OpenRaiser/Intern-Atlas huggingface dataset:
I'll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application
Authors: Zhiyuan Lin, Tim Althoff, Jure Leskovec
Venue: WWW
Year: 2018

Mobile health applications that track activities, such as exercise, sleep, and diet, are becoming widely used. While these activity tracking applications have the potential to improve our health, user engagement and retention are critical factors for their success. However, long-term user engagement patterns in real-world activity tracking applications are not yet well understood. Here we study user engagement patterns within a mobile physical activity tracking application consisting of 115 million logged activities taken by over a million users over 31 months. Specifically, we show that over 75% of users return and re-engage with the application after prolonged periods of inactivity, no matter the duration of the inactivity. We find a surprising result that the re-engagement usage patterns resemble those of the start of the initial engagement period, rather than being a simple continuation of the end of the initial engagement period. This evidence points to a conceptual model of multiple lives of user engagement, extending the prevalent single life view of user activity. We demonstrate that these multiple lives occur because the users have a variety of different primary intents or goals for using the app. These primary intents are associated with how long each life lasts and how likely the user is to re-engage for a new life. We find evidence for users being more likely to stop using the app once they achieved their primary intent or goal (e.g., weight loss). However, these users might return once their original intent resurfaces (e.g., wanting to lose newly gained weight). We discuss implications of the multiple life paradigm and propose a novel prediction task of predicting the number of lives of a user. Based on insights developed in this work, including a marker of improved primary intent performance, our prediction models achieve 71% ROC AUC. Overall, our research has implications for modeling user re-engagement in health activity tracking applications and has consequences for how notifications, recommendations as well as gamification can be used to increase engagement.
Iterative Knowledge Extraction from Social Networks
Authors: Marco Brambilla, Stefano Ceri, Florian Daniel, Marco Di Giovanni, Andrea Mauri, Giorgia Ramponi
Venue: WWW
Year: 2018

Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds. as new seeds. In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge
Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content
Authors: Andrea Mauri, Achilleas Psyllidis, Alessandro Bozzon
Venue: WWW
Year: 2018

Having a thorough understanding of energy consumption behavior is an important element of sustainability studies. Traditional sources of information about energy consumption, such as smart meter devices and surveys, can be costly to deploy, may lack contextual information or have infrequent updates. In this paper, we examine the possibility of extracting energy consumption-related information from user-generated content. More specifically, we develop a pipeline that helps identify energy-related content in Twitter posts and classify it into four categories (dwelling, food, leisure, and mobility), according to the type of activity performed. We further demonstrate a web-based application--called Social Smart Meter--that implements the proposed pipeline and enables different stakeholders to gain an insight into daily energy consumption behavior, as well as showcase it in case studies involving several world cities.
Walking down a Different Path: Route Recommendation based on Visual and Facility based Diversity
Authors: Yihong Zhang, Panote Siriaraya, Yuanyuan Wang, Shoko Wakamiya, Yukiko Kawai, Adam Jatowt
Venue: WWW
Year: 2018

For a traveler to enjoy a trip in a city, one important factor is the diversity of sceneries and facilities along the route. Current navigation systems can provide the shortest route between two points, as well as scenic or safe routes. However, diversity is largely ignored in existing works. In this paper, we present a system that provides diversity-based route recommendation. It measures visual-based diversity and facility-based diversity with information extracted from publicly available data such as Google Street View images and FourSquare venues. As we will show, the current prototype system is able to provide diversity-based route recommendation for city areas in San Fransisco and Kyoto.
BigNet 2018 Chairs' Welcome & Organization
Authors: Jie Tang, Michalis Vazirgiannis, Yuxiao Dong, Fragkiskos D. Malliaros, Michael Cochez, Mayank Kejriwal, Achim Rettinger
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the 2018 International Workshop on Learning Representations for Big Networks ([email protected]). This is the third edition of the BigNet workshop series, following its inauguration at the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016) and the second edition at the 26th International World Wide Web Conference (WWW 2017). Recent years have witnessed the emergence of network representation learning research. Different from the feature engineering process in conventional analysis, network representation learning, also know as network embedding, aims to learn the latent low-dimensional representations for objects in networks, such as nodes, links, and groups. Its ultimate objective is to encode networks' structural properties into the latent representations, benefiting all existing network mining tasks, such as node classification, link prediction, community detection, etc. In the BigNet 2018 workshop, we aim to provide a forum for presenting the most recent advances in network representation learning to unearth rich knowledge.
Welcome on Behalf of the Wiki Workshop Chairs & Organization
Authors: Robert West, Leila Zia, Dario Taraborelli, Jure Leskovec
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the Wiki Workshop at the Web Conference 2018. The goal of this workshop is to bring together researchers exploring all aspects of Wikimedia projects such as Wikipedia, Wikidata, and Wikimedia Commons. With members of the Wikimedia Foundation's Research team on the organizing committee and with the experience of successful workshops in 2015, 2016, and 2017, we aim to continue facilitating a direct pathway for exchanging ideas between the organization that coordinates Wikimedia projects and the researchers interested in studying them. We received 17 paper submissions (and counting) from all around the world covering a broad range of topics. Our program committee evaluated them regarding relevance, quality, and novelty, selecting 8 to be presented as posters and published in the workshop proceedings, and numerous others for poster presentation only.
Processing Social Media Messages in Mass Emergency: Survey Summary
Authors: Muhammad Imran, Carlos Castillo, Fernando Diaz, Sarah Vieweg
Venue: WWW
Year: 2018

Millions of people use social media to share information during disasters and mass emergencies. Information available on social media, particularly in the early hours of an event when few other sources are available, can be extremely valuable for emergency responders and decision makers, helping them gain situational awareness and plan relief efforts. Processing social media content to obtain such information involves solving multiple challenges, including parsing brief and informal messages, handling information overload, and prioritizing different types of information. These challenges can be mapped to information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. This work highlights these challenges and presents state of the art computational techniques to deal with social media messages, focusing on their application to crisis scenarios.
Identifying Time Intervals for Knowledge Graph Facts
Authors: Dhruv Gupta, Klaus Berberich
Venue: WWW
Year: 2018

Knowledge graphs capture very little temporal information associated with facts. In this work, we address the problem of identifying time intervals of knowledge graph facts from large document collections annotated with temporal expressions. Prior approaches in this direction have leveraged limited metadata associated with documents in large collections (e.g., publication dates) or have limited techniques to model the uncertainty and dynamics of temporal expressions. Our approach to identify time intervals for time-sensitive facts in knowledge graphs leverages a time model that incorporates uncertainty and models them at different levels of granularity (i.e., day, month, and year). Evaluation on a temporal fact benchmark using two large news archives amounting to more than eleven million documents show the quality of our results.
Metadata vs. Ground-truth: A Myth behind the Evolution of Community Detection Methods
Authors: Tanmoy Chakraborty, Zhe Cui, Noseong Park
Venue: WWW
Year: 2018

A community detection (CD) method is usually evaluated by what extent it is able to discover the 'ground-truth' community structure of a network. A certain 'node-centric metadata' is used to define the ground-truth partition. However, nodes in real networks often have multiple metadata types (e.g., occupation, location); each can potentially form a ground-truth partition. Our experiment with 10 CD methods on 5 datasets (having multiple metadata-based ground-truth partitions) show that the metadata-based evaluation is misleading because there is no single CD method that can outperform others by detecting all types of metadata-based partitions. We further show that the community structure obtained from the CD methods is usually topologically stronger than any metadata-based partitions. Finally, we suggest a new task-based evaluation framework for CD methods and show that a certain type of CD methods is useful for a certain type of task.
Venice through the Lens of Instagram: A Visual Narrative of Tourism in Venice
Authors: Luca Rossi, Eric Boscaro, Andrea Torsello
Venue: WWW
Year: 2018

The last decade has seen a huge expansion in the use of social media to extract data about human behaviour. While metadata and textual information have taken the lion's share as data sources for social media analysis, geotagged image-based platforms represent an unprecedented and as yet almost untapped source of data to analyse human behaviour and characterise the physical space we live in. In this paper we investigate the use of Instagram photos to analyse tourism consumption. We take the city of Venice (Italy) as a case study and we collect a dataset of about 90k photos taken between January 2014 and December 2015. Using computer vision techniques, we build a supervised classifier which assigns each photo to one of six different categories. We then observe how the frequency and spatial distribution of these categories varies with time. This in turn allows us to confirm the existence of a number of touristic hotspots associated with different events, such as Venice Carnival and Biennale. Our analysis also uncovers the existence of touristic flows associated with these events, such as the Folklore Line that marks the path of tourists from "Santa Lucia" railway station to "San Marco" square during the Carnival period. Overall, our findings confirm the effectiveness of the proposed framework to investigate tourism consumption using Instagram data.
Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases
Authors: Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, Gerhard Weikum
Venue: WWW
Year: 2018

Translating natural language questions to semantic representations such as SPARQL is a core challenge in open-domain question answering over knowledge bases (KB-QA). Existing methods rely on a clear separation between an offline training phase, where a model is learned, and an online phase where this model is deployed. Two major shortcomings of such methods are that (i) they require access to a large annotated training set that is not always readily available and (ii) they fail on questions from before-unseen domains. To overcome these limitations, this paper presents NEQA, a continuous learning paradigm for KB-QA. Offline, NEQA automatically learns templates mapping syntactic structures to semantic ones from a small number of training question-answer pairs. Once deployed, continuous learning is triggered on cases where templates are insufficient. Using a semantic similarity function between questions and by judicious invocation of non-expert user feedback, NEQA learns new templates that capture previously-unseen syntactic structures. This way, NEQA gradually extends its template repository. NEQA periodically re-trains its underlying models, allowing it to adapt to the language used after deployment. Our experiments demonstrate NEQA's viability, with steady improvement in answering quality over time, and the ability to answer questions from new domains.
HARE: An Engine for Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing
Authors: Maribel Acosta, Elena Simperl, Fabian Flöck, Maria-Esther Vidal
Venue: WWW
Year: 2018

We propose HARE, a SPARQL query engine that encompasses human-machine query processing to augment the completeness of query answers. We empirically assessed the effectiveness of HARE on 50 SPARQL queries over DBpedia. Experimental results clearly show that our solution accurately enhances answer completeness.
Privacy and Efficiency Tradeoffs for Multiword Top K Search with Linear Additive Rank Scoring
Authors: Daniel Agun, Jinjin Shao, Shiyu Ji, Stefano Tessaro, Tao Yang
Venue: WWW
Year: 2018

This paper proposes a private ranking scheme with linear additive scoring for efficient top K keyword search on modest-sized cloud datasets. This scheme strikes for tradeoffs between privacy and efficiency by proposing single-round client-server collaboration with server-side partial ranking based on blinded feature weights with random masks. Client-side preprocessing includes query decomposition with chunked postings to facilitate earlier range intersection and fast access of server-side key-value stores. Server-side query processing deals with feature vector sparsity through optional feature matching and enables result filtering with query-dependent chunk-wide random masks for queries that yield too many matched documents. This paper provides details on indexing and run-time conjunctive query processing and presents an evaluation that assesses the accuracy, efficiency, and privacy tradeoffs of this scheme through five datasets with various sizes.
LocWeb2018 Chairs' Welcome & Organization
Authors: Dirk Ahlers, Erik Wilde, Rossano Schifanella, Jalal S. Alowibdi, Muhammad Zubair Shafiq
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the 8th International Workshop on Location and the Web (LocWeb2018) at WWW 2018. LocWeb 2018 will continue a successful workshop series at the intersection of location-based services and Web architecture. It focuses on Web-scale services and systems facilitating location-aware information access as well as on Spatial Social Behavior Analytics on the Web as part of social computing. The location topic is seen as a cross-cutting issue equally concerning information access, semantics and standards, social analysis and mining, and Web-scale systems and services. The workshop is an integrated venue where location and spatio-social aspects can be discussed in depth with an interested community. New application areas for Web architecture, such as the Internet of Things (IoT) and the Web of Things (WoT), will lead to increasingly rich and large sets of applications for which location is highly relevant as the connection to the physical world. Location has high importance in Web-based designs, and it continues to provide challenging research questions.
Workshop Chairs' Welcome
Authors: Eyhab Al-Masri, Marie-Christine Rousset
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Workshops. This year's workshops of WWW 2018 feature a number of co-located workshops that are intended to provide a forum for researchers and practitioners in Web technologies to discuss and exchange positions on current and emergent Web topics. We received forty proposals from all around the world covering a broad range of topics. We evaluated them regarding relevance, quality, and novelty selecting eighteen full-day workshops and ten half-day workshops. We also took into account the coverage of the different areas related to WWW as well as the potential audience, to schedule them in two consecutive days with the minimal audience interest overlap.
An Evaluation of Performance and Competition in Customer Services on Twitter: A UK Telecoms Case Study
Authors: Nabeel Albishry, Tom Crick, Theo Tryfonas, Tesleem Fagade
Venue: WWW
Year: 2018

With an increasing number of consumers using social media platforms to share both their satisfaction and displeasure about the products and services they use every day, organisations with a customer service focus are recognising the importance of rapid--and genuine--online engagement with their customers. In turn, consumers increasingly judge organisations on the quality of customer service and degree of responsiveness to online queries. This paper presents an extensible framework for evaluating direct engagements of customer service teams with customers on Twitter. Furthermore, this framework provides the capability to measure and analyse indirect engagement with industry sector rivals, especially their patterns, frequency and intensity. By applying graph analysis to these Twitter interactions, our framework generates various analytical measures and visual representations, exemplified through a case study based on seven major UK telecoms companies. With a dataset consisting of 15,000 tweets and 3,500 user profiles, the results provide sustained evidence for indirect engagements between business rivals, with customer queries acting as a trigger for intense competition between companies based in the same industry sub-domain.
Educational Migration from Russia to the Nordic Countries, China and the Middle East. Social Media Data
Authors: Daniel Alexandrov, Viktor Karepin, Ilya Musabirov, Daria Chuprina
Venue: WWW
Year: 2018

We use social media and WWW data to analyse international educational migration from Russia. We find substantial regional differences in migration patterns for three contrast directions: the Nordic countries, China and the Middle East. We built a model of migration flows with geographic distances to destination countries, various socio-demographic data and institutional characteristics of educational organisations.
MusicLynx: Exploring Music Through Artist Similarity Graphs
Authors: Alo Allik, Florian Thalmann, Mark B. Sandler
Venue: WWW
Year: 2018

MusicLynx is a web application for music discovery that enables users to explore an artist similarity graph constructed by linking together various open public data sources. It provides a multifaceted browsing platform that strives for an alternative, graph-based representation of artist connections to the grid-like conventions of traditional recommendation systems. Bipartite graph filtering of the Linked Data cloud, content-based music information retrieval, machine learning on crowd-sourced information and Semantic Web technologies are combined to analyze existing and create new categories of music artists through which they are connected. The categories can uncover similarities between artists who otherwise may not be immediately associated: for example, they may share ethnic background or nationality, common musical style or be signed to the same record label, come from the same geographic origin, share a fate or an affliction, or have made similar lifestyle choices. They may also prefer similar musical keys, instrumentation, rhythmic attributes, or even moods their music evokes. This demonstration is primarily meant to showcase the graph-based artist discovery interface of MusicLynx: how artists are connected through various categories, how the different graph filtering methods affect the topology and geometry of linked artists graphs, and ways in which users can connect to external services for additional content and information about objects of their interest.
CHIMP: Crowdsourcing Human Inputs for Mobile Phones
Authors: Mário Almeida, Muhammad Bilal, Alessandro Finamore, Ilias Leontiadis, Yan Grunenberger, Matteo Varvello, Jeremy Blackburn
Venue: WWW
Year: 2018

While developing mobile apps is becoming easier, testing and characterizing their behavior is still hard. On the one hand, the de facto testing tool, called "Monkey," scales well due to being based on random inputs, but fails to gather inputs useful in understanding things like user engagement and attention. On the other hand, gathering inputs and data from real users requires distributing instrumented apps, or even phones with pre-installed apps, an expensive and inherently unscaleable task. To address these limitations we present CHIMP, a system that integrates automated tools and large-scale crowdsourced inputs. CHIMP is different from previous approaches in that it runs apps in a virtualized mobile environment that thousands of users all over the world can access via a standard Web browser. CHIMP is thus able to gather the full range of real-user inputs, detailed run-time traces of apps, and network traffic. We thus describe CHIMP»s design and demonstrate the efficiency of our approach by testing thousands of apps via thousands of crowdsourced users. We calibrate CHIMP with a large-scale campaign to understand how users approach app testing tasks. Finally, we show how CHIMP can be used to improve both traditional app testing tasks, as well as more novel tasks such as building a traffic classifier on encrypted network flows.