas item-pricing can achieve the hindsight optimal if the seller is allowed to choose a proper tie-breaking rule to maximize the welfare [6, 11]. Moreover, we prove an O(1) upper bound of competitive ratio when the underlying graph is a tree. For general graphs, we prove an Ω(m1/8) lower bound of the competitive ratio. We show that an mΩ(1) competitive ratio is unavoidable even if the graph is a grid, or if the capacity of every edge is augmented by a constant factor c. The results hold even if the seller has tie-breaking power.
Multi-Lingual Multi-Partite Product Title Matching
Authors: Huan-Lin Tay, Wei-Jie Tay, Hady W. Lauw
Venue: WWW
Year: 2023

In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.