OpenRaiser/Intern-Atlas huggingface dataset:
A roadmap to integrated digital public health surveillance: the vision and the challenges
Authors: Patty Kostkova
Venue: WWW
Year: 2013

The exponentially increasing stream of real time big data produced by Web 2.0 Internet and mobile networks created radically new interdisciplinary challenges for public health and computer science. Traditional public health disease surveillance systems have to utilize the potential created by new situation-aware realtime signals from social media, mobile/sensor networks and citizens? participatory surveillance systems providing invaluable free realtime event-based signals for epidemic intelligence. However, rather than improving existing isolated systems, an integrated solution bringing together existing epidemic intelligence systems scanning news media (e.g., GPHIN, MedISys) with real-time social media intelligence (e.g., Twitter, participatory systems) is required to substantially improve and automate early warning, outbreak detection and preparedness operations. However, automatic monitoring and novel verification methods for these multichannel event-based real time signals has to be integrated with traditional case-based surveillance systems from microbiological laboratories and clinical reporting. Finally, the system needs effectively support coordination of epidemiological teams, risk communication with citizens and implementation of prevention measures.
Leveraging geographical metadata to improve search over social media
Authors: Alexander Kotov, Yu Wang, Eugene Agichtein
Venue: WWW
Year: 2013

We propose the methods for document, query and relevance model expansion that leverage geographical metadata provided by social media. In particular, we propose a geographically-aware extension of the LDA topic model and utilize the resulting topics and language models in our expansion methods. The proposed approach has been experimentally evaluated over a large sample of Twitter, demonstrating significant improvements in search accuracy over traditional (geographically-unaware) retrieval models.
Understanding latency variations of black box services
Authors: Darja Krushevskaja, Mark Sandler
Venue: WWW
Year: 2013

Data centers run many services that impact millions of users daily. In reality, the latency of each service varies from one request to another. Existing tools allow to monitor services for performance glitches or service disruptions, but typically they do not help understanding the variations in latency.
Diversified recommendation on graphs: pitfalls, measures, and algorithms
Authors: Onur Küçüktunç, Erik Saule, Kamer Kaya, Ümit V. Çatalyürek
Venue: WWW
Year: 2013

Result diversification has gained a lot of attention as a way to answer ambiguous queries and to tackle the redundancy problem in the results. In the last decade, diversification has been applied on or integrated into the process of PageRank- or eigenvector-based methods that run on various graphs, including social networks, collaboration networks in academia, web and product co-purchasing graphs. For these applications, the diversification problem is usually addressed as a bicriteria objective optimization problem of relevance and diversity. However, such an approach is questionable since a query-oblivious diversification algorithm that recommends most of its results without even considering the query may perform the best on these commonly used measures. In this paper, we show the deficiencies of popular evaluation techniques of diversification methods, and investigate multiple relevance and diversity measures to understand whether they have any correlations. Next, we propose a novel measure called expanded relevance which combines both relevance and diversity into a single function in order to measure the coverage of the relevant part of the graph. We also present a new greedy diversification algorithm called BestCoverage, which optimizes the expanded relevance of the result set with (1-1/e)-approximation. With a rigorous experimentation on graphs from various applications, we show that the proposed method is efficient and effective for many use cases.
HIGGINS: knowledge acquisition meets the crowds
Authors: Sarath Kumar Kondreddi, Peter Triantafillou, Gerhard Weikum
Venue: WWW
Year: 2013

We present HIGGINS, a system for Knowledge Acquisition (KA), placing emphasis on its architecture. The distinguishing characteristic and novelty of HIGGINS lies in its blending of two engines: an automated Information Extraction (IE) engine, aided by semantic resources and statistics, and a game-based Human Computing (HC) engine. We focus on KA from web pages and text sources and, in particular, on deriving relationships between entities. As a running application we utilize movie narratives, from which we wish to derive relationships among movie characters.
KONECT: the Koblenz network collection
Authors: Jérôme Kunegis
Venue: WWW
Year: 2013

We present the Koblenz Network Collection (KONECT), a project to collect network datasets in the areas of web science, network science and related areas, as well as provide tools for their analysis. In the cited areas, a surprisingly large number of very heterogeneous data can be modeled as networks and consequently, a unified representation of networks can be used to gain insight into many kinds of problems. Due to the emergence of the World Wide Web in the last decades many such datasets are now openly available. The KONECT project thus has the goal of collecting many diverse network datasets from the Web, and providing a way for their systematic study. The main parts of KONECT are (1) a collection of over 160 network datasets, consisting of directed, undirected, unipartite, bipartite, weighted, unweighted, signed and temporal networks collected from the Web, (2) a Matlab toolbox for network analysis and (3) a website giving a compact overview the various computed statistics and plots. In this paper, we describe KONECT's taxonomy of networks datasets, give an overview of the datasets included, review the supported statistics and plots, and briefly discuss KONECT's role in the area of web science and network science.
Ranking method specialized for content descriptions of classical music
Authors: Taku Kuribayashi, Yasuhito Asano, Masatoshi Yoshikawa
Venue: WWW
Year: 2013

In this paper, we propose novel ranking methods of effectively finding content descriptions of classical music compositions. In addition to rather naive methods using technical term frequency and latent Dirichlet allocation(LDA), we proposed a novel classification of web pages about classical music and used the characteristics of the classification for our method of search by labeled LDA(L-LDA). The experimental results showed our method performed well at finding content descriptions of classical music compositions.
Crowdsourcing MapReduce: JSMapReduce
Authors: Philipp Langhans, Christoph Wieser, François Bry
Venue: WWW
Year: 2013

JSMapReduce is an implementation of MapReduce which exploits the computing power available in the computers of the users of a web platform by giving tasks to the JavaScript engines of their web browsers. This article describes the implementation of JSMapReduce exploiting HTML 5 features, the heuristics it uses for distributing tasks to workers, and reports on an experimental evaluation of JSMapReduce.
Creating 3rd generation web APIs with hydra
Authors: Markus Lanthaler
Venue: WWW
Year: 2013

In this paper we describe a novel approach to build hypermedia-driven Web APIs based on Linked Data technologies such as JSON-LD. We also present the result of implementing a first prototype featuring both a RESTful Web API and a generic API client. To the best of our knowledge, no comparable integrated system to develop Linked Data-based APIs exists.
Model your application domain, not your JSON structures
Authors: Markus Lanthaler, Christian Gütl
Venue: WWW
Year: 2013

Creating truly RESTful Web APIs is still more an art than a science. Developers have to struggle with a number of complex design decisions because concrete guidelines and processes are missing. Consequently, often it is decided to implement the simplest solution which is, most of the time, to rely on out-of-band contracts between the client and the server. Instead of properly modeling the application domain, all the effort is put in the design of proprietary JSON structures and URLs. This then forms the base for the contract which is communicated in natural-language (with all its ambiguity) to client developers. Since it is the server who owns the contract it may be changed at any point, which, more often than not, results in broken clients. In this position paper, we discuss some of the challenges and choices that need to be made when designing RESTful Web APIs. In particular, we compare how contracts are supposed to be established and how they are defined in practice. We illustrate the problems that are the cause of these divergences. As a first step to address these issues we describe and motivate an alternative, domain-driven approach to design Web APIs.
A click model for time-sensitive queries
Authors: Seung Eun Lee, Dongug Kim
Venue: WWW
Year: 2013

User behavior on search results pages provides a clue about the query intent and the relevance of documents. To incorporate this information into search rankings, a variety of click modeling techniques have been proposed so far and now they are widely used in commercial search engines. For time-sensitive queries, however, applying click models can degrade the search relevance because the best document in the past may not be the current best answer. To address this problem, it is required to detect a time point, a turning point, where the search intent for a given query changes and to reflect it in click models. In this work, we devised a method to detect the turning point of a query from its search volume history. The proposed click model is designed to take only user behavior observed after the turning points. We applied our model in a commercial search engine and evaluated its relevance.
Fast anomaly detection despite the duplicates
Authors: Jay Yoon Lee, U Kang, Danai Koutra, Christos Faloutsos
Venue: WWW
Year: 2013

Given a large cloud of multi-dimensional points, and an off-the shelf outlier detection method, why does it take a week to finish? After careful analysis, we discovered that duplicate points create subtle issues, that the literature has ignored: if dmax is the multiplicity of the most over-plotted point, typical algorithms are quadratic on dmax. We propose several ways to eliminate the problem; we report wall-clock times and our time savings; and we show that our methods give either exact results, or highly accurate approximate ones.
Hidden view game: designing human computation games to update maps and street views
Authors: Jongin Lee, John Kim, KwanHong Lee
Venue: WWW
Year: 2013

Although the Web has abundant information, it does not necessarily contain the latest, most recently updated information. In particular, interactive map websites and the accompanying street view applications often contain information that is a few years old and are somewhat outdated because street views can change quickly. In this work, we propose Hidden View - a human computation mobile game that enables the updating of maps and street views with the latest information. The preliminary implementation of the game is described and some results collected from a sample user study are presented. This work is the first step towards leveraging human computation and an individual's familiarity with different points-of-interest to keep maps and street views up to date.
Fria: fast and robust instance alignment
Authors: Sanghoon Lee, Jongwuk Lee, Seung-won Hwang
Venue: WWW
Year: 2013

This paper proposes Fria, a fast and robust instance alignment framework across two independently built knowledge bases (KBs). Our objective is two-fold: (1) to design an effective instance similarity measure and (2) to build a fast and robust alignment framework. Specifically, Fria consists of two-phases. Fria first achieves high-precision alignment for seed matches which have strong evidence for aligning. To obtain high-recall alignment, Fria then divides non-matched instances according to the types identified from seeds, and gives additional chances to the same-typed instances to be matched. Experimental results show that Fria is fast and robust, by achieving comparable accuracy to state-of-the-arts and a 10-times speed up.
Finding news curators in twitter
Authors: Janette Lehmann, Carlos Castillo, Mounia Lalmas, Ethan Zuckerman
Venue: WWW
Year: 2013

Users interact with online news in many ways, one of them being sharing content through online social networking sites such as Twitter. There is a small but important group of users that devote a substantial amount of effort and care to this activity. These users monitor a large variety of sources on a topic or around a story, carefully select interesting material on this topic, and disseminate it to an interested audience ranging from thousands to millions. These users are news curators, and are the main subject of study of this paper. We adopt the perspective of a journalist or news editor who wants to discover news curators among the audience engaged with a news site.
Second screen interaction: an approach to infer tv watcher's interest using 3d head pose estimation
Authors: Julien Leroy, François Rocca, Matei Mancas, Bernard Gosselin
Venue: WWW
Year: 2013

In this paper, we present our "work-in-progress" approach to implicitly track user interaction and infer the interest a user can have for TV media. The aim is to identify moments of attentive focus, noninvasively and continuously, to dynamicaly improve the user profile by detecting which annotated media have drawn the user attention. Our method is based on the detection and estimation of face pose in 3D using a consumer depth camera. This allows us to determine when a user is or not looking at his television. This study is realized in the scenario of second screen interaction (tablet, smartphone), a behavior that has become common for spectators. We present our progress on the system and its integration in the LinkedTV project.
Enriching media fragments with named entities for video classification
Authors: Yunjia Li, Giuseppe Rizzo, José Luis Redondo García, Raphaël Troncy, Mike Wald, Gary B. Wills
Venue: WWW
Year: 2013

With the steady increase of videos published on media sharing platforms such as Dailymotion and YouTube, more and more efforts are spent to automatically annotate and organize these videos. In this paper, we propose a framework for classifying video items using both textual features such as named entities extracted from subtitles, and temporal features such as the duration of the media fragments where particular entities are spotted. We implement four automatic machine learning algorithms for multiclass classification problems, namely Logistic Regression (LG), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). We study the temporal distribution patterns of named entities extracted from 805 Dailymotion videos. The results show that the best performance using the entity distribution is obtained with KNN (overall accuracy of 46.58%) while the best performance using the temporal distribution of named entities for each type is obtained with SVM (overall accuracy of 43.60%). We conclude that this approach is promising for automatically classifying online videos.
Link prediction in social networks based on hypergraph
Authors: Dong Li, Zhiming Xu, Sheng Li, Xin Sun
Venue: WWW
Year: 2013

In recent years, online social networks have undergone a significant growth and attracted much attention. In these online social networks, link prediction is a critical task that not only offers insights into the factors behind creation of individual social relationship but also plays an essential role in the whole network growth. In this paper, we propose a novel link prediction method based on hypergraph. In contrast with conventional methods that using ordinary graph, we model the social network as a hypergraph, which can fully capture all types of objects and either the pair wise or high-order relations among these objects in the network. Then the link prediction task is formulated as a ranking problem on this hypergraph. Experimental results on Sina-Weibo dataset have demonstrated the effectiveness of our methods.
Link recommendation for promoting information diffusion in social networks
Authors: Dong Li, Zhiming Xu, Sheng Li, Xin Sun, Anika Gupta, Katia P. Sycara
Venue: WWW
Year: 2013

Online social networks mainly have two functions: social interaction and information diffusion. Most of current link recommendation researches only focus on strengthening the social interaction function, but ignore the problem of how to enhance the information diffusion function. For solving this problem, this paper introduces the concept of user diffusion degree and proposes the algorithm for calculating it, then combines it with traditional recommendation methods for reranking recommended links. Experimental results on Email dataset and Amazon dataset under Independent Cascade Model and Linear Threshold Model show that our method noticeably outperforms the traditional methods in terms of promoting information diffusion.
Collaborative filtering meets next check-in location prediction
Authors: Defu Lian, Vincent Wenchen Zheng, Xing Xie
Venue: WWW
Year: 2013

With the increasing popularity of Location-based Social Networks, a vast amount of location check-ins have been accumulated. Though location prediction in terms of check-ins has been recently studied, the phenomena that users often check in novel locations has not been addressed. To this end, in this paper, we leveraged collaborative filtering techniques for check-in location prediction and proposed a short- and long-term preference model. We extensively evaluated it on two large-scale check-in datasets from Gowalla and Dianping with 6M and 1M check-ins, respectively, and showed that the proposed model can outperform the competing baselines.
Text vs. images: on the viability of social media to assess earthquake damage
Authors: Yuan Liang, James Caverlee, John Mander
Venue: WWW
Year: 2013

In this paper, we investigate the potential of social media to provide rapid insights into the location and extent of damage associated with two recent earthquakes - the 2011 Tohoku earthquake in Japan and the 2011 Christchurch earthquake in New Zealand. Concretely, we (i) assess and model the spatial coverage of social media; and (ii) study the density and dynamics of social media in the aftermath of these two earthquakes. We examine the difference between text tweets and media tweets (containing links to images and videos), and investigate tweet density, re-tweet density, and user tweeting count to estimate the epicenter and to model the intensity attenuation of each earthquake. We find that media tweets provide more valuable location information, and that the relationship between social media activity vs. loss/damage attenuation suggests that social media following a catastrophic event can provide rapid insight into the extent of damage.
Rethinking the web as a personal archive
Authors: Siân E. Lindley, Catherine C. Marshall, Richard Banks, Abigail Sellen, Tim Regan
Venue: WWW
Year: 2013

In recent years the Web has evolved substantially, transforming from a place where we primarily find information to a place where we also leave, share and keep it. This presents a fresh set of challenges for the management of personal information, which include how to underpin greater awareness and more control over digital belongings and other personally meaningful content that is hosted online. In the study reported here, we follow up on research that suggests a sense of ownership and control can be reinforced by federating online content as a virtual, single store; we do this by conducting interviews with 14 individuals about their Web-based content. Participants were asked to give the researchers a tour of online content that is personally meaningful to them; to perform a search for themselves in order to uncover additional content; and to respond to a series of design envisionments. We examine whether there is any value in an integrated personal archive that would automatically update and serve firstly, as a source of information regarding the content within it (e.g. where it is stored, who has the rights to it), and secondly, as a resource for crafting personal artefacts such as scrapbooks, CVs and gifts for others. Our analysis leads us to reject the concept of a single archive. Instead, we present a framework of five different types of online content, each of which has separate implications for personal information management.
SoCo: a social network aided context-aware recommender system
Authors: Xin Liu, Karl Aberer
Venue: WWW
Year: 2013

Contexts and social network information have been proven to be valuable information for building accurate recommender system. However, to the best of our knowledge, no existing works systematically combine diverse types of such information to further improve recommendation quality. In this paper, we propose SoCo, a novel context-aware recommender system incorporating elaborately processed social network information. We handle contextual information by applying random decision trees to partition the original user-item-rating matrix such that the ratings with similar contexts are grouped. Matrix factorization is then employed to predict missing preference of a user for an item using the partitioned matrix. In order to incorporate social network information, we introduce an additional social regularization term to the matrix factorization objective function to infer a user's preference for an item by learning opinions from his/her friends who are expected to share similar tastes. A context-aware version of Pearson Correlation Coefficient is proposed to measure user similarity. Real datasets based experiments show that SoCo improves the performance (in terms of root mean square error) of the state-of-the-art context-aware recommender system and social recommendation model by 15.7% and 12.2% respectively.
Using stranger as sensors: temporal and geo-sensitive question answering via social media
Authors: Yefeng Liu, Todorka Alexandrova, Tatsuo Nakajima
Venue: WWW
Year: 2013

MoboQ is a location-based real-time social question answering service deployed in the field in China. Using MoboQ, people can ask temporal and geo-sensitive questions, such as how long is the line at a popular business right now, and then receive answers that crowdsourced from other users in a timely fashion. To obtain answers for questions, the system analyzes the live stream from public microblogging service Sina Weibo to identify people who are likely to currently be at the place that is associated with a question and sends them the unsolicited question through the microblogging service from which they were identified. MoboQ was deployed in China at the beginning of 2012, until October of the same year, it was used to ask 15,224 questions by 35,214 registered users, and it gathered 29,491 answers; 74.6% of the questions received at least one answer, 28% received a first response within 10 minutes, and 51% of the questions got first answer within 20 minutes. In total, 91% of the questions successfully found at least one answer candidate, and they were sent to 162,954 microblogging service users. We analyze the usage patterns and behaviors of the real-world end-users, discuss the lessons learned, and outline the future directions and possible applications that could be built on top of MoboQ.
Extracting the multilevel communities based on network structural and nonstructural information
Authors: Xin Liu, Tsuyoshi Murata, Ken Wakita
Venue: WWW
Year: 2013

Many real-world networks contain nonstructural information on nodes, such as the spatial coordinate of a location, profile of a person, or contents of a web page. In this paper, we propose Dist-Modularity, a unified modularity measure, which is useful in extracting the multilevel communities based on network structural and nonstructural information.