OpenRaiser/Intern-Atlas huggingface dataset:
Graphs in Computer Vision then and now: how Deep Learning has reinvigorated Structural Pattern Recognition
Authors: Donatello Conte
Venue: WWW
Year: 2022

Computer Vision Problems, such as object detection, object tracking, action recognition and so on, have been, in the past, usually addressed through Statistical Pattern Recognition techniques. SVM, Regression or Neural Networks, are some examples of classical statistical techniques that have been used, quite effectively, in many application contexts of computer vision. Nevertheless, some attempts have been proposed using more complex data structures (notably graphs) for solving Computer Vision Tasks. However, in terms of performances, their use did not have the same success as techniques based on vector representations. First part of this talk will present some of these proposals, in the context of object tracking ([1]), people re-identification ([3]) and action recognition ([2]). An graph representation is proposed in [1] to deal with occlusion problem. The representation is based on a graph pyramid, namely, each moving region is represented at different levels of resolution using a graph for each level. The algorithm compares the topmost levels of each pyramid in the association phase between moving objects in two consecutive frames. If the comparison outcome is sufficient to assign a label to each node the tracking algorithm stops. Instead, if some ambiguities arise (as it is the case when two objects over- lap), the algorithm is repeated using the next levels of the pyramids, until either a consistent labelling is found. The purpose of re-identification (re-id) is to identify people coming back into the field of view of a camera or to recognize an individual through different cameras in a distributed network. At the heart of the process there is a comparison between signatures given probe and gallery sets. In [3] graphs are used to represent people appearance and comparison is done by means of Graph Kernels. Finally, action recognition is a classification problem in which each video representing an action has to be classified with the correct action label. In [2] we proposed to represent videos using graph sequences and proposed a model inspired from bag-of-words techniques to classify a sequence. Recently, graphs have gained a lot of attention in the Computer Vision community thanks to the use of this kind of data within deep learning techniques. Graph Neural Networks have demonstrated their effectiveness in solving Computer Vision problems, and in some cases recent proposals have bridged the gap between statistical and structural pattern recognition. Second part of the talk will be devoted to illustrate some of these examples ([4, 5, 6]). Starting from the already mentioned applications in Computer Vision (object tracking, action recognition), we will discuss the new proposals based on Deep Learning with graphs and the open problems in this context.
Exploring Representations for Singular and Multi-Concept Relations for Biomedical Named Entity Normalization
Authors: Clint Cuffy, Evan French, Sophia Fehrmann, Bridget T. McInnes
Venue: WWW
Year: 2022

Since the rise of the COVID-19 pandemic, peer-reviewed biomedical repositories have experienced a surge in chemical and disease related queries. These queries have a wide variety of naming conventions and nomenclatures from trademark and generic, to chemical composition mentions. Normalizing or disambiguating these mentions within texts provides researchers and data-curators with more relevant articles returned by their search query. Named entity normalization aims to automate this disambiguation process by linking entity mentions onto their appropriate candidate concepts within a biomedical knowledge base or ontology. We explore several term embedding aggregation techniques in addition to how the term’s context affects evaluation performance. We also evaluate our embedding approaches for normalizing term instances containing one or many relations within unstructured texts.
ALLIE: Active Learning on Large-scale Imbalanced Graphs
Authors: Limeng Cui, Xianfeng Tang, Sumeet Katariya, Nikhil Rao, Pallav Agrawal, Karthik Subbian, Dongwon Lee
Venue: WWW
Year: 2022

Human labeling is time-consuming and costly. This problem is further exacerbated in extremely imbalanced class label scenarios, such as detecting fraudsters in online websites. Active learning selects the most relevant example for human labelers to improve the model performance at a lower cost. However, existing methods for active learning for graph data often assumes that both data and label distributions are balanced. These assumptions fail in extreme rare-class classification scenarios, such as classifying abusive reviews in an e-commerce website. We propose a novel framework ALLIE to address this challenge of active learning in large-scale imbalanced graph data. In our approach, we efficiently sample from both majority and minority classes using a reinforcement learning agent with imbalance-aware reward function. We employ focal loss in the node classification model in order to focus more on rare class and improve the accuracy of the downstream model. Finally, we use a graph coarsening strategy to reduce the search space of the reinforcement learning agent. We conduct extensive experiments on benchmark graph datasets and real-world e-commerce datasets. ALLIE out-performs state-of-the-art graph-based active learning methods significantly, with up to 10% improvement of F1 score for the positive class. We also validate ALLIE on a proprietary e-commerce graph data by tasking it to detect abuse. Our coarsening strategy reduces the computational time by up to 38% in both proprietary and public datasets.
Technology Growth Ranking Using Temporal Graph Representation Learning
Authors: Daniel Cummings, Ashrita Brahmaroutu, Marcel Nassar, Nesreen K. Ahmed
Venue: WWW
Year: 2022

A key component of technology sector business strategy is understanding the mechanisms by which technologies are adopted and the rate of their growth over time. Furthermore, predicting how technologies grow in relation to each other informs business decision-making in terms of product definition, research and development, and marketing strategies. An important avenue for exploring technology trends is by looking at activity in the software community. Social networks for developers can provide useful technology trend insights and have an inherent temporal graph structure. We demonstrate an approach to technology growth ranking that adapts spatiotemporal graph neural networks to work with structured temporal relational graph data.
Assessing Network Representations for Identifying Interdisciplinarity
Authors: Eoghan Cunningham, Derek Greene
Venue: WWW
Year: 2022

Many studies have sought to identify interdisciplinary research as a function of the diversity of disciplines identified in an article’s references or citations. However, given the constant evolution of the scientific landscape, disciplinary boundaries are shifting and blurring, making it increasingly difficult to describe research within a strict taxonomy. In this work, we explore the potential for graph learning methods to learn embedded representations for research papers that encode their ‘interdisciplinarity’ in a citation network. This facilitates the identification of interdisciplinary research without the use of disciplinary categories. We evaluate these representations and their ability to identify interdisciplinary research, according to their utility in interdisciplinary citation prediction. We find that those representations which preserve structural equivalence in the citation graph are best able to predict distant, interdisciplinary interactions in the network, according to multiple definitions of citation distance.
Multi-Context Based Neural Approach for COVID-19 Fake-News Detection
Authors: Arkadipta De, Maunendra Sankar Desarkar
Venue: WWW
Year: 2022

When the world is facing the COVID-19 pandemic, society is also fighting another battle to tackle misinformation. Due to the widespread effect of COVID 19 and increased usage of social media, fake news and rumors about COVID-19 are being spread rapidly. Identifying such misinformation is a challenging and active research problem. The lack of suitable datasets and external world knowledge contribute to the challenges associated with this task. In this paper, we propose MiCNA, a multi-context neural architecture to mitigate the problem of COVID-19 fake news detection. In the proposed model, we leverage the rich information of the three different pre-trained transformer-based models, i.e., BERT, BERTweet and COVID-Twitter-BERT to three different aspects of information (viz. general English language semantics, Tweet semantics, and information related to tweets on COVID 19) which together gives us a single multi-context representation. Our experiments provide evidence that the proposed model outperforms the existing baseline and the candidate models (i.e., three transformer architectures) and becomes a state-of-the-art model on the task of COVID-19 fake-news detection. We achieve new state-of-the-art performance on a benchmark COVID-19 fake-news dataset with 98.78% accuracy on the validation dataset and 98.69% accuracy on the test dataset.
Methodology to Compare Twitter Reaction Trends between Disinformation Communities, to COVID related Campaign Events at Different Geospatial Granularities
Authors: Debraj De, Gautam S. Thakur, Drahomira Herrmannova, Carter Christopher
Venue: WWW
Year: 2022

With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology.
A Policy-Oriented Architecture for Enforcing Consent in Solid
Authors: Laurens Debackere, Pieter Colpaert, Ruben Taelman, Ruben Verborgh
Venue: WWW
Year: 2022

The Solid project aims to restore end-users’ control over their data by decoupling services and applications from data storage. To realize data governance by the user, the Solid Protocol 0.9 relies on Web Access Control, which has limited expressivity and interpretability. In contrast, recent privacy and data protection regulations impose strict requirements on personal data processing applications and the scope of their operation. The Web Access Control mechanism lacks the granularity and contextual awareness needed to enforce these regulatory requirements. Therefore, we suggest a possible architecture for relating Solid’s low-level technical access control rules with higher-level concepts such as the legal basis and purpose for data processing, the abstract types of information being processed, and the data sharing preferences of the data subject. Our architecture combines recent technical efforts by the Solid community panels with prior proposals made by researchers on the use of ODRL and SPECIAL policies as an extension to Solid’s authorization mechanism. While our approach appears to avoid a number of pitfalls identified in previous research, further work is needed before it can be implemented and used in a practical setting.
One year of DALIDA Data Literacy Workshops for Adults: A Report
Authors: Christophe Debruyne, Laura Grehan, Mairéad Hurley, Anne Kearns, Ciaran O&apos, Neill
Venue: WWW
Year: 2022

This paper reports on the data literacy discussion workshops we held between April 2021 and January 2022. We developed these workshops for adults in Ireland and, while open to everyone, we sought to reach adults from socially, economically, or educationally disadvantaged groups. This experience paper describes the workshop’s structure, elaborates on our challenges (primarily due to the pandemic), and details some of the lessons we’ve learned. We also present the findings of our participant evaluations. While the drop-out rate was high, the participants who participated and filled in the evaluation form were delighted with the workshop’s content and format. The most important lesson we’ve learned is that a collaboration between scholars and education and public engagement teams (EPE), where both stakeholders approach the projects as equals, is crucial for successful projects.
CLEOPATRA'22: 3rd International Workshop on Cross-lingual Event-centric Open Analytics
Authors: Elena Demidova, Sherzod Hakimov, Jane Winters, Marko Tadic
Venue: WWW
Year: 2022

The 3rd International Workshop on Cross-lingual Event-centric Open Analytics (CLEOPATRA 2022) is co-located with The Web Conference (WWW) and held on the 25th of April, 20221. Modern society faces an unprecedented number of events that impact countries, communities and economies around the globe across language, country and community borders. Recent examples include sudden or unexpected events such as terrorist attacks and political shake-ups such as Brexit, events related to the ongoing COVID-19 pandemic, as well as longer ongoing and evolving topics such as the migration crisis in Europe that regularly spawn events of global importance affecting local communities. These developments result in a vast amount of event-centric, multilingual information available from heterogeneous sources on the Web, on the Web of Data, within Knowledge Graphs, in social media, inside Web archives and in news sources. Such event-centric information differs across sources, languages and communities, potentially reflecting community-specific aspects, opinions, sentiments and bias. The theme of the workshop includes a variety of interdisciplinary challenges related to analysis, interaction with and interpretation of vast amounts of event-centric textual, semantic and visual information in multiple languages originating from different communities. The goal of the interdisciplinary CLEOPATRA workshop is to bring together researchers and practitioners from the fields of Semantic Web, the Web, NLP, IR, Human Computation, Visual Analytics and Digital Humanities to discuss and evaluate methods and solutions for effective and efficient analytics of event-centric multilingual information spread across heterogeneous sources. This will support the delivery of analytical results in ways meaningful to users, helping them to cross language barriers and better understand event representations and their context in other languages. The workshop features advanced methods for extracting event-centric information, multi-modal geo-localisation, cross-lingual sentiment detection and causality detection, NLP tools for under-resourced languages and applications of these methods to digital humanities research. Furthermore, the workshop introduces selected tools developed by researchers working in the CLEOPATRA ITN - a Marie Skłodowska-Curie Innovative Training Network. We would like to take this opportunity to sincerely thank the authors and presenters for their inspiring contributions to the workshop. Our sincere thanks are due to the program committee members for reviewing the submissions and ensuring the high quality of our workshop program. We also thank Manolis Koubarakis for his keynote talk in the workshop. We are also very grateful to the organisers of The Web Conference 2022, and particularly the Workshops Chairs, Nathalie Hernandez and Preslav Nakov, for their support with the workshop organisation.
Reproducibility and Replicability of Web Measurement Studies
Authors: Nurullah Demir, Matteo Große-Kampmann, Tobias Urban, Christian Wressnegger, Thorsten Holz, Norbert Pohlmann
Venue: WWW
Year: 2022

Web measurement studies can shed light on not yet fully understood phenomena and thus are essential for analyzing how the modern Web works. This often requires building new and adjusting existing crawling setups, which has led to a wide variety of analysis tools for different (but related) aspects. If these efforts are not sufficiently documented, the reproducibility and replicability of the measurements may suffer—two properties that are crucial to sustainable research. In this paper, we survey 117 recent research papers to derive best practices for Web-based measurement studies and specify criteria that need to be met in practice. When applying these criteria to the surveyed papers, we find that the experimental setup and other aspects essential to reproducing and replicating results are often missing. We underline the criticality of this finding by performing a large-scale Web measurement study on 4.5 million pages with 24 different measurement setups to demonstrate the influence of the individual criteria. Our experiments show that slight differences in the experimental setup directly affect the overall results and must be documented accurately and carefully.
Responsible AI: From Principles To Action: Keynote Talk
Authors: Virginia Dignum
Venue: WWW
Year: 2022

Virginia Dignum has given a Keynote Talk at The ACM Web Conference 2022 on Thursday 28th April 2022. This paper provides a summary of the topics she addressed during her talk.
Context, Prioritization, and Unexpectedness: Factors Influencing User Attitudes About Infographic and Comic Consent
Authors: Xengie Doan, Annika Selzer, Arianna Rossi, Maria Wilhelmina Botes, Gabriele Lenzini
Venue: WWW
Year: 2022

Being asked to agree to data disclosure is a ubiquitous experience in digital services - yet it is rare to encounter a well-designed consent experience. Considering the momentum for a European data space where personal information easily flows across organizations, sectors, and nations, solving the thorny issue of ”how to get consent right” cannot be postponed any further. In this paper, we describe the first findings from a study based on 24 semi-structured interviews investigating participants’ expectations and opinions toward a consent form redesigned as a comic and an infographic in a data-sharing scenario. We found that time, information prioritization, tone, and audience fit are crucial when individuals are invited to disclose their information and the infographic is a better fit in biomedical scenarios.
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Authors: Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li
Venue: WWW
Year: 2022

Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed networks in various web-based applications such as social recommendation and web search. Nevertheless, in high-stake decision-making scenarios such as online fraud detection, there is an increasing societal concern that GNNs could make discriminatory decisions towards certain demographic groups. Despite recent explorations on fair GNNs, these works are tailored for a specific GNN model. However, myriads of GNN variants have been proposed for different applications, and it is costly to fine-tune existing debiasing algorithms for each specific GNN architecture. Different from existing works that debias GNN models, we aim to debias the input attributed network to achieve fairer GNNs through feeding GNNs with less biased data. Specifically, we propose novel definitions and metrics to measure the bias in an attributed network, which leads to the optimization objective to mitigate bias. We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks. EDITS works in a model-agnostic manner, i.e., it is independent of any specific GNN. Experiments demonstrate the validity of the proposed bias metrics and the superiority of EDITS on both bias mitigation and utility maintenance. Open-source implementation: https://github.com/yushundong/EDITS.
Personalization and Relevance in NLG
Authors: Shiran Dudy
Venue: WWW
Year: 2022

Despite the recent advances in language modeling techniques, personalization remains a challenge for many NLP tasks. In this talk, we will explore personalization through several different lens to understand how we can make progress on this front, and emphasize why human-centered approach is a crucial part of the solution. First, I will challenge the ground-truth assumption in the context of user or situation sensitive language tasks. In other words, I will argue that the same question might be addressed differently by a system, depending on the user or the situation they are currently facing. Then we’ll discuss what are our user needs, and how can we design these tasks to produce useful and relevant responses, but also what potential harms we should be aware of working on personalization [5]. Next, we will look into personalization in the augmentative and alternative communication (AAC) world. Specifically, how through an icon-based language, individuals with compromised language abilities (that may arise due to Traumatic Brain injury (TBI) or Cerebral Palsy (CP)), we can accommodate their needs and what are the challenges in developing icon-based language models [3]. Third, in the process of personalization, models are expected to accommodate and adapt to the specific language and jargon spoken by the user. What are remaining challenges for deep learning architectures in the process of adapting user data or new domains [2, 4]. Finally, I will share work-in-progress where through Wizard-of-Oz (WoZ) experiments [1] we identify and learn useful actions of social conversational systems in classroom setting.
Anchor Prediction: A Topic Modeling Approach
Authors: Jean Dupuy, Adrien Guille, Julien Jacques
Venue: WWW
Year: 2022

Networks of documents connected by hyperlinks, such as Wikipedia, are ubiquitous. Hyperlinks are inserted by the authors to enrich the text and facilitate the navigation through the network. However, authors tend to insert only a fraction of the relevant hyperlinks, mainly because this is a time consuming task. In this paper we address an annotation, which we refer to as anchor prediction. Even though it is conceptually close to link prediction or entity linking, it is a different task that require developing a specific method to solve it. Given a source document and a target document, this task consists in automatically identifying anchors in the source document, i.e words or terms that should carry a hyperlink pointing towards the target document. We propose a contextualized relational topic model, CRTM, that models directed links between documents as a function of the local context of the anchor in the source document and the whole content of the target document. The model can be used to predict anchors in a source document, given the target document, without relying on a dictionary of previously seen mention or title, nor any external knowledge graph. Authors can benefit from CRTM, by letting it automatically suggest hyperlinks, given a new document and the set of target document to connect to. It can also benefit to readers, by dynamically inserting hyperlinks between the documents they’re reading. Experiments conducted on several Wikipedia corpora (in English, Italian and German) highlight the practical usefulness of anchor prediction and demonstrate the relevancy of our approach.