OpenRaiser/Intern-Atlas huggingface dataset:
A visual workflow to explore the web of data for scholars
Authors: Anastasia Dimou, Laurens De Vocht, Mathias Van Compernolle, Erik Mannens, Peter Mechant, Rik Van de Walle
Venue: WWW
Year: 2014

As the Web evolves in an integrated and interlinked knowledge space thanks to the growing amount of published Linked Open Data, the need to find solutions that enable the scholars to discover, explore and analyse the underlying research data emerges. Scholars, typically non-expert technology users, lack of in-depth understanding of the underlying semantic technology which limits their ability to interpret and query the data. We present a visual workflow to connect scholars and scientific resources on the Web of Data. We allow scholars to move from exploratory analysis in academic social networks to exposing relations between these resources. We allow them to reveal experts in a particular field and discover relations in and beyond their research communities. This paper aims to evaluate the potential of such a visual workflow to be used by non-expert users to interact with the semantically enriched data and familiarize with the underlying dataset.
Modeling contextual agreement in preferences
Authors: Loc Do, Hady Wirawan Lauw
Venue: WWW
Year: 2014

Personalization, or customizing the experience of each individual user, is seen as a useful way to navigate the huge variety of choices on the Web today. A key tenet of personalization is the capacity to model user preferences. The paradigm has shifted from that of individual preferences, whereby we look at a user's past activities alone, to that of shared preferences, whereby we model the similarities in preferences between pairs of users (e.g., friends, people with similar interests). However, shared preferences are still too granular, because it assumes that a pair of users would share preferences across all items. We therefore postulate the need to pay attention to "context", which refers to the specific item on which the preferences between two users are to be estimated. In this paper, we propose a generative model for contextual agreement in preferences. For every triplet consisting of two users and an item, the model estimates both the prior probability of agreement between the two users, as well as the posterior probability of agreement with respect to the item at hand. The model parameters are estimated from ratings data. To extend the model to unseen ratings, we further propose several matrix factorization techniques focused on predicting agreement, rather than ratings. Experiments on real-life data show that our model yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.
How social is social tagging?
Authors: Stephan Doerfel, Daniel Zoller, Philipp Singer, Thomas Niebler, Andreas Hotho, Markus Strohmaier
Venue: WWW
Year: 2014

Social tagging systems have established themselves as an important part in today's web and have attracted the interest of our research community in a variety of investigations. This has led to several assumptions about tagging, such as that tagging systems exhibit a social component. In this work we overcome the previous absence of data for testing such an assumption. We thoroughly study social interaction, leveraging for the first time live log data gathered from the real-world public social tagging system \bibs. Our results indicate that sharing of resources constitutes an important and indeed social aspect of tagging.
Extracting and aggregating temporal events from text
Authors: Lars Döhling, Ulf Leser
Venue: WWW
Year: 2014

Finding reliable information about a given event from large and dynamic text collections is a topic of great interest. For instance, rescue teams and insurance companies are interested in concise facts about damages after disasters, which can be found in web blogs, newspaper articles, social networks etc. However, finding, extracting, and condensing specific facts is a highly complex undertaking: It requires identifying appropriate textual sources, recognizing relevant facts within the sources, and aggregating extracted facts into a condensed answer despite inconsistencies, uncertainty, and changes over time. In this paper, we present a three-step framework providing techniques and solutions for each of these problems. We tested the feasibility of extracting time-associated event facts using our framework in a comprehensive case study: gathering data on particular earthquakes from web data sources. Our results show that it is, under certain circumstances, possible to automatically obtain reliable and timely data on natural disasters from the web.
Online learning and linked data: lessons learned and best practices
Authors: John Domingue, Alexander Mikroyannidis, Stefan Dietze
Venue: WWW
Year: 2014

Following the latest developments in online learning and Linked Data, the scope of this tutorial will be two-fold: 1. New online learning methods will be taught for supporting the teaching of Linked Data. Additionally, the lessons learned and the best practices derived from designing and delivering a Linked Data curriculum by the EUCLID project will be discussed. 2. Ways in which Linked Data principles and technologies can be used to support online learning and create innovative educational services will be explained, based on the experience developed in the development of existing Linked Data applications for online learning. We will in particular rely on the data catalogue, use cases and applications considered by the LinkedUp project.
Who am I on twitter?: a cross-country comparison
Authors: Wei Dong, Minghui Qiu, Feida Zhu
Venue: WWW
Year: 2014

Users often manage which aspects of their personal identities to be manifested on social network sites (SNS). Thus, the content of personal information disclosed on users' profiles can be influenced by a number of factors, such as motivation of using SNS and privacy concerns, both of which may vary depending on where users reside in. In this study, we compared the content of 2800 United States (US) and Singapore (SG) Twitter users' bios on their profile pages. We found US Twitter users were far more likely to disclose personal information that may reveal their true identity than SG users. The between country difference remained after we took bio length and user activity level into account. The results provide important insights on future studies to understand users' privacy concern in different regions of the world.
DBLP-filter: effectively search on the DBLP bibliography
Authors: Jiang Du, Peiquan Jin, Lizhou Zheng, Shouhong Wan, Lihua Yue
Venue: WWW
Year: 2014

DBLP is a well-known online computer science bibliography. As nearly all important journals and conferences on computer science are tracked in DBLP, how to effectively search DBLP records has become a valuable topic for the computer science community. In this paper we present DBLP-Filter, a new DBLP search tool. The major features of DBLP-Filter are: (1) it provides new search options on concepts and literature importance; (2) it can maintain user profiles and can support user-area-aware search; (3) it provides the service of new literatures alert. Compared with the existing DBLP search tools, DBLP-Filter is more functional and also shows better effectiveness in terms of MAP and F-measure when tested under a set of randomly-selected queries.
Perceptron-based tagging of query boundaries for Chinese query segmentation
Authors: Jingfei Du, Yan Song, Chi-Ho Li
Venue: WWW
Year: 2014

Query boundaries carry useful information for query segmentation, especially when analyzing queries in a language with no space, e.g., Chinese. This paper presents our research on Chinese query segmentation via averaged perceptron to model query boundaries through an L-R tagging scheme on a large amount of unlabeled queries. Experimental results indicate that query boundaries are very informative and they significantly improve supervised Chinese query segmentation when labeled training data is very limited.
Relatedness measures between conferences in computer science: a preliminary study based on DBLP
Authors: Suhendry Effendy, Irvan Jahja, Roland H. C. Yap
Venue: WWW
Year: 2014

A large percentage of the research in computer science is published in conferences and workshops. We propose three methods which compute a "relatedness score" for conferences relative to a pivot conference, usually a top rated conference. We experiment with the DBLP bibliography to show that our relatedness ranking can be used to help understand the basis of conference reputation ratings, determine what conferences are related to an area and the classification of conferences into areas.
Reduce and aggregate: similarity ranking in multi-categorical bipartite graphs
Authors: Alessandro Epasto, Jon Feldman, Silvio Lattanzi, Stefano Leonardi, Vahab S. Mirrokni
Venue: WWW
Year: 2014

We study the problem of computing similarity rankings in large-scale multi-categorical bipartite graphs, where the two sides of the graph represent actors and items, and the items are partitioned into an arbitrary set of categories. The problem has several real-world applications, including identifying competing advertisers and suggesting related queries in an online advertising system or finding users with similar interests and suggesting content to them. In these settings, we are interested in computing on-the-fly rankings of similar actors, given an actor and an arbitrary subset of categories of interest. Two main challenges arise: First, the bipartite graphs are huge and often lopsided (e.g. the system might receive billions of queries while presenting only millions of advertisers). Second, the sheer number of possible combinations of categories prevents the pre-computation of the results for all of them. We present a novel algorithmic framework that addresses both issues for the computation of several graph-theoretical similarity measures, including # common neighbors, and Personalized PageRank. We show how to tackle the imbalance in the graphs to speed up the computation and provide efficient real-time algorithms for computing rankings for an arbitrary subset of categories. Finally, we show experimentally the accuracy of our approach with real-world data, using both public graphs and a very large dataset from Google AdWords.
Monitoring web browsing behavior with differential privacy
Authors: Liyue Fan, Luca Bonomi, Li Xiong, Vaidy S. Sunderam
Venue: WWW
Year: 2014

Monitoring web browsing behavior has benefited many data mining applications, such as top-K discovery and anomaly detection. However, releasing private user data to the greater public would concern web users about their privacy, especially after the incident of AOL search log release where anonymization was not correctly done. In this paper, we adopt differential privacy, a strong, provable privacy definition, and show that differentially private aggregates of web browsing activities can be released in real-time while preserving the utility of shared data. Our proposed algorithms utilize the rich correlation of the time series of aggregated data and adopt a state-space approach to estimate the underlying, true aggregates from the perturbed values by the differential privacy mechanism. We evaluate our algorithms with real-world web browsing data. Utility evaluations with three metrics demonstrate that the quality of the private, released data by our solutions closely resembles that of the original, unperturbed aggregates.
What makes a good biography?: multidimensional quality analysis based on wikipedia article feedback data
Authors: Lucie Flekova, Oliver Ferschke, Iryna Gurevych
Venue: WWW
Year: 2014

With more than 22 million articles, the largest collaborative knowledge resource never sleeps, experiencing several article edits every second. Over one fifth of these articles describes individual people, the majority of which are still alive. Such articles are, by their nature, prone to corruption and vandalism. Manual quality assurance by experts can barely cope with this massive amount of data. Can it be effectively replaced by feedback from the crowd? Can we provide meaningful support for quality assurance with automated text processing techniques? Which properties of the articles should then play a key role in the machine learning algorithms and why? In this paper, we study the user-perceived quality of Wikipedia articles based on a novel Wikipedia user feedback dataset. In contrast to previous work on quality assessment which mostly relied on judgements of active Wikipedia authors, we analyze ratings of ordinary Wikipedia users along four quality dimensions (Complete, Well written, Trustworthy and Objective). We first present an empirical analysis of the novel dataset with over 36 million Wikipedia article ratings. We then select a subset of biographical articles and perform classification experiments to predict their quality ratings along each of the dimensions, exploring multiple linguistic, surface and network properties of the rated articles. Additionally, we study the classification performance and differences for the biographies of living and dead people as well as those for men and women. We demonstrate the effectiveness of our approach by the F-scores of 0.94, 0.89, 0.73, and 0.73 for the dimensions Complete, Well written, Trustworthy, and Objective. Based on the results, we believe that the quality assessment of big textual data can be effectively supported by current text classification and language processing tools.
WikiWho: precise and efficient attribution of authorship of revisioned content
Authors: Fabian Flöck, Maribel Acosta
Venue: WWW
Year: 2014

Revisioned text content is present in numerous collaboration platforms on the Web, most notably Wikis. To track authorship of text tokens in such systems has many potential applications; the identification of main authors for licensing reasons or tracing collaborative writing patterns over time, to name some. In this context, two main challenges arise. First, it is critical for such an authorship tracking system to be precise in its attributions, to be reliable for further processing. Second, it has to run efficiently even on very large datasets, such as Wikipedia. As a solution, we propose a graph-based model to represent revisioned content and an algorithm over this model that tackles both issues effectively. We describe the optimal implementation and design choices when tuning it to a Wiki environment. We further present a gold standard of 240 tokens from English Wikipedia articles annotated with their origin. This gold standard was created manually and confirmed by multiple independent users of a crowdsourcing platform. It is the first gold standard of this kind and quality and our solution achieves an average of 95% precision on this data set. We also perform a first-ever precision evaluation of the state-of-the-art algorithm for the task, exceeding it by over 10% on average. Our approach outperforms the execution time of the state-of-the-art by one order of magnitude, as we demonstrate on a sample of over 240 English Wikipedia articles. We argue that the increased size of an optional materialization of our results by about 10% compared to the baseline is a favorable trade-off, given the large advantage in runtime performance.
De-anonymizing social graphs via node similarity
Authors: Hao Fu, Aston Zhang, Xing Xie
Venue: WWW
Year: 2014

Recently, a number of anonymization algorithms have been developed to protect the privacy of social graph data. However, in order to satisfy higher level of privacy requirements, it is sometimes impossible to maintain sufficient utility. Is it really easy to de-anonymize "lightly" anonymized social graphs? Here "light" anonymization algorithms stand for those algorithms that maintain higher data utility. To answer this question, we proposed a de-anonymization algorithm based on a node similarity measurement. Using the proposed algorithm, we evaluated the privacy risk of several "light" anonymization algorithms on real datasets.
Contextual insights
Authors: Ariel Fuxman, Patrick Pantel, Yuanhua Lv, Ashok K. Chandra, Pradeep Chilakamarri, Michael Gamon, David Hamilton, Bernhard Kohlmeier, Dhyanesh Narayanan, Evangelos E. Papalexakis, Bo Zhao
Venue: WWW
Year: 2014

In today's productivity environment, users are constantly researching topics while consuming or authoring content in applications such as e-readers, word processors, presentation programs, or social networks. However, none of these applications sufficiently enable users to do their research directly within the application. In fact, users typically have to switch to a browser and write a query on a search engine. Switching to a search engine is distracting and hurts productivity. Furthermore, the main problem is that the search engine is not aware of important user context such as the book that they are reading or the document they are authoring. To tackle this problem, we introduce the notion of contextual insights: providing users with information that is contextually relevant to the content that they are consuming or authoring. We then present Leibniz, a system that provides a solution for the contextual insights problem.
Pragmatic hypermedia: creating a generic, self-inflating API client for production use
Authors: Pete Gamache
Venue: WWW
Year: 2014

Hypermedia API design is a method of creating APIs using hyperlinks to represent and publish an API's functionality. Hypermedia-based APIs bring theoretical advantages over many other designs, including the possibility of self-updating, generic API client software. Such hypermedia API clients only lately have come to exist, and the existing hypermedia client space did not compare favorably to custom API client libraries, requiring somewhat tedious manual access to HTTP resources. Nonetheless, the limitations in creating a compelling hypermedia client were few.
Effective and effortless features for popularity prediction in microblogging network
Authors: Shuai Gao, Jun Ma, Zhumin Chen
Venue: WWW
Year: 2014

Predicting popularity of online contents is of remarkable practical value in various business and administrative applications. Existing studies mainly focus on finding the most effective features for prediction. However, some effective features, such as structural features which are extracted from the underlying user network, are hard to access. In this paper, we aim to identify features that are both effective and effortless (easy to obtain or compute). Experiments on Sina Weibo show the effectiveness and effortlessness of the temporal features and satisfying prediction performance can be obtained based on only the temporal features of first 10 retweets.
Describing and contextualizing events in TV news show
Authors: José Luis Redondo García, Laurens De Vocht, Raphaël Troncy, Erik Mannens, Rik Van de Walle
Venue: WWW
Year: 2014

Describing multimedia content in general and TV programs in particular is a hard problem. Relying on subtitles to extract named entities that can be used to index fragments of a program is a common method. However, this approach is limited to what is being said in a program and written in a subtitle, therefore lacking a broader context. Furthermore, this type of index is restricted to a flat list of entities. In this paper, we combine the power of non-structured documents with structured data coming from DBpedia to generate a much richer, context aware metadata of a TV program. We demonstrate that we can harvest a rich context by expanding an initial set of named entities detected in a TV fragment. We evaluate our approach on a TV news show.
GLASE-IRUKA: gaze feedback improves satisfaction in exploratory image search
Authors: Viktors Garkavijs, Rika Okamoto, Tetsuo Ishikawa, Mayumi Toshima, Noriko Kando
Venue: WWW
Year: 2014

We propose two methods for exploratory image search systems using gaze data for continuous learning of the users' interests and relevance calculation. The first system uses the fixation time over the images selected by gaze in the search results pages, whereas the second one utilizes the fixation time over the clicked images and fixations over the non-selected images on the results page. A user model is trained and continuously updated from the gaze input throughout the whole session in both systems. We conducted an experiment with 24 users, each performing four search tasks using the proposed systems and compared the results to a baseline system, which does not employ any gaze data. The Gaze feedback system users viewed 22.35% more images than the users of the baseline system. A high correlation between the number of saved images and the satisfaction with the results was observed in data collected from the users of a mouse feedback system enriched by gaze data. The results show that including the gaze data into the relevance calculation in both cases increases the degree of satisfaction with the search results compared with the baseline.
A semi-supervised method for opinion target extraction
Authors: Tao Ge, Wenjie Li, Zhifang Sui
Venue: WWW
Year: 2014

This paper proposes a semi-supervised self-learning method, which is based on a Naive Bayes classifier exploiting context features and PMI scores, to extract opinion targets. The experimental results indicate our bootstrapping framework is effective for this task and outperforms the state-of-the-art models on COAE2008 dataset2, especially in precision.
Cross domain communication in the web of things: a new context for the old problem
Authors: Nam Ky Giang, Minkeun Ha, Daeyoung Kim
Venue: WWW
Year: 2014

Cross domain communication has been a long-discussed subject in the field of web-based application, especially for any sort of mashups where a single web app combines resources from different locations. This issue becomes more important in the Web of Things context, where every physical resources are exposed to the Web and mashed up by other web applications. In this paper we demonstrate a use case in which cross domain communication is applied in the Web of Things using the HTML5 Cross Document Messaging API (HTML5CDM). In addition, we contribute an advanced implementation of HTML5CDM that brings RESTful communication model to HTML5CDM and supports better concurrent message exchange, which we believe will be of much benefit to web developers. In addition, a time/space evaluation that measures CPU and Memory usage for the developed HTML5CDM library is carried out and the results has proved our implementation's practicability.
Localized CAPTCHA testing on users and farms
Authors: Ekaterina Gladkikh, Kirill Nikolaev, Mikhail Nikitin
Venue: WWW
Year: 2014

The paper describes the experience of resisting the large-scale solving of CAPTCHA through the CAPTCHA-farms and presents the results of experimenting with different types of textual CAPTCHA on the farm worker and casual user crowds. Localization of CAPTCHA led to cutting twice the absolute volume of CAPTCHA parsing, but introducing the semantics into the test complicated it to casual users and was not found promising.
Legal and ethical considerations: step 1b in building a health web observatory
Authors: Marie Joan Kristine Gloria, John S. Erickson, Joanne S. Luciano, Dominic DiFranzo, Deborah L. McGuinness
Venue: WWW
Year: 2014

This paper explores the impact of health information technologies, including the Web, on society and advocates for the development of a Health Web Observatory (HWO) to collect, store and analyze new sources of health information. The paper begins with a high-level literature review from across domains to demonstrate the need for a multi-disciplinary pursuit when building web observatories. For as researchers in the social sciences and legal domains have highlighted, data carries assumptions of power, identity, governance, etc., which should not be overlooked. The paper then recommends example legal and ethical questions to consider when building any health web observatory. The goal is to insert social and regulatory concerns much earlier into the WO methodology