OpenRaiser/Intern-Atlas huggingface dataset:
Selective recrawling for object-level vertical search
Authors: Yaqian Zhou 0001, Mengjing Jiang, Qi Zhang 0001, Xuanjing Huang 0001, Lide Wu
Venue: WWW
Year: 2010

In this paper we propose a novel recrawling method based on navigation patterns called Selective Recrawling. The goal of selective recrawling is to automatically select page collections that have large coverage and little redundancy to a pre-defined vertical domain. It only requires several seed objects and can select a set of URL patterns to cover most objects. The selected set can be used to recrawl the web pages for quite a period of time and renewed periodically. Experiments on local event data show that our method can greatly reduce the downloading of web pages while keep the comparative object coverage.
Measurement and analysis of an online content voting network: a case study of Digg
Authors: Yingwu Zhu
Venue: WWW
Year: 2010

In online content voting networks, aggregate user activities (e.g., submitting and rating content) make high-quality content thrive through the unprecedented scale, high dynamics and divergent quality of user generated content (UGC). To better understand the nature and impact of online content voting networks, we have analyzed Digg, a popular online social news aggregator and rating website. Based on a large amount of data collected, we provide an in-depth study of Digg. We study structural properties of Digg social network, revealing some strikingly distinct properties such as low link symmetry and the power-law distribution of node outdegree with truncated tails. We explore impact of the social network on user digging activities, and investigate the issues of content promotion, content filtering, vote spam and content censorship, which are inherent to content rating networks. We also provide insight into design of content promotion algorithms and recommendation-assisted content discovery. Overall, we believe that the results presented in this paper are crucial in understanding online content rating networks.
Efficient web pages identification for entity resolution
Authors: Jia Zhu 0003, Gabriel Pui Cheong Fung, Xiaofang Zhou 0001
Venue: WWW
Year: 2010

Entity resolution (ER) is a problem that arises in many areas. In most of cases, it represents a task that multiple entities from different sources require to be identified if they refer to the same or different objects because there are not unique identifiers associated with them. In this paper, we propose a model using web pages identification to identify entities and merge those entities refer to one object together. We use a classical name disambiguation problem as case study and examine our model on a subset of digital library records as the first stage of our work. The favorable results indicated that our proposed approach is highly effective. © 2010 Copyright is held by the author/owner(s).
Anonymizing user profiles for personalized web search
Authors: Yun Zhu, Li Xiong 0001, Christopher Verdery
Venue: WWW
Year: 2010

We study the problem of anonymizing user profiles so that user privacy is sufficiently protected while the anonymized profiles are still effective in enabling personalized web search. We propose a Bayes-optimal privacy notion to bound the prior and posterior probability of associating a user with an individual term in the anonymized user profile set. We also propose a novel bundling technique that clusters user profiles into groups by taking into account the semantic relationships between the terms while satisfying the privacy constraint. We evaluate our approach through a set of preliminary experiments using real data demonstrating its feasibility and effectiveness.
Patch-based skin color detection and its application to pornography image filtering
Authors: Haiqiang Zuo, Weiming Hu 0004, Ou Wu 0001
Venue: WWW
Year: 2010

Along with the explosive growth of the World Wide Web, an immense industry for the production and consumption of pornography has grown. Though the censorship and legal restraints on pornography are discriminating in different historical, cultural and national contexts, selling pornography to minors is not allowed in most cases. Detecting human skin tone is of utmost importance in pornography image filtering algorithms. In this paper, we propose two patch-based skin color detection algorithms: regular patch and irregular patch skin color detection algorithms. On the basis of skin detection, we extract 31-dimensional features from the input image, and these features are fed into a random forest classifier. Our algorithm has been incorporated into an adult-content filtering infrastructure, and is now in active use for preventing minors from accessing pornographic images via mobile phones.
Faceted exploration of image search results
Authors: Roelof van Zwol, Börkur Sigurbjörnsson, Ramu Adapala, Lluis Garcia Pueyo, Abhinav Katiyar, Kaushal Kurapati, Mridul Muralidharan, Sudar Muthu, Vanessa Murdock 0001, Polly Ng, Anand Ramani, Anuj Sahai, Sriram Thiru Sathish, Hari Vasudev, Upendra Vuyyuru
Venue: WWW
Year: 2010

This paper describes MediaFaces, a system that enables faceted exploration of media collections. The system processes semi-structured information sources to extract objects and facets, e.g. the relationships between two objects. Next, we rank the facets based on a statistical analysis of image search query logs, and the tagging behaviour of users annotating photos in Flickr. For a given object of interest, we can then retrieve the top-k most relevant facets and present them to the user. The system is currently deployed in production by Yahoo!'s image search engine1. We present the system architecture, its main components, and the application of the system as part of the image search experience.
Social media: source of information or bunch of noise
Authors: Amr El Abaddi, Lars Backstrom, Soumen Chakrabarti, Alejandro Jaimes, Jure Leskovec, Andrew Tomkins
Venue: WWW
Year: 2011

Social media has witnessed an explosive growth in the past few years. Wikipedia has over 3.5 million pages with descriptions of entities. Flickr members have uploaded over 5 billion photos,You Tube has 35 hours of videos uploaded to the site each minute, and Twitter users generate 65 million tweets a day. While some forms of social media like Wikipedia clearly have valuable information embedded in them, the jury is still out on other forms like tweets, comments, and social network (e.g., Facebook) updates. Some of the key questions that the panel will debate include: Is there useful information in social media like tweets? How to extract structured records from unstructured user-generated content like reviews? How to sift through the vast amounts of social media and filter out the spam/offensive content? How to rank social media like blogs and comments based on relevance or importance?
Toward optimal vaccination strategies for probabilistic models
Authors: Zeinab Abbassi, Hoda Heidari
Venue: WWW
Year: 2011

Epidemic outbreaks such as the recent H1N1 influenza show how susceptible large communities are toward the spread of such outbreaks. The occurrence of a widespread disease transmission raises the question of vaccination strategies that are appropriate and close to optimal. The seemingly different problem of viruses disseminating through email networks, shares a common structure with disease epidemics. While it is not possible to vaccinate every individual during a virus outbreak, due to economic and logistical constraints, fortunately, we can leverage the structure and properties of face-to-face social networks to identify individuals whose vaccination would result in a lower number of infected people.
Identifying enrichment candidates in textbooks
Authors: Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi
Venue: WWW
Year: 2011

Many textbooks written in emerging countries lack clear and adequate coverage of important concepts. We propose a technological solution for algorithmically identifying those sections of a book that are not well written and could benefit from better exposition. We provide a decision model based on the syntactic complexity of writing and the dispersion of key concepts. The model parameters are learned using a tune set which is algorithmically generated using a versioned authoritative web resource as a proxy. We evaluate the proposed methodology over a corpus of Indian textbooks which demonstrates its effectiveness in identifying enrichment candidates.
Unified analysis of streaming news
Authors: Amr Ahmed, Qirong Ho, Jacob Eisenstein, Eric P. Xing, Alexander J. Smola, Choon Hui Teo
Venue: WWW
Year: 2011

News clustering, categorization and analysis are key components of any news portal. They require algorithms capable of dealing with dynamic data to cluster, interpret and to temporally aggregate news articles. These three tasks are often solved separately. In this paper we present a unified framework to group incoming news articles into temporary but tightly-focused storylines, to identify prevalent topics and key entities within these stories, and to reveal the temporal structure of stories as they evolve. We achieve this by building a hybrid clustering and topic model. To deal with the available wealth of data we build an efficient parallel inference algorithm by sequential Monte Carlo estimation. Time and memory costs are nearly constant in the length of the history, and the approach scales to hundreds of thousands of documents. We demonstrate the efficiency and accuracy on the publicly available TDT dataset and data of a major internet news site.
WWW 2011 invited tutorial overview: latent variable models on the internet
Authors: Amr Ahmed, Alexander J. Smola
Venue: WWW
Year: 2011

Graphical models are an effective tool for analyzing structured and relational data. In particular, they allow us to arrive at insights that are implicit, i.e. latent in the data. Dealing with such data on the internet poses a range of challenges. Firstly, the sheer size renders many well-known inference algorithms infeasible. Secondly, the problems arising on the internet do not always fit well into the known categories for latent variable inference such as Latent Dirichlet Allocation or clustering. In this tutorial we address a number of aspects. Firstly, we present a variety of applications ranging from general purpose document analysis, ideology detection, clustering of sequential data, and dynamic user profiling to recommender systems and data integration. Secondly we give an overview over a number of popular models such as mixture models, topic models, nonparametric variants of temporal dependence, and an integrated analysis and clustering approach, all of which can be used to solve a range of data analysis problems at hand. Thirdly, we present a range of sampling based algorithms for large scale distributed inference using multicore systems and clusters of workstations.
Two-stream indexing for spoken web search
Authors: Jitendra Ajmera, Anupam Joshi, Sougata Mukherjea, Nitendra Rajput, Shrey Sahay, Mayank Shrivastava, Kundan Srivastava
Venue: WWW
Year: 2011

This paper presents two-stream processing of audio to index the audio content for Spoken Web search. The first stream indexes the meta-data associated with a particular audio document. The meta-data is usually very sparse, but accurate. This therefore results in a high-precision, low-recall index. The second stream uses a novel language-independent speech recognition to generate text to be indexed. Owing to the multiple languages and the noise in user generated content on the Spoken Web, the speech recognition accuracy of such systems is not high, thus they result in a low-precision, high-recall index. The paper attempts to use these two complementary streams to generate a combined index to increase the precision-recall performance in audio content search.
Timestamp-based cache invalidation for search engines
Authors: Sadiye Alici, Ismail Sengör Altingövde, Rifat Ozcan, Berkant Barla Cambazoglu, Özgür Ulusoy
Venue: WWW
Year: 2011

We propose a new mechanism to predict stale queries in the result cache of a search engine. The novelty of our approach is in the use of timestamps in staleness predictions. We show that our approach incurs very little overhead on the system while its prediction accuracy is comparable to earlier works.
Towards automatic quality assurance in Wikipedia
Authors: Maik Anderka, Benno Stein, Nedim Lipka
Venue: WWW
Year: 2011

Featured articles in Wikipedia stand for high information quality, and it has been found interesting to researchers to analyze whether and how they can be distinguished from "ordinary" articles. Here we point out that article discrimination falls far short of writer support or automatic quality assurance: Featured articles are not identified, but are made. Following this motto we compile a comprehensive list of information quality flaws in Wikipedia, model them according to the latest state of the art, and devise one-class classification technology for their identification.
EP-SPARQL: a unified language for event processing and stream reasoning
Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
Venue: WWW
Year: 2011

Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams.
Workshop on online reputation: context, privacy, and reputation management
Authors: Judd Antin, Elizabeth F. Churchill, Bee-Chung Chen
Venue: WWW
Year: 2011

In this workshop we bring together researchers and practitioners from diverse disciplines to discuss the future of online reputation systems. Our goal is to combine social and technical perspectives to address three challenges: (1) the social challenges around reputation, privacy, and online identity, (2) the technical challenges around designing adaptable reputation systems which cater to users' privacy concerns, and (3) the user experience challenges around transparency and the design or reputation management tools.
we.b: the web of short urls
Authors: Demetres Antoniades, Iasonas Polakis, Georgios Kontaxis, Elias Athanasopoulos, Sotiris Ioannidis, Evangelos P. Markatos, Thomas Karagiannis
Venue: WWW
Year: 2011

Short URLs have become ubiquitous. Especially popular within social networking services, short URLs have seen a significant increase in their usage over the past years, mostly due to Twitter's restriction of message length to 140 characters. In this paper, we provide a first characterization on the usage of short URLs. Specifically, our goal is to examine the content short URLs point to, how they are published, their popularity and activity over time, as well as their potential impact on the performance of the web.
On the informativeness of cascade and intent-aware effectiveness measures
Authors: Azin Ashkan, Charles L. A. Clarke
Venue: WWW
Year: 2011

The Maximum Entropy Method provides one technique for validating search engine effectiveness measures. Under this method, the value of an effectiveness measure is used as a constraint to estimate the most likely distribution of relevant documents under a maximum entropy assumption. This inferred distribution may then be compared to the actual distribution to quantify the "informativeness" of the measure. The inferred distribution may also be used to estimate values for other effectiveness measures. Previous work focused on traditional effectiveness measures, such as average precision. In this paper, we extend the Maximum Entropy Method to the newer cascade and intent-aware effectiveness measures by considering the dependency of the documents ranked in a results list. These measures are intended to reflect the novelty and diversity of search results in addition to the traditional relevance. Our results indicate that intent-aware measures based on the cascade model are informative in terms of both inferring actual distribution and predicting the values of other retrieval measures.
OntoTrix: a hybrid visualization for populated ontologies
Authors: Benjamin Bach, Emmanuel Pietriga, Ilaria Liccardi, Gennady Legostaev
Venue: WWW
Year: 2011

Most Semantic Web data visualization tools structure the representation according to the concept definitions and interrelations that constitute the ontology's vocabulary. Instances are often treated as somewhat peripheral information, when considered at all. These instances, that populate ontologies, represent an essential part of any knowledge base, and are often orders of magnitude more numerous than the concept definitions that give them machine-processable meaning. We present a visualization technique designed to enable users to visualize large instance sets and the relations that connect them. This hybrid visualization uses both node-link and adjacency matrix representations of graphs to visualize different parts of the data depending on their semantic and local structural properties, exploiting ontological knowledge to drive the graph layout. The representation is embedded in an environment that features advanced interaction techniques for easy navigation, including support for smooth continuous zooming and coordinated views.
Network bucket testing
Authors: Lars Backstrom, Jon M. Kleinberg
Venue: WWW
Year: 2011

Bucket testing, also known as A/B testing, is a practice that is widely used by on-line sites with large audiences: in a simple version of the methodology, one evaluates a new feature on the site by exposing it to a very small fraction of the total user population and measuring its effect on this exposed group. For traditional uses of this technique, uniform independent sampling of the population is often enough to produce an exposed group that can serve as a statistical proxy for the full population. In on-line social network applications, however, one often wishes to perform a more complex test: evaluating a new social feature that will only produce an effect if a user and some number of his or her friends are exposed to it. In this case, independent uniform draws from the population on their own will be unlikely to produce a group that contains users together with their friends, and so the construction of the sample must take the network structure into account. This leads quickly to challenging combinatorial problems, since there is an inherent tension between producing enough correlation to select users and their friends, but also enough uniformity and independence that the selected group is a reasonable sample of the full population. Here we develop an algorithmic framework for bucket testing in a network that addresses these challenges. First we describe a novel walk-based sampling method for producing samples of nodes that are internally well-connected but also approximately uniform over the population. Then we show how a collection of multiple independent subgraphs constructed this way can yield reasonable samples for testing. We demonstrate the effectiveness of our algorithms through computational experiments on large portions of the Facebook network.
Distributed web retrieval
Authors: Ricardo Baeza-Yates
Venue: WWW
Year: 2011

In the ocean of Web data, Web search engines are the primary way to access content. As the data is on the order of petabytes, current search engines are very large centralized systems based on replicated clusters. Web data, however, is always evolving. The number of Web sites continues to grow rapidly (over 270 millions at the beginning of 2011) and there are currently more than 20 billion indexed pages. On the other hand, Internet users are above one billion and hundreds of million of queries are issued each day. In the near future, centralized systems are likely to become less effective against such a data-query load, thus suggesting the need of fully distributed search engines. Such engines need to maintain high quality answers, fast response time, high query throughput, high availability and scalability; in spite of network latency and scattered data. In this tutorial we present the architecture of current search engines and we explore the main challenges behind the design of all the processes of a distributed Web retrieval system crawling, indexing, and query processing.
The 1st temporal web analytics workshop (TWAW)
Authors: Ricardo Baeza-Yates, Julien Masanès, Marc Spaniol
Venue: WWW
Year: 2011

The objective of the 1st Temporal Web Analytics Workshop (TWAW) is to provide a venue for researchers of all domains (IE/IR, Web mining etc.) where the temporal dimension opens up an entirely new range of challenges and possibilities. The workshop's ambition is to help shaping a community of interest on the research challenges and possibilities resulting from the introduction of the time dimension in Web analysis. The maturity of the Web, the emergence of large scale repositories of Web material, makes this very timely and a growing sets of research and services (recorded future1, truthy2 launched just in the last months) are emerging that have this focus in common. Having a dedicated workshop will help, we believe, to take a rich and cross-domain approach to this new research challenge with a strong focus on the temporal dimension.
Addressing the RDFa publishing bottleneck
Authors: Xi Bai
Venue: WWW
Year: 2011

In the more dynamic environments emerging from ad hoc and peer-to-peer networks, our research has explored the extent to which Web-based knowledge sharing as well as community formation require automation to understand human-readable content in a more distributed manner. RDFa is a syntactic format which can leverage this issue by allowing machine-readable data to be easily integrated into XHTML Web pages. Although there is a growing number of tools and techniques for generating and distilling RDFa, comparatively little work has been carried out on publishing existing RDF data sets as an XHTML+RDFa serialization. This paper proposes a generic approach to integrating RDF data into Web pages using the concept of automatically discovered "topic nodes". RDFa² is a proof-of-concept implementation of this approach and provides an on-line service assisting users in generating and personalizing pages with RDFa. We provide experimental results that support the viability of our approach to generating Web documents such as FOAF-based online profiles as well as RDF vocabularies with little user intervention.