OpenRaiser/Intern-Atlas huggingface dataset:
Noise robust detection of the emergence and spread of topics on the web
Authors: Masahiro Inoue, Keishi Tajima
Venue: WWW
Year: 2012

As the same information appears on many Web pages, we often want to know which page is the first one that discussed it, or how the information has spread on the Web as time passes. In this paper, we develop two methods: a method of detecting the first page that discussed the given information, and a method of generating a graph showing how the number of pages discussing it has changed along the timeline. To extract such information, we need to determine which pages discuss the given topic, and also need to determine when these pages were created. For the former step, we design a metric for estimating inclusion degree between information and a page. For the latter step, we develop a technique of extracting creation timestamps on web pages. Although timestamp extraction is a crucial component in temporal Web analysis, no research has shown how to do it in detail. Both steps are, however, still error-prone. In order to improve noise elimination, we examine not only the properties of each page, but also temporal relationship between pages. If temporal relationship between some candidate page and other pages are unlikely in typical patterns of information spread on the Web, we eliminate the candidate page as a noise. Results of our experiments show that our methods achieve high precision and can be used for practical use.
The affects of task difficulty on medical searches
Authors: Anushia Inthiran, Saadat M. Alhashmi, Pervaiz K. Ahmed
Venue: WWW
Year: 2012

In this paper, we analyze medical searching behavior performed by a typical medical searcher. We broadly classify a typical medical searcher as: non-medical professionals or medical professionals. We use behavioral signals to study how task difficulty affects medical searching behavior. Using simulated scenarios, we gathered data from an exploratory survey of 180 search sessions performed by 60 participants. Our research study provides a deep understanding of how task difficulty affects medical search behavior. Non-medical professionals and medical professionals demonstrate similar search behavior when searching on an easy task. Longer queries, more time and more incomplete search sessions are observed for an easy task. However, they demonstrate different results evaluation behavior based on task difficulty.
Leveraging interlingual classification to improve web search
Authors: Jagadeesh Jagarlamudi, Paul N. Bennett, Krysta M. Svore
Venue: WWW
Year: 2012

In this paper we address the problem of improving accuracy of web search in a smaller, data-limited search market (search language) using behavioral data from a larger, data-rich market (assist language). Specifically, we use interlingual classification to infer the search language query's intent using the assist language click-through data. We use these improved estimates of query intent, along with the query intent based on the search language data, to compute features that encode the similarity between a search result (URL) and the query. These features are subsequently fed into the ranking model to improve the relevance ranking of the documents. Our experimental results on German and French languages show the effectiveness of using assist language behavioral data especially, when the search language queries have small click-through data.
Modeling click-through based word-pairs for web search
Authors: Jagadeesh Jagarlamudi, Jianfeng Gao
Venue: WWW
Year: 2012

Statistical translation models and latent semantic analysis (LSA) are two effective approaches to exploit click-through data for web search ranking. This paper presents two document ranking models that combine both approaches by explicitly modeling word-pairs. The first model, called PairModel, is a monolingual ranking model based on word pairs that are derived from click-through data. It maps queries and documents into a concept space spanned by these word pairs. The second model, called Bilingual Paired Topic Model (BPTM), uses bilingual word pairs and jointly models a bilingual query-document collection. This model maps queries and documents in multiple languages into a lower dimensional semantic subspace. Experimental results on web search task show that they significantly outperform the state-of-the-art baseline models, and the best result is obtained by interpolating PairModel and BPTM.
A dual-mode user interface for accessing 3D content on the world wide web
Authors: Jacek Jankowski, Stefan Decker
Venue: WWW
Year: 2012

The Web evolved from a text-based system to the current rich and interactive medium that supports images, 2D graphics, audio and video. The major media type that is still missing is 3D graphics. Although various approaches have been proposed (most notably VRML/X3D), they have not been widely adopted. One reason for the limited acceptance is the lack of 3D interaction techniques that are optimal for the hypertext-based Web interface. We present a novel strategy for accessing integrated information spaces, where hypertext and 3D graphics data are simultaneously available and linked. We introduce a user interface that has two modes between which a user can switch anytime: the driven by simple hypertext-based interactions "don't-make-me-think" mode, where a 3D scene is embedded in hypertext and the more immersive 3D "take-me-to-the-Wonderland" mode, which immerses the hypertextual annotations into the 3D scene. A user study is presented, which characterizes the user interface in terms of its efficiency and usability.
FoCUS: learning to crawl web forums
Authors: Jingtian Jiang, Nenghai Yu, Chin-Yew Lin
Venue: WWW
Year: 2012

In this paper, we present FoCUS (Forum Crawler Under Supervision), a supervised web-scale forum crawler. The goal of FoCUS is to only trawl relevant forum content from the web with minimal overhead. Forum threads contain information content that is the target of forum crawlers. Although forums have different layouts or styles and are powered by different forum software packages, they always have similar implicit navigation paths connected by specific URL types to lead users from entry pages to thread pages. Based on this observation, we reduce the web forum crawling problem to a URL type recognition problem and show how to learn accurate and effective regular expression patterns of implicit navigation paths from an automatically created training set using aggregated results from weak page type classifiers. Robust page type classifiers can be trained from as few as 5 annotated forums and applied to a large set of unseen forums. Our test results show that FoCUS achieved over 98% effectiveness and 97% coverage on a large set of test forums powered by over 150 different forum software packages.
Google image swirl: a large-scale content-based image visualization system
Authors: Yushi Jing, Henry A. Rowley, Jingbin Wang, David Tsai, Chuck Rosenberg, Michele Covell
Venue: WWW
Year: 2012

Web image retrieval systems, such as Google or Bing image search, present search results as a relevance-ordered list. Although alternative browsing models (e.g. results as clusters or hierarchies) have been proposed in the past, it remains to be seen whether such models can be applied to large-scale image search. This work presents Google Image Swirl, a large-scale, publicly available, hierarchical image browsing system by automatically group the search results based on visual and semantic similarity. This paper describes methods used to build such system and shares the findings from 2-years worth of user feedback and usage statistics.
Scalable search platform: improving pipelined query processing for distributed full-text retrieval
Authors: Simon Jonassen
Venue: WWW
Year: 2012

In theory, term-wise partitioned indexes may provide higher throughput than document-wise partitioned. In practice, term-wise partitioning shows lacking scalability with increasing collection size and intra-query parallelism, which leads to long query latency and poor performance at low query loads. In our work, we have developed several techniques to deal with these problems. Our current results show a significant improvement over the state-of-the-art approach on a small distributed IR system, and our next objective is to evaluate the scalability of the improved approach on a large system. In this paper, we describe the relation between our work and the problem of scalability, summarize the results, limitations and challenges of our current work, and outline directions for further research.
T@gz: intuitive and effortless categorization and sharing of email conversations
Authors: Parag Mulendra Joshi, Claudio Bartolini, Sven Graupner
Venue: WWW
Year: 2012

In this paper, we describe T@gz, a system designed for effortless and instantaneous sharing of enterprise knowledge through routine email communications and powerful harvesting of such enterprise information using text analytics techniques. T@gz is a system that enables dynamic, non-intrusive and effortless sharing of information within an enterprise and automatically harvests knowledge from such daily interactions. It also allows enterprise knowledge workers to easily subscribe to new information. It enables self organization of information in conversations while it carefully avoids requiring users to substantially change their usual work-flow of exchanging emails.
Full-text search in email archives using social evaluation, attached and linked resources
Authors: Vojtech Juhász
Venue: WWW
Year: 2012

Emails are important tools for communication and cooperation, they contain large amount of information and connections to knowledge and data sources. Because of this, it is very important to improve the efficiency of their processing. This paper describes an email search system which integrates full-text search with social search while processing also the attached and linked resources. The project described in this paper is still in progress. Due to this fact, some proposed parts of the system are not implemented and also not proven yet. The proposed equation for determining the social importance of an email has also to be tuned during the last phases of the development and the evaluation phase. The already implemented part of the system includes content extraction from the email messages, attached and linked resources and also the textual search and social relation extraction is implemented. The next phase of the development includes tuning of the social evaluation and it's integration with textual search.
Building a role search engine for social media
Authors: Vanesa Junquero-Trabado, David Dominguez-Sal
Venue: WWW
Year: 2012

A social role is a set of characteristics that describe the behavior of individuals and their interactions between them within a social context. In this paper, we describe the architecture of a search engine for detecting roles in a social network. Our approach, based on indexed clusters, gives the user the possibility to define the roles interactively during a search session and retrieve the users for that role in milliseconds. We found that role selection strategies based on selecting people deviating from the average standards provides flexible query expressions and high quality results.
Answering math queries with search engines
Authors: Shahab Kamali, Johnson Apacible, Yasaman Hosseinkashi
Venue: WWW
Year: 2012

Conventional search engines such as Bing and Google provide a user with a short answer to some queries as well as a ranked list of documents, in order to better meet her information needs. In this paper we study a class of such queries that we call math. Calculations (e.g. "12% of 24$ ", "square root of 120"), unit conversions (e.g. "convert 10 meter to feet"), and symbolic computations (e.g. "plot x^2+x+1") are examples of math queries. Among the queries that should be answered, math queries are special because of the infinite combinations of numbers and symbols, and rather few keywords that form them. Answering math queries must be done through real time computations rather than keyword searches or database look ups. The lack of a formal definition for the entire range of math queries makes it hard to automatically identify them all. We propose a novel approach for recognizing and classifying math queries using large scale search logs, and investigate its accuracy through empirical experiments and statistical analysis. It allows us to discover classes of math queries even if we do not know their structures in advance. It also helps to identify queries that are not math even though they might look like math queries.
Keeping keywords fresh: a BM25 variation for personalized keyword extraction
Authors: Margarita Karkali, Vassilis Plachouras, Constantinos Stefanatos, Michalis Vazirgiannis
Venue: WWW
Year: 2012

Keyword extraction from web pages is essential to various text mining tasks including contextual advertising, recommendation selection, user profiling and personalization. For example, extracted keywords in contextual advertising are used to match advertisements with the web page currently browsed by a user. Most of the keyword extraction methods mainly rely on the content of a single web page, ignoring the browsing history of a user, and hence, potentially leading to the same advertisements or recommendations. In this work we propose a new feature scoring algorithm for web page terms extraction that, assuming a recent browsing history per user, takes into account the freshness of keywords in the current page as means of shifting users interests. We propose BM25H, a variant of BM25 scoring function, implemented on the client-side, that takes into account the user browsing history and suggests keywords relevant to the currently browsed page, but also fresh with respect to the user's recent browsing history. In this way, for each web page we obtain a set of keywords, representing the time shifting interests of the user. BM25H avoids repetitions of keywords which may be simply domain specific stop-words, or may result in matching the same ads or similar recommendations. Our experimental results show that BM25H achieves more than 70% in precision at 20 extracted keywords (based on human blind evaluation) and outperforms our baselines (TF and BM25 scoring functions), while it succeeds in keeping extracted keywords fresh compared to recent user history.
Economics of BitTorrent communities
Authors: Ian A. Kash, John K. Lai, Haoqi Zhang, Aviv Zohar
Venue: WWW
Year: 2012

Over the years, private file-sharing communities built on the BitTorrent protocol have developed their own policies and mechanisms for motivating members to share content and contribute resources. By requiring members to maintain a minimum ratio between uploads and downloads, private communities effectively establish credit systems, and with them full-fledged economies. We report on a half-year-long measurement study of DIME -- a community for sharing live concert recordings -- that sheds light on the economic forces affecting users in such communities. A key observation is that while the download of files is priced only according to the size of the file, the rate of return for seeding new files is significantly greater than for seeding old files. We find via a natural experiment that users react to such differences in resale value by preferentially consuming older files during a 'free leech' period. We consider implications of these finding on a user's ability to earn credits and meet ratio enforcements, focusing in particular on the relationship between visitation frequency and wealth and on low bandwidth users. We then share details from an interview with DIME moderators, which highlights the goals of the community based on which we make suggestions for possible improvement.
Extraction of onomatopoeia used for foods from food reviews and its application to restaurant search
Authors: Ayumi Kato, Yusuke Fukazawa, Tomomasa Sato, Taketoshi Mori
Venue: WWW
Year: 2012

Onomatopoeia is widely used in food reviews about food or restaurants. In this paper, we propose and evaluate a method to extract onomatopoeia including unknown ones automatically from food reviews sites. From the evaluation result, we found that we can extract onomatopoeia for specific foods with more than 46% precision; we find 18 unknown onomatopoeia, i.e. not registered in an existing onomatopoeia dictionary, in 62 extracted onomatopoeia. In addition, we propose a system that can present the user with a list of onomatopoeia specific to a restaurant she is interested in. The evaluation results indicate that an intuitive restaurant search can be done via a list of onomatopoeia, and that they are helpful for selecting food or restaurants.
Identifying sentiments over N-gram
Authors: Noriaki Kawamae
Venue: WWW
Year: 2012

Our proposal, identifying sentiment over N-gram (ISN) focuses on both word order and phrases, and the interdependency between specific rating and corresponding sentiment in a text to detect subjective information.
Watch me playing, i am a professional: a first study on video game live streaming
Authors: Mehdi Kaytoue, Arlei Silva, Loïc Cerf, Wagner Meira Jr., Chedy Raïssi
Venue: WWW
Year: 2012

"Electronic-sport" (E-Sport) is now established as a new entertainment genre. More and more players enjoy streaming their games, which attract even more viewers. In fact, in a recent social study, casual players were found to prefer watching professional gamers rather than playing the game themselves. Within this context, advertising provides a significant source of revenue to the professional players, the casters (displaying other people's games) and the game streaming platforms. For this paper, we crawled, during more than 100 days, the most popular among such specialized platforms: Twitch.tv. Thanks to these gigabytes of data, we propose a first characterization of a new Web community, and we show, among other results, that the number of viewers of a streaming session evolves in a predictable way, that audience peaks of a game are explainable and that a Condorcet method can be used to sensibly rank the streamers by popularity. Last but not least, we hope that this paper will bring to light the study of E-Sport and its growing community. They indeed deserve the attention of industrial partners (for the large amount of money involved) and researchers (for interesting problems in social network dynamics, personalized recommendation, sentiment analysis, etc.).
MenuMiner: revealing the information architecture of large web sites by analyzing maximal cliques
Authors: Matthias Keller, Martin Nussbaumer
Venue: WWW
Year: 2012

The foundation of almost all web sites' information architecture is a hierarchical content organization. Thus information architects put much effort in designing taxonomies that structure the content in a comprehensible and sound way. The taxonomies are obvious to human users from the site's system of main and sub menus. But current methods of web structure mining are not able to extract these central aspects of the information architecture. This is because they cannot interpret the visual encoding to recognize menus and their rank as humans do. In this paper we show that a web site's main navigation system can not only be distinguished by visual features but also by certain structural characteristics of the HTML tree and the web graph. We have developed a reliable and scalable solution that solves the problem of extracting menus for mining the information architecture. The novel MenuMiner-algorithm allows retrieving the original content organization of large-scale web sites. These data are very valuable for many applications, e.g. the presentation of search results. In an experiment we applied the method for finding site boundaries within a large domain. The evaluation showed that the method reliably delivers menus and site boundaries where other current approaches fail.
StormRider: harnessing "storm" for social networks
Authors: Vaibhav Khadilkar, Murat Kantarcioglu, Bhavani Thuraisingham
Venue: WWW
Year: 2012

The focus of online social media providers today has shifted from "content generation" towards finding effective methodologies for "content storage, retrieval and analysis" in the presence of evolving networks. Towards this end, in this paper we present StormRider, a framework that uses existing cloud computing and semantic web technologies to provide application programmers with automated support for these tasks, thereby allowing a richer assortment of use cases to be implemented on the underlying evolving social networks.
Building reputation and trust using federated search and opinion mining
Authors: Somayeh Khatiban
Venue: WWW
Year: 2012

The term online reputation addresses trust relationships amongst agents in dynamic open systems. These can appear as ratings, recommendations, referrals and feedback. Several reputation models and rating aggregation algorithms have been proposed. However, finding a trusted entity on the web is still an issue as all reputation systems work individually. The aim of this project is to introduce a global reputation system that aggregates people’s opinions from different resources (e.g. e-commerce websites, and review) with the help federated search techniques. A sentiment analysis approach is subsequently used to extract high quality opinions and inform how to increase trust in the search result.
Towards network-aware service composition in the cloud
Authors: Adrian Klein, Fuyuki Ishikawa, Shinichi Honiden
Venue: WWW
Year: 2012

Service-Oriented Computing (SOC) enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. With the advent of Cloud Computing, both the number of such services and their distribution across the network are rising rapidly, increasing the impact of the network on the QoS of such compositions. Despite this, current approaches do not differentiate between the QoS of services themselves and the QoS of the network. Therefore, the computed latency differs substantially from the actual latency, resulting in suboptimal QoS for service compositions in the cloud. Thus, we propose a network-aware approach that handles the QoS of services and the QoS of the network independently. First, we build a network model in order to estimate the network latency between arbitrary services and potential users. Our selection algorithm then leverages this model to find compositions that will result in a low latency given an employed execution policy. In our evaluation, we show that our approach efficiently computes compositions with much lower latency than current approaches.
Towards decision support for a home care services platform
Authors: Jan-Willem van&apos, t Klooster, Catherine Combes, Bert-Jan van Beijnum
Venue: WWW
Year: 2012

It is believed that ICT-mediation for home care services increases patient empowerment, independency, self-efficacy and quality of life. Providing elderly people with tailored care services allows us to learn from patient data to predict future care needs. In this article, we demonstrate the contribution of machine learning to homecare services, using data collected by a home care services platform. As an actual case, we show how simulated medication compliance can be measured and modeled using clustering and regression techniques. The approach is validated using data from French nursing home databases. The results show that it is possible to classify situations in elderly healthcare, and schedule resource planning according to expected health problems.