OpenRaiser/Intern-Atlas huggingface dataset:
Two New Gestures to Zoom: Enhancing Online Maps Services
Authors: Alessio Bellino
Venue: WWW
Year: 2015

Online services such as Google Maps or Open Street Maps allow the exploration of maps on smartphones and tablets. The gestures used are the pinch to adjust the zoom level and the drag to move the map. In this paper, two new gestures to adjust the zoom level of maps are presented. Both gestures with slight differences allow the identification of a target area to zoom, which is enlarged automatically up to cover the whole map container. The proposed gestures are added to the traditional ones (drag, pinch and flick) without any overlap. Therefore, users do not need to change their regular practices. They have just two more options to control the zoom level. One of the most relevant and appreciated advantages has to do with the gesture for smartphones (Tap&Tap): this allows users to control the zoom level with just one hand. The traditional pinch gesture, instead, needs two hands. According to the test results on new gestures in comparison with the traditional pinch, 30% of time is saved on tablets (Two-Finger-Tap gesture) whereas 14% on smartphones (Tap&Tap gesture).
Champagne: A Web Tool for the Execution of Crowdsourcing Campaigns
Authors: Carlo Bernaschina, Ilio Catallo, Piero Fraternali, Davide Martinenghi, Marco Tagliasacchi
Venue: WWW
Year: 2015

We present Champagne, a web tool for the execution of crowdsourcing campaigns. Through Champagne, task requesters can model crowdsourcing campaigns as a sequence of choices regarding different, independent crowdsourcing design decisions. Such decisions include, e.g., the possibility of qualifying some workers as expert reviewers, or of combining different quality assurance techniques to be used during campaign execution. In this regard, a walkthrough example showcasing the capabilities of the platform is reported. Moreover, we show that our modular approach in the design of campaigns overcomes many of the limitations exposed by the major platforms available in the market.
Viral Misinformation: The Role of Homophily and Polarization
Authors: Alessandro Bessi, Fabio Petroni, Michela Del Vicario, Fabiana Zollo, Aris Anagnostopoulos, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi
Venue: WWW
Year: 2015

The spreading of unsubstantiated rumors on online social networks (OSN) either unintentionally or intentionally (e.g., for political reasons or even trolling) can have serious consequences such as in the recent case of rumors about Ebola causing disruption to health-care workers. Here we show that indicators aimed at quantifying information consumption patterns might provide important insights about the virality of false claims. In particular, we address the driving forces behind the popularity of contents by analyzing a sample of 1.2M Facebook Italian users consuming different (and opposite) types of information (science and conspiracy news). We show that users' engagement across different contents correlates with the number of friends having similar consumption patterns (homophily), indicating the area in the social network where certain types of contents are more likely to spread. Then, we test diffusion patterns on an external sample of 4,709 intentional satirical false claims showing that neither the presence of hubs (structural properties) nor the most active users (influencers) are prevalent in viral phenomena. Instead, we found out that in an environment where misinformation is pervasive, users' aggregation around shared beliefs may make the usual exposure to conspiracy stories (polarization) a determinant for the virality of false information.
Social Glass: A Platform for Urban Analytics and Decision-making Through Heterogeneous Social Data
Authors: Stefano Bocconi, Alessandro Bozzon, Achilleas Psyllidis, Christiaan Titos Bolivar, Geert-Jan Houben
Venue: WWW
Year: 2015

This demo presents Social Glass, a novel web-based platform that supports the analysis, valorisation, integration, and visualisation of large-scale and heterogeneous urban data in the domains of city planning and decision-making. The platform systematically combines publicly available social datasets from municipalities together with social media streams (e.g. Twitter, Instagram and Foursquare) and resources from knowledge repositories. It further enables the mapping of demographic information, human movement patterns, place popularity, traffic conditions, as well as citizens' and visitors' opinions and preferences with regard to specific venues in the city. Social Glass will be demonstrated through several real-world case studies, that exemplify the framework's conceptual properties, and its potential value as a solution for urban analytics and city-scale event monitoring and assessment.
Large-scale Network Analytics: Diffusion-based Computation of Distances and Geometric Centralities
Authors: Paolo Boldi
Venue: WWW
Year: 2015

Given a large complex network, which of its nodes are more central? This question emerged in many contexts (e.g., sociology, psychology and computer science), and gave rise to a large range of proposed centrality measures. Providing a sufficiently general and mathematically sound classification of these measures is challenging: on one hand, it requires that one can suggest some simple, basic properties that a centrality measure should exhibit; on the other hand, it calls for innovative algorithms that allow an efficient computation of these measures on large real networks. HyperBall is a recently proposed tool that accesses the graph in a semi-streaming fashion and is at the same time able to compute the distance distribution and to approximate all geometric (i.e., distance-based) centralities. It uses a very small amount of core memory, thanks to the application of HyperLogLog counters, and exhibits high, guaranteed accuracy.
Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google
Authors: Joseph Bonneau, Elie Bursztein, Ilan Caron, Rob Jackson, Mike Williamson
Venue: WWW
Year: 2015

We examine the first large real-world data set on personal knowledge question's security and memorability from their deployment at Google. Our analysis confirms that secret questions generally offer a security level that is far lower than user-chosen passwords. It turns out to be even lower than proxies such as the real distribution of surnames in the population would indicate. Surprisingly, we found that a significant cause of this insecurity is that users often don't answer truthfully. A user survey we conducted revealed that a significant fraction of users (37%) who admitted to providing fake answers did so in an attempt to make them "harder to guess" although on aggregate this behavior had the opposite effect as people "harden" their answers in the same and predictable way. On the usability side, we show that secret answers have surprisingly poor memorability despite the assumption that their reliability motivates their continued deployment. From millions of account recovery attempts we observed a significant fraction of users (e.g 40% of our English-speaking US users) were unable to recall their answers when needed. This is lower than the success rate of alternative recovery mechanisms such as SMS reset codes (over 80%). Comparing question strength and memorability reveals that the questions that are potentially the most secure (e.g what is your first phone number) are also the ones with the worst memorability. We conclude that it appears next to impossible to find secret questions that are both secure and memorable. Secret questions continue have some use when combined with other signals, but they should not be used alone and best practice should favor more reliable alternatives.
Constructing and Mining Web-Scale Knowledge Graphs: WWW 2015 Tutorial
Authors: Antoine Bordes, Evgeniy Gabrilovich
Venue: WWW
Year: 2015

Recent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, Google's Knowledge Graph, YAGO, Facebook's Entity Graph, and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of vertices and edges. In this tutorial, we will present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, available datasets, and open research challenges. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques.
Supporting Ethical Web Research: A New Research Ethics Review
Authors: Anne Bowser, Janice Y. Tsai
Venue: WWW
Year: 2015

Research ethics is an important and timely topic. In academia, federally regulated Institutional Review Boards (IRBs) protect participants of human subjects research, and offer researchers a mechanism to assess the ethical implications of their work. Industry research labs are not subject to the same requirements, and may lack processes for research ethics review. We describe the creation of a new ethics framework and a research ethics submission system (RESS) within Microsoft Research (MSR). This RESS is customized to the needs of web researchers. We describe our iterative development process, including our assessment of the current state of web research, developing a framework of methods based on a survey of 358 research papers; build and evaluate our system with 14 users to identify the benefits and pitfalls of full deployment; evaluate how our system matches with existing federal regulations; and, suggest next steps for supporting ethical web research.
Isaac Bloomberg Meets Michael Bloomberg: Better EntityDisambiguation for the News
Authors: Luka Bradesko, Janez Starc, Stefano Pacifico
Venue: WWW
Year: 2015

This paper shows the implementation and evaluation of the Entity Linking or Named Entity Disambiguation system used and developed at Bloomberg. In particular, we present and evaluate a methodology and a system that do not require the use of Wikipedia as a knowledge base or training corpus. We present how we built features for disambiguation algorithms from the Bloomberg News corpus, and how we employed them for both single-entity and joint-entity disambiguation into a Bloomberg proprietary knowledge base of people and companies. Experimental results show high quality in the disambiguation of the available annotated corpus.
An Explorative Approach for Crowdsourcing Tasks Design
Authors: Marco Brambilla, Stefano Ceri, Andrea Mauri, Riccardo Volonterio
Venue: WWW
Year: 2015

Crowdsourcing applications are becoming widespread; they cover very different scenarios, including opinion mining, multimedia data annotation, localised information gathering, marketing campaigns, expert response gathering, and so on. The quality of the outcome of these applications depends on different design parameters and constraints, and it is very hard to judge about their combined effects without doing some experiments; on the other hand, there are no experiences or guidelines that tell how to conduct experiments, and thus these are often conducted in an ad-hoc manner, typically through adjustments of an initial strategy that may converge to a parameter setting which is quite different from the best possible one. In this paper we propose a comparative, explorative approach for designing crowdsourcing tasks. The method consists of defining a representative set of execution strategies, then execute them on a small dataset, then collect quality measures for each candidate strategy, and finally decide the strategy to be used with the complete dataset.
Exceptional Texts On The Multilingual Web
Authors: Gavin Brelstaff, Francesca Chessa
Venue: WWW
Year: 2015

Great writers help keep a language efficient for discourse of all kinds. In doing so they produce exceptional texts which may defy Statistical Machine Translation by employing uncommon idiom. Such "turns of phrase" can enter into a Nation's collective memory and form the basis from which compassion and conviction are conveyed during important national discourse. Communities that unite across language barriers have no such robust basis for discourse. Here we describe a Multilingual Web prototype application that promotes appreciation of exceptional texts by non-native readers. The application allows dual column original/translation texts (in Open Office format) to be imported into the translator's browser, to be manually aligned for semantic correspondence, to be aligned with an audio reading, and then saved as HTML5 for subsequent presentation to non-native readers. We hope to provide a new way of experiencing exceptional texts (poetry, here) that transmits their significance without incurring extraneous distraction. We motivate, outline and illustrate our application in action.
Towards Government as a Social Machine
Authors: Vanilson Arruda Burégio, Kellyton Brito, Nelson Souto Rosa, Misael Neto, Vinicius Cardoso Garcia, Silvio Romero de Lemos Meira
Venue: WWW
Year: 2015

Government initiatives to open data to the public are becoming increasingly popular every day. The vast amount of data made available by government organizations yields interesting opportunities and challenges - both socially and technically. In this paper, we propose a social machine-oriented architecture as a way to extend the power of open data and create the basis to derive government as a social machine (Gov-SM). The proposed Gov-SM combines principles from existing architectural patterns and provides a platform of specialized APIs to enable the creation of several other social-technical systems on top of it. Based on some implementation experiences, we believe that deriving government as a social machine can, in more than one sense, collaborate to fully integrate users, developers and crowd in order to participate in and solve a multitude of governmental issues and policy.
Modelling Question Selection Behaviour in Online Communities
Authors: Grégoire Burel, Paul Mulholland, Yulan He, Harith Alani
Venue: WWW
Year: 2015

Value of online Question Answering (Q&A) communities is driven by the question-answering behaviour of its members. Finding the questions that members are willing to answer is therefore vital to the efficient operation of such communities. In this paper, we aim to identify the parameters that correlate with such behaviours. We train different models and construct effective predictions using various user, question and thread feature sets. We show that answering behaviour can be predicted with a high level of success.
Sentiment Analysis on Microblogs for Natural Disasters Management: a Study on the 2014 Genoa Floodings
Authors: Davide Buscaldi, Irazú Hernandez-Farias
Venue: WWW
Year: 2015

People use social networks for different communication purposes, for example to share their opinion on ongoing events. One way to exploit this common knowledge is by using Sentiment Analysis and Natural Language Processing in order to extract useful information. In this paper we present a SA approach applied to a set of tweets related to a recent natural disaster in Italy; our goal is to identify tweets that may provide useful information from a disaster management perspective.
Ontology Search: Finding the Right Ontologies on the Web
Authors: Anila Sahar Butt
Venue: WWW
Year: 2015

With the recent growth of Linked Data on the Web, there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on keyword queries. In this proposal we introduce the main challenges to find appropriate ontologies, and preliminary solutions to address these challenges. Our evaluation shows that the proposed solution performs significantly better than existing solutions on a benchmark ontology collection for the majority of the sample queries defined in the benchmark.
Smart Urban Planning Support through Web Data Science on Open and Enterprise Data
Authors: Gloria Re Calegari, Irene Celino
Venue: WWW
Year: 2015

Urban information abound today in open Web sources as well as in enterprise datasets. However, the maintenance and update of this wealth of information about cities comes at different costs: some datasets are automatically produced, while other sources require expensive workflows including human intervention. Regression techniques can be employed to predict a costly dataset from a set of cheaper information sources. In this paper we present our early experiments in predicting land use and demographics from heterogeneous open and enterprise datasets referring to the city of Milano. The results are encouraging, thus demonstrating that a data science approach leveraging diverse data can be actually worth for a smarter urban planning support.
ControVol: Let Yesterday's Data Catch Up with Today's Application Code
Authors: Thomas Cerqueus, Eduardo Cunha de Almeida
Venue: WWW
Year: 2015

In building software-as-a-service applications, a flexible development environment is key to shipping early and often. Therefore, schema-flexible data stores are becoming more and more popular. They can store data with heterogeneous structure, allowing for new releases to be pushed frequently, without having to migrate legacy data first. However, the current application code must continue to work with any legacy data that has already been persisted in production. To let legacy data structurally "catch up" with the latest application code, developers commonly employ object mapper libraries with life-cycle annotations. Yet when used without caution, they can cause runtime errors and even data loss. We present ControVol, an IDE plugin that detects evolutionary changes to the application code that are incompatible with legacy data. ControVol warns developers already at development time, and even suggests automatic fixes for lazily migrating legacy data when it is loaded into the application. Thus, ControVol ensures that the structure of legacy data can catch up with the structure expected by the latest software release.
Got Many Labels?: Deriving Topic Labels from Multiple Sources for Social Media Posts using Crowdsourcing and Ensemble Learning
Authors: Shuo Chang, Peng Dai, Jilin Chen, Ed Huai-hsin Chi
Venue: WWW
Year: 2015

Online search and item recommendation systems are often based on being able to correctly label items with topical keywords. Typically, topical labelers analyze the main text associated with the item, but social media posts are often multimedia in nature and contain contents beyond the main text. Topic labeling for social media posts is therefore an important open problem for supporting effective social media search and recommendation. In this work, we present a novel solution to this problem for Google+ posts, in which we integrated a number of different entity extractors and annotators, each responsible for a part of the post (e.g. text body, embedded picture, video, or web link). To account for the varying quality of different annotator outputs, we first utilized crowdsourcing to measure the accuracy of individual entity annotators, and then used supervised machine learning to combine different entity annotators based on their relative accuracy. Evaluating using a ground truth data set, we found that our approach substantially outperforms topic labels obtained from the main text, as well as naive combinations of the individual annotators. By accurately applying topic labels according to their relevance to social media posts, the results enables better search and item recommendation.
Dataset Descriptions for Optimizing Federated Querying
Authors: Angelos Charalambidis, Stasinos Konstantopoulos, Vangelis Karkaletsis
Venue: WWW
Year: 2015

Dataset description vocabularies focus on provenance, versioning, licensing, and similar metadata. VoID is a notable exception, providing some expressivity for describing subsets and their contents and can, to some extent, be used for discovering relevant resources and for optimizing querying. In this poster we describe an extension of VoID that provides the expressivity needed in order to support the query planning methods typically used in federated querying.
Browser Record and Replay as a Building Block for End-User Web Automation Tools
Authors: Sarah E. Chasins, Shaon Barman, Rastislav Bodík, Sumit Gulwani
Venue: WWW
Year: 2015

To build a programming by demonstration (PBD) web scraping tool for end users, one needs two central components: a list finder, and a record and replay tool. A list finder extracts logical tables from a webpage. A record and replay (R+R) system records a user's interactions with a webpage, and replays them programmatically. The research community has invested substantial work in list finding --- variously called wrapper induction, structured data extraction, and template detection. In contrast, researchers largely considered the browser R+R problem solved until recently, when webpage complexity and interactivity began to rise. We argue that the increase in interactivity necessitates the use of new, more robust R+R approaches, which will facilitate the PBD web tools of the future. Because robust R+R is difficult to build and understand, we argue that tool developers need an R+R layer that they can treat as a black box. We have designed an easy-to-use API that allows programmers to use and even customize R+R, without having to understand R+R internals. We have instantiated our API in Ringer, our robust R+R tool. We use the API to implement WebCombine, a PBD scraping tool. A WebCombine user demonstrates how to collect the first row of a relational dataset, and the tool collects all remaining rows. WebCombine uses the Ringer API to handle navigation between pages, enabling users to scrape from modern, interaction-heavy pages. We demonstrate WebCombine by collecting a 3,787,146 row dataset from Google Scholar that allows us to explore the relationship between researchers' years of experience and their papers' citation counts.
Dicer: A Framework for Controlled, Large-Scale Web Experiments
Authors: Sarah E. Chasins, Phitchaya Mangpo Phothilimthana
Venue: WWW
Year: 2015

As dynamic, complex, and non-deterministic webpages proliferate, running controlled web experiments on live webpages is becoming increasingly difficult. To compare algorithms that take webpages as inputs, an experimenter must worry about ever-changing webpages, and also about scalability. Because webpage contents are constantly changing, experimenters must intervene to hold webpages constant, in order to guarantee a fair comparison between algorithms. Because webpages are increasingly customized and diverse, experimenters must test web algorithms over thousands of webpages, and thus need to implement their experiments efficiently. Unfortunately, no existing testing frameworks have been designed for this type of experiment. We introduce Dicer, a framework for running large-scale controlled experiments on live webpages. Dicer's programming model allows experimenters to easily 1) control when to enforce a same-page guarantee and 2) parallelize test execution. The same-page guarantee ensures that all loads of a given URL produce the same response. The framework utilizes a specialized caching proxy server to enforce this guarantee. We evaluate tool on a dataset of 1,000 real webpages, and find it upholds the same-page guarantee with little overhead.