OpenRaiser/Intern-Atlas huggingface dataset:
Learning the Markov Order of Paths in Graphs
Authors: Luka V. Petrovic, Ingo Scholtes
Venue: WWW
Year: 2022

We address the problem of learning the Markov order in categorical sequences that represent paths in a network, i.e., sequences of variable lengths where transitions between states are constrained to a known graph. Such data pose challenges for standard Markov order detection methods and demand modeling techniques that explicitly account for the graph constraint. Adopting a multi-order modeling framework for paths, we develop a Bayesian learning technique that (i) detects the correct Markov order more reliably than a competing method based on the likelihood ratio test, (ii) requires considerably less data than methods using AIC or BIC, and (iii) is robust against partial knowledge of the underlying constraints. We further show that a recently published method that uses a likelihood ratio test exhibits a tendency to overfit the true Markov order of paths, which is not the case for our Bayesian technique. Our method is important for data scientists analyzing patterns in categorical sequence data that are subject to (partially) known constraints, e.g. click stream data or other behavioral data on the Web, information propagation in social networks, mobility trajectories, or pathway data in bioinformatics. Addressing the key challenge of model selection, our work is also relevant for the growing body of research that emphasizes the need for higher-order models in network analysis.
Going Down the Rabbit Hole: Characterizing the Long Tail of Wikipedia Reading Sessions
Authors: Tiziano Piccardi, Martin Gerlach, Robert West
Venue: WWW
Year: 2022

“Wiki rabbit holes” are informally defined as navigation paths followed by Wikipedia readers that lead them to long explorations, sometimes involving unexpected articles. Although wiki rabbit holes are a popular concept in Internet culture, our current understanding of their dynamics is based on anecdotal reports only. To bridge this gap, this paper provides a large-scale quantitative characterization of the navigation traces of readers who fell into a wiki rabbit hole. First, we represent user sessions as navigation trees and operationalize the concept of wiki rabbit holes based on the depth of these trees. Then, we characterize rabbit hole sessions in terms of structural patterns, time properties, and topical exploration. We find that article layout influences the structure of rabbit hole sessions and that the fraction of rabbit hole sessions is higher during the night. Moreover, readers are more likely to fall into a rabbit hole starting from articles about entertainment, sports, politics, and history. Finally, we observe that, on average, readers tend to stay focused on one topic by remaining in the semantic neighborhood of the first articles even during rabbit hole sessions. These findings contribute to our understanding of Wikipedia readers’ information needs and user behavior on the Web.
With One Voice: Composing a Travel Voice Assistant from Repurposed Models
Authors: Shachaf Poran, Gil Amsalem, Amit Beka, Dmitri Goldenberg
Venue: WWW
Year: 2022

Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for Booking.com. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional Machine-Learning product can be built from existing models.
Interactions in Information Spread
Authors: Gaël Poux-Médard
Venue: WWW
Year: 2022

Large quantities of data flow on the internet. When a user decides to help the spread of a piece of information (by retweeting, liking, posting content), most research works assumes she does so according to information’s content, publication date, the user’s position in the network, the platform used, etc. However, there is another aspect that has received little attention in the literature: the information interaction. The idea is that a user’s choice is partly conditioned by the previous pieces of information she has been exposed to. In this document, we review the works done on interaction modeling and underline several aspects of interactions that complicate their study. Then, we present an approach seemingly fit to answer those challenges and detail a dedicated interaction model based on it. We show our approach fits the problem better than existing methods, and present leads for future works. Throughout the text, we show that taking interactions into account improves our comprehension of information interaction processes in real-world datasets, and argue that this aspect of information spread is should not be neglected when modeling spreading processes.
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided Masking
Authors: Jiashu Pu, Jianshi Lin, Xiaoxi Mao, Jianrong Tao, Xudong Shen, Yue Shang, Runze Wu
Venue: WWW
Year: 2022

Players of online games generate rich behavioral data during gaming. Based on these data, game developers can build a range of data science applications, such as bot detection and social recommendation, to improve the gaming experience. However, the development of such applications requires data cleansing, training sample labeling, feature engineering, and model development, which makes the use of such applications in small and medium-sized game studios still uncommon. While acquiring supervised learning data is costly, unlabeled behavioral logs are often continuously and automatically generated in games. Thus we resort to unsupervised representation learning of player behavioral data to optimize intelligent services in games. Behavioral data has many unique properties, including semantic complexity, excessive length, etc. A worth noting property within raw player behavioral data is that a lot of it is task-irrelevant. For these data characteristics, we introduce a BPE-enhanced compression method and propose a novel adaptive masking strategy called Masking by Token Confidence (MTC) for the Masked Language Modeling (MLM) pre-training task. MTC is designed to increase the masking probabilities of task-relevant tokens. Experiments on four downstream tasks and successful deployment in a world-renowned Massively Multiplayer Online Role-Playing Game (MMORPG) prove the effectiveness of the MTC strategy1.
Informative Integrity Frictions in Social Networks
Authors: Lluis Garcia Pueyo, Samantha Guthrie, Bernardo Santana Schwarz, Baoxuan Xu
Venue: WWW
Year: 2022

Social media platforms such as Facebook and Twitter benefited from massive adoption in the last decade, and in turn facilitated the possibility of spreading harmful content, including false and misleading information. Some of these contents get massive distribution through user actions such as sharing, to a point that content removal or distribution reduction does not always stop its viral spread. At the same time, social media platforms efforts to implement solutions to preserve its integrity are typically not transparent, causing that users are not aware of any integrity intervention happening on the site. In this paper we present the rationale for adding what are now visible friction mechanisms to content share actions in the Facebook News Feed, its design and implementation challenges, and results obtained when applying them in the platform. We discuss effectiveness metrics for such interventions, and show their effects in terms of positive integrity outcomes, as well as in terms of bringing awareness to users about potentially making harmful content viral.
Generating Perturbation-based Explanations with Robustness to Out-of-Distribution Data
Authors: Luyu Qiu, Yi Yang, Caleb Chen Cao, Yueyuan Zheng, Hilary Hei Ting Ngai, Janet Hui-wen Hsiao, Lei Chen
Venue: WWW
Year: 2022

Perturbation-based techniques are promising for explaining black-box machine learning models due to their effectiveness and ease of implementation. However, prior works have faced the problem of Out-of-Distribution (OoD) — an artifact of randomly perturbed data becoming inconsistent with the original dataset, degrading the reliability of generated explanations, which is still under-explored according to our best knowledge. This work addresses the OoD issue by designing a simple yet effective module that can quantify the affinity between the perturbed data and the original dataset distribution. Specifically, we penalize the influences of unreliable OoD data for the perturbed samples by integrating the inlier scores and prediction results of the target models, thereby making the final explanations more robust. Our solution is shown to be compatible with the most popular perturbation-based XAI algorithms: RISE, OCCLUSION, and LIME. Extensive experiments confirmed that our methods exhibit superior performance in most cases with computational and cognitive metrics. In particular, we point out the degradation problem of RISE algorithm for the first time. With our design, the performance of RISE can be boosted significantly. Besides, our solution also resolves a fundamental problem with a faithfulness indicator, a commonly used evaluation metric of XAI algorithms that appears sensitive to the OoD issue.
SHACL and ShEx in the Wild: A Community Survey on Validating Shapes Generation and Adoption
Authors: Kashif Rabbani, Matteo Lissandrini, Katja Hose
Venue: WWW
Year: 2022

Knowledge Graphs (KGs) are widely used to represent heterogeneous domain knowledge on the Web and within organizations. Various methods exist to manage KGs and ensure the quality of their data. Among these, the Shapes Constraint Language (SHACL) and the Shapes Expression Language (ShEx) are the two state-of-the-art languages to define validating shapes for KGs. Since the usage of these constraint languages has recently increased, new needs arose. One such need is to enable the efficient generation of these shapes. Yet, since these languages are relatively new, we witness a lack of understanding of how they are effectively employed for existing KGs. Therefore, in this work, we answer How validating shapes are being generated and adopted? Our contribution is threefold. First, we conducted a community survey to analyze the needs of users (both from industry and academia) generating validating shapes. Then, we cross-referenced our results with an extensive survey of the existing tools and their features. Finally, we investigated how existing automatic shape extraction approaches work in practice on real, large KGs. Our analysis shows the need for developing semi-automatic methods that can help users generate shapes from large KGs.
Search Engines: From the Lab to the Engine Room, and Back: Keynote Talk
Authors: Prabhakar Raghavan
Venue: WWW
Year: 2022

Prabhakar Raghavan has given a Keynote Talk at The ACM Web Conference 2022 on Wednesday 27th April 2022. This paper provides a summary of the topics he addressed during his talk.
MarkovGNN: Graph Neural Networks on Markov Diffusion
Authors: Md. Khaledur Rahman, Abhigya Agrawal, Ariful Azad
Venue: WWW
Year: 2022

Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities. To learn from these networks, we develop MarkovGNN that captures the formation and evolution of communities directly in different convolutional layers. Unlike most Graph Neural Networks (GNNs) that consider a static graph at every layer, MarkovGNN generates different stochastic matrices using a Markov process and then uses these community-capturing matrices in different layers. MarkovGNN is a general approach that could be used with most existing GNNs. We experimentally show that MarkovGNN outperforms other GNNs for clustering, node classification, and visualization tasks. The source code of MarkovGNN is publicly available at https://github.com/HipGraph/MarkovGNN.
Anonymous Hyperlocal Communities: What do they talk about?
Authors: Jens Helge Reelfs, Oliver Hohlfeld, Niklas Henckell
Venue: WWW
Year: 2022

In this paper, we study what users talk about in a plethora of independent hyperlocal and anonymous online communities in a single country: Saudi Arabia (KSA). We base this perspective on performing a content classification of the Jodel network in the KSA. To do so, we first contribute a content classification schema that assesses both the intent (why) and the topic (what) of posts. We use the schema to label 15k randomly sampled posts and further classify the top 1k hashtags. We observe a rich set of benign (yet at times controversial in conservative regimes) intents and topics that dominantly address information requests, entertainment, or dating/flirting. By comparing two large cities (Riyadh and Jeddah), we further show that hyperlocality leads to shifts in topic popularity between local communities. By evaluating votes (content appreciation) and replies (reactions), we show that the communities react differently to different topics; e.g., entertaining posts are much appreciated through votes, receiving the least replies, while beliefs & politics receive similarly few replies but are controversially voted.
Exploring Cross-Lingual Transfer to Counteract Data Scarcity for Causality Detection
Authors: Sebastian Reimann, Sara Stymne
Venue: WWW
Year: 2022

Finding causal relations in text is an important task for many types of textual analysis. It is a challenging task, especially for the many languages with no or only little annotated training data available. To overcome this issue, we explore cross-lingual methods. Our main focus is on Swedish, for which we have a limited amount of data, and where we explore transfer from English and German. We also present additional results for German with English as a source language. We explore both a zero-shot setting without any target training data, and a few-shot setting with a small amount of target data. An additional challenge is the fact that the annotation schemes for the different data sets differ, and we discuss how we can address this issue. Moreover, we explore the impact of different types of sentence representations. We find that we have the best results for Swedish with German as a source language, for which we have a rather small but compatible data set. We are able to take advantage of a limited amount of noisy Swedish training data, but only if we balance its classes. In addition we find that the newer transformer-based representations can make better use of target language data, but that a representation based on recurrent neural networks is surprisingly competitive in the zero-shot setting.
Predicting Spatial Spread on Social Media
Authors: Rimjhim, Sourav Kumar Dandapat
Venue: WWW
Year: 2022

Understanding and prediction of spreading phenomena are vital for numerous applications. The massive availability of social network data provides a platform for studying spreading phenomena. Past works studying and predicting spreading phenomena have explored the spread in dimensions of time and volume, such as predicting total infected users, predicting popularity, predicting the time when content receives a threshold number of infected users. However, as the information spreads from user to user, it also spreads from location to location. In this paper, we attempt to predict the spread in the dimension of geographic space. In accordance with the past spreading prediction problems, we design our problem to predict the spatial spread at an early stage. For this, we utilized spatial features, social features, and emotion features. We feed these features into existing classification algorithms and evaluate on three datasets from Twitter.
Preferences on a Budget: Prioritizing Document Pairs when Crowdsourcing Relevance Judgments
Authors: Kevin Roitero, Alessandro Checco, Stefano Mizzaro, Gianluca Demartini
Venue: WWW
Year: 2022

In Information Retrieval (IR) evaluation, preference judgments are collected by presenting to the assessors a pair of documents and asking them to select which of the two, if any, is the most relevant. This is an alternative to the classic relevance judgment approach, in which human assessors judge the relevance of a single document on a scale; such an alternative allows to make relative rather than absolute judgments of relevance. While preference judgments are easier for human assessors to perform, the number of possible document pairs to be judged is usually so high that it makes it unfeasible to judge them all. Thus, following a similar idea to pooling strategies for single document relevance judgments where the goal is to sample the most useful documents to be judged, in this work we focus on analyzing alternative ways to sample document pairs to judge, in order to maximize the value of a fixed number of preference judgments that can feasibly be collected. Such value is defined as how well we can evaluate IR systems given a budget, that is, a fixed number of human preference judgments that may be collected. By relying on several datasets featuring relevance judgments gathered by means of experts and crowdsourcing, we experimentally compare alternative strategies to select document pairs and show how different strategies lead to different IR evaluation result quality levels. Our results show that, by using the appropriate procedure, it is possible to achieve good IR evaluation results with a limited number of preference judgments, thus confirming the feasibility of using preference judgments to create IR evaluation collections.
Modeling Position Bias Ranking for Streaming Media Services
Authors: Matteo Ruffini, Vito Bellini, Alexander Buchholz, Giuseppe Di Benedetto, Yannik Stein
Venue: WWW
Year: 2022

We tackle the problem of position bias estimation for streaming media services. Position bias is a widely studied topic in ranking literature and its impact on ranking quality is well understood. Although several methods exist to estimate position bias, their applicability to an industrial setting is limited, either because they require ad-hoc interventions that harm user experience, or because their learning accuracy is poor. In this paper, we present a novel position bias estimator that overcomes these limitations: it can be applied to streaming media services without manual interventions while delivering best in class estimation accuracy. We compare the proposed method against existing ones on real and synthetic data and illustrate its applicability to Amazon Music.
Biomedical Word Sense Disambiguation with Contextualized Representation Learning
Authors: Mozhgan Saeidi, Evangelos E. Milios, Norbert Zeh
Venue: WWW
Year: 2022

Representation learning is an important component in solving most Natural Language Processing (NLP) problems, including Word Sense Disambiguation (WSD). The WSD task tries to find the best meaning in a knowledge base for a word with multiple meanings (ambiguous word). WSD methods choose this best meaning based on the context, i.e., the words around the ambiguous word in the input text document. Thus, word representations may improve the effectiveness of the disambiguation models if they carry useful information from the context and the knowledge base. Most of the current representation learning approaches are that they are mostly trained on the general English text and are not domain specified. In this paper, we present a novel contextual-knowledge base aware sense representation method in the biomedical domain. The novelty in our representation is the integration of the knowledge base and the context. This representation lies in a space comparable to that of contextualized word vectors, thus allowing a word occurrence to be easily linked to its meaning by applying a simple nearest neighbor approach. Comparing our approach with state-of-the-art methods shows the effectiveness of our method in terms of text coherence.
Canonicalisation of SPARQL 1.1 Queries
Authors: Jaime Salas
Venue: WWW
Year: 2022

SPARQL is the standard query language for RDF as stated by the W3C. It is a highly expressive querying language that contains the standard operations based on set algebra, as well as navigational operations found in graph querying languages. Because of this, there are various ways to represent the same query, which may lead to redundancy in applications of the Semantic Web such as caching systems. We propose a canonicalisation method as a solution, such that all (monotone) congruent queries will have the same canonical form. Despite the theoretical complexity of this problem, our experiments show a good performance over real-world queries. Finally, we anticipate applications in caching, log analysis, query optimisation, etc.
A Study of Computational Reproducibility using URLs Linking to Open Access Datasets and Software
Authors: Lamia Salsabil, Jian Wu, Muntabir Hasan Choudhury, William A. Ingram, Edward A. Fox, Sarah Michele Rajtmajer, C. Lee Giles
Venue: WWW
Year: 2022

Datasets and software packages are considered important resources that can be used for replicating computational experiments. With the advocacy of Open Science and the growing interest of investigating reproducibility of scientific claims, including URLs linking to publicly available datasets and software packages has become an institutionalized part of research publications. In this preliminary study, we investigated the disciplinary dependency and chronological trends of including open access datasets and software (OADS) in electronic theses and dissertations (ETDs), based on a hybrid classifier called OADSClassifier, consisting of a heuristic and a supervised learning model. The classifier achieves the best F1 of 0.92. We found that the inclusion of OADS-URLs exhibited a strong disciplinary dependence and the fraction of ETDs containing OADS-URLs has been gradually increasing over the past 20 years. We developed and share a ground truth corpus consisting of 500 manually labeled sentences containing URLs from scientific papers. The dataset and source code are available at https://github.com/lamps-lab/oadsclassifier.