OpenRaiser/Intern-Atlas huggingface dataset:
Potential networks, contagious communities, and understanding social network structure
Authors: Grant Schoenebeck
Venue: WWW
Year: 2013

In this paper we study how the network of agents adopting a particular technology relates to the structure of the underlying network over which the technology adoption spreads. We develop a model and show that the network of agents adopting a particular technology may have characteristics that differ significantly from the social network of agents over which the technology spreads. For example, the network induced by a cascade may have a heavy-tailed degree distribution even if the original network does not. This provides evidence that online social networks created by technology adoption over an underlying social network may look fundamentally different from social networks and indicates that using data from many online social networks may mislead us if we try to use it to directly infer the structure of social networks. Our results provide an alternate explanation for certain properties repeatedly observed in data sets, for example: heavy-tailed degree distribution, network densification, shrinking diameter, and network community profile. These properties could be caused by a sort of sampling bias rather than by attributes of the underlying social structure. By generating networks using cascades over traditional network models that do not themselves contain these properties, we can nevertheless reliably produce networks that contain all these properties. An opportunity for interesting future research is developing new methods that correctly infer underlying network structure from data about a network that is generated via a cascade spread over the underlying network.
Partitioning RDF exploiting workload information
Authors: Rebeca Schroeder, Raqueline R. M. Penteado, Carmem Satie Hara
Venue: WWW
Year: 2013

One approach to leverage scalable systems for RDF management is partitioning large datasets across distributed servers. In this paper we consider workload data, given in the form of query patterns and their frequencies, for determining how to partition RDF datasets. Our experimental study shows that our workload-aware method is an effective way to cluster related data and provides better query response times compared to an elementary fragmentation method.
Profile diversity in search and recommendation
Authors: Maximilien Servajean, Esther Pacitti, Sihem Amer-Yahia, Pascal Neveu
Venue: WWW
Year: 2013

We investigate profile diversity, a novel idea in searching scientic documents. Combining keyword relevance with popularity in a scoring function has been the subject of different forms of social relevance [2, 6, 9]. Content diversity has been thoroughly studied in search and advertising [4, 11], database queries [16, 5, 8], and recommendations [17, 10, 18]. We believe our work is the first to investigate profile diversity to address the problem of returning highly popular but too-focused documents. We show how to adapt Fagin's threshold-based algorithms to return the most relevant and most popular documents that satisfy content and profile diversities and run preliminary experiments on two benchmarks to validate our scoring function.
SemantEco: a next-generation web observatory
Authors: A. Patrice Seyed, Timothy Lebo, Evan W. Patton, Jamie P. McCusker, Deborah L. McGuinness
Venue: WWW
Year: 2013

A web observatory for empirical research of Web data benefits from software frameworks that are modular, has a clear underlying semantic model, and that includes metadata enabling a trace and inspection of the source data and justifications for derived datasets. We present SemantEco as an architecture that can serve as an exemplar abstraction for infrastructure design and metadata based on best practices in Semantic Web, Provenance, and Software Engineering, that can be employed in any Web Observatory, that may grow out of a community. We will describe how the SemantEco framework allows for searching, visualizing, and tracing a wide variety of data.
RepRank: reputation in a peer-to-peer online system
Authors: Zeqian Shen, Neel Sundaresan
Venue: WWW
Year: 2013

Peer-to-peer e-commerce networks exemplify online lemon markets. Trust is key to sustaining these networks. We present a reputation system named RepRank that approaches trust with an intuition that in the peer-to-peer e-commerce world consisting of buyers and sellers, good buyers are those who buy from good sellers, and good sellers are those from whom good buyers buy. We propagate trust and distrust in a network using this mutually recursive definition. We discuss the algorithms and present the evaluation results.
Quality-biased ranking for queries with commercial intent
Authors: Alexander Shishkin, Polina Zhinalieva, Kirill Nikolaev
Venue: WWW
Year: 2013

Modern search engines are good enough to answer popular commercial queries with mainly highly relevant documents. However, our experiments show that users behavior on such relevant commercial sites may differ from one to another web-site with the same relevance label. Thus search engines face the challenge of ranking results that are equally relevant from the perspective of the traditional relevance grading approach. To solve this problem we propose to consider additional facets of relevance, such as trustability, usability, design quality and the quality of service. In order to let a ranking algorithm take these facets in account, we proposed a number of features, capturing the quality of a web page along the proposed dimensions. We aggregated new facets into the single label, commercial relevance, that represents cumulative quality of the site. We extrapolated commercial relevance labels for the entire learning-to-rank dataset and used weighted sum of commercial and topical relevance instead of default relevance labels. For evaluating our method we created new DCG-like metrics and conducted off-line evaluation as well as on-line interleaving experiments demonstrating that a ranking algorithm taking the proposed facets of relevance into account is better aligned with user preferences.
Finding relevant missing references in learning courses
Authors: Patrick Siehndel, Ricardo Kawase, Asmelash Teka Hadgu, Eelco Herder
Venue: WWW
Year: 2013

Reference sites play an increasingly important role in learning processes. Teachers use these sites in order to identify topics that should be covered by a course or a lecture. Learners visit online encyclopedias and dictionaries to find alternative explanations of concepts, to learn more about a topic, or to better understand the context of a concept. Ideally, a course or lecture should cover all key concepts of the topic that it encompasses, but often time constraints prevent complete coverage. In this paper, we propose an approach to identify missing references and key concepts in a corpus of educational lectures. For this purpose, we link concepts in educational material to the organizational and linking structure of Wikipedia. Identifying missing resources enables learners to improve their understanding of a topic, and allows teachers to investigate whether their learning material covers all necessary concepts.
Classifying YouTube channels: a practical system
Authors: Vincent Simonet
Venue: WWW
Year: 2013

This paper presents a framework for categorizing channels of videos in a thematic taxonomy with high precision and coverage. The proposed approach consists of three main steps.First, videos are annotated by semantic entities describing their central topics. Second, semantic entities are mapped to categories using a combination of classifiers.Last, the categorization of channels is obtained by combining the results of both previous steps.
Computing semantic relatedness from human navigational paths on Wikipedia
Authors: Philipp Singer, Thomas Niebler, Markus Strohmaier, Andreas Hotho
Venue: WWW
Year: 2013

This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Our results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. We also show that we can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Our work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.
Linked data in crowdsourcing purposive social network
Authors: Priyanka Singh, Nigel Shadbolt
Venue: WWW
Year: 2013

Internet is an easy medium for people to collaborate and crowdsourcing is an efficient feature of social web where people with common interest and expertise come together to solve specific problems by collective thinking and create a community. It can also be used to filter out important information from large data, remove spams, and gamification techniques are used to reward the users for their contribution and keep a sustainable environment for the growth of the community. Semantic web technologies can be used to structure the community data so it can be combined, decentralized and be used across platform. Using such tools knowledge can be enhanced and easily discovered and merged together. This paper discusses the concept of a purposive social network where people with similar interest and varied expertise come together, use crowdsourcing technique to solve a common problem and build tools for common purpose. The StackOverflow website is chosen to study the purposive network, different network ties and roles of user is studied. Linked Data is used for name disambiguation of keywords and topics for easier search and discovery of experts in a field and provide useful information that is otherwise unavailable in the website.
A predictive model for advertiser value-per-click in sponsored search
Authors: Eric Sodomka, Sébastien Lahaie, Dustin Hillard
Venue: WWW
Year: 2013

Sponsored search is a form of online advertising where advertisers bid for placement next to search engine results for specific keywords. As search engines compete for the growing share of online ad spend, it becomes important for them to understand what keywords advertisers value most, and what characteristics of keywords drive value. In this paper we propose an approach to keyword value prediction that draws on advertiser bidding behavior across the terms and campaigns in an account. We provide original insights into the structure of sponsored search accounts that motivate the use of a hierarchical modeling strategy. We propose an economically meaningful loss function which allows us to implicitly fit a linear model for values given observables such as bids and click-through rates. The model draws on demographic and textual features of keywords and takes advantage of the hierarchical structure of sponsored search accounts. Its predictive quality is evaluated on several high-revenue and high-exposure advertising accounts on a major search engine. Besides the general evaluation of advertiser welfare, our approach has potential applications to keyword and bid suggestion.
Recommendation for online social feeds by exploiting user response behavior
Authors: Ping-Han Soh, Yu-Chieh Lin, Ming-Syan Chen
Venue: WWW
Year: 2013

In recent years, online social networks have been dramatically expanded. Active users spend hours communicating with each other via these networks such that an enormous amount of data is created every second. The tremendous amount of newly created information costs users much time to discover interesting messages from their online social feeds. The problem is even exacerbated if users access these networks via mobile devices. To assist users in discovering interesting messages efficiently, in this paper, we propose a new approach to recommend interesting messages for each user by exploiting the user's response behavior. We extract data from the most popular social network, and the experimental results show that the proposed approach is effective and efficient.
I know the shortened URLs you clicked on Twitter: inference attack using public click analytics and Twitter metadata
Authors: Jonghyuk Song, Sangho Lee, Jong Kim
Venue: WWW
Year: 2013

Twitter is a popular social network service for sharing messages among friends. Because Twitter restricts the length of messages, many Twitter users use URL shortening services, such as bit.ly and goo.gl, to share long URLs with friends. Some URL shortening services also provide click analytics of the shortened URLs, including the number of clicks, countries, platforms, browsers and referrers. To protect visitors' privacy, they do not reveal identifying information about individual visitors. In this paper, we propose a practical attack technique that can infer who clicks what shortened URLs on Twitter. Unlike the conventional browser history stealing attacks, our attack methods only need publicly available information provided by URL shortening services and Twitter. Evaluation results show that our attack technique can compromise Twitter users' privacy with high accuracy.
Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance
Authors: Yang Song, Hao Ma, Hongning Wang, Kuansan Wang
Venue: WWW
Year: 2013

In this paper, we present a log-based study on user search behavior comparisons on three different platforms: desktop, mobile and tablet. We use three-month search logs in 2012 from a commercial search engine for our study. Our objective is to better understand how and to what extent mobile and tablet searchers behave differently than desktop users. Our study spans a variety of aspects including query categorization, query length, search time distribution, search location distribution, user click patterns and so on. From our data set, we reveal that there are significant differences between user search patterns in these three platforms, and therefore use the same ranking system is not an optimal solution for all of them. Consequently, we propose a framework that leverages a set of domain-specific features, along with the training data from desktop search, to further improve the search relevance for mobile and tablet platforms. Experimental results demonstrate that by transferring knowledge from desktop search, search relevance on mobile and tablet can be greatly improved.
A non-learning approach to spelling correction in web queries
Authors: Jason J. Soo
Venue: WWW
Year: 2013

We describe an adverse environment spelling correction algorithm, known as Segments. Segments is language and domain independent and does not require any training data. We evaluate Segments' correction rate of transcription errors in web query logs with the state-of-the-art learning approach. We show that in environments where learning approaches are not applicable, such as multilingual documents, Segments has an F1-score within 0.005 of the learning approach.
Unsupervised approach to generate informative structured snippets for job search engines
Authors: Nikita Spirin, Karrie Karahalios
Venue: WWW
Year: 2013

Aiming to improve user experience for a job search engine, in this paper we propose an idea to switch from query-biased snippets used by most web search engines to rich structured snippets associated with the main sections of a job posting page, which are more appropriate for job search due to specific user needs and the structure of job pages. We present a very simple yet actionable approach to generate such snippets in an unsupervised way. The advantages of the proposed approach are two-fold: it doesn't require manual annotation and therefore can be easily deployed to many languages, which is a desirable property for a job search engine operating internationally; it fuses naturally with the trend towards Mobile Web where the content needs to be optimized for small screen devices and informativeness.
Data-Fu: a language and an interpreter for interaction with read/write linked data
Authors: Steffen Stadtmüller, Sebastian Speiser, Andreas Harth, Rudi Studer
Venue: WWW
Year: 2013

An increasing amount of applications build their functionality on the utilisation and manipulation of web resources. Consequently REST gains popularity with a resource-centric interaction architecture that draws its flexibility from links between resources. Linked Data offers a uniform data model for REST with self-descriptive resources that can be leveraged to avoid a manual ad-hoc development of web-based applications. For declaratively specifying interactions between web resources we introduce Data-Fu, a lightweight declarative rule language with state transition systems as formal grounding. Data-Fu enables the development of data-driven applications that facilitate the RESTful manipulation of read/write Linked Data resources. Furthermore, we describe an interpreter for Data-Fu as a general purpose engine that allows to perform described interactions with web resources by orders of magnitude faster than a comparable Linked Data processor.
A meteoroid on steroids: ranking media items stemming from multiple social networks
Authors: Thomas Steiner
Venue: WWW
Year: 2013

We have developed an application called Social Media Illustrator that allows for finding media items on multiple social networks, clustering them by visual similarity, ranking them by different criteria, and finally arranging them in media galleries that were evaluated to be perceived as aesthetically pleasing. In this paper, we focus on the ranking aspect and show how, for a given set of media items, the most adequate ranking criterion combination can be found by interactively applying different criteria and seeing their effect on-the-fly. This leads us to an empirically optimized media item ranking formula, which takes social network interactions into account. While the ranking formula is not universally applicable, it can serve as a good starting point for an individually adapted formula, all within the context of Social Media Illustrator. A demo of the application is available publicly online at the URL http://social-media-illustrator.herokuapp.com/.
To crop, or not to crop: compiling online media galleries
Authors: Thomas Steiner, Christopher Chedeau
Venue: WWW
Year: 2013

We have developed an application for the automatic generation of media galleries that visually and audibly summarize events based on media items like videos and photos from multiple social networks. Further, we have evaluated different media gallery styles with online surveys and examined their pros and cons. Besides the survey results, our contribution is also the application itself, where media galleries of different styles can be created on-the-fly. A demo is available at http://social-media-illustrator.herokuapp.com/.
MJ no more: using concurrent wikipedia edit spikes with social network plausibility checks for breaking news detection
Authors: Thomas Steiner, Seth van Hooland, Ed Summers
Venue: WWW
Year: 2013

We have developed an application called Wikipedia Live Monitor that monitors article edits on different language versions of Wikipedia--as they happen in realtime. Wikipedia articles in different languages are highly interlinked. For example, the English article "en:2013_Russian_meteor_event" on the topic of the February 15 meteoroid that exploded over the region of Chelyabinsk Oblast, Russia, is interlinked with "ru:ПaДehne_meteopnta_ha_Ypajie_B_2013_roДy?, the Russian article on the same topic. As we monitor multiple language versions of Wikipedia in parallel, we can exploit this fact to detect concurrent edit spikes of Wikipedia articles covering the same topics, both in only one, and in different languages. We treat such concurrent edit spikes as signals for potential breaking news events, whose plausibility we then check with full-text cross-language searches on multiple social networks. Unlike the reverse approach of monitoring social networks first, and potentially checking plausibility on Wikipedia second, the approach proposed in this paper has the advantage of being less prone to false-positive alerts, while being equally sensitive to true-positive events, however, at only a fraction of the processing cost. A live demo of our application is available online at the URL http://wikipedia-irc.herokuapp.com/, the source code is available under the terms of the Apache 2.0 license at https://github.com/tomayac/wikipedia-irc.
A hybrid approach for spotting, disambiguating and annotating places in user-generated text
Authors: Karen Stepanyan, George Gkotsis, Vangelis Banos, Alexandra I. Cristea, Mike Joy
Venue: WWW
Year: 2013

We introduce a geolocation-aware semantic annotation model that extends the existing solutions for spotting and disambiguation of places within user-generated texts. The implemented prototype processes the text of weblog posts and annotates the places and toponyms. It outperforms existing solutions by taking into consideration the embedded geolocation data. The evaluation of the model is based on a set of randomly selected 3,165 geolocation embedded weblog posts, obtained from 1,775 web feeds. The results demonstrate a high degree of accuracy in annotation (87.7%) and a considerable gain (27.8%) in identifying additional entities, and therefore support the adoption of the model for supplementing the existing solutions.
Leveraging on social media to support the global building resilient cities campaign
Authors: David Stevens
Venue: WWW
Year: 2013

This paper presents a summary of the main points put forward during the presentation delivered at the 2nd International Workshop on Social Web for Disaster Management which was held in conjunction with WWW 2013 on May 14th 2013 in Rio de Janeiro, Brazil.
A few thoughts on engineering social machines: extended abstract
Authors: Markus Strohmaier
Venue: WWW
Year: 2013

Social machines are integrated systems of people and computers. What distinguishes social machines from other types of software systems - such as software for cars or air planes - is the unprecedented involvement of data about user behavior, -goals and -motivations into the software system's structure. In social machines, the interaction between a user and the system is mediated by the aggregation of explicit or implicit data from other users. This is the case with systems where, for example, user data is used to suggest search terms (e.g. Google Autosuggest), to recommend products (e.g. Amazon recommendations), to aid navigation (e.g. tag-based navigation) or to filter content (e.g. Digg.com). This makes social machines a novel class of software systems (as opposed to for example safety-related software that is being used in cars) and unique in a sense that potentially essential system properties and functions - such as navigability - are dynamically influenced by aggregate user behavior. Such properties can not be satisfied through the implementation of requirements alone, what is needed is regulation, i.e. a dynamic integration of users' goals and behavior into the continuous process of engineering.
NIFTY: a system for large scale information flow tracking and clustering
Authors: Caroline Suen, Sandy Huang, Chantat Eksombatchai, Rok Sosic, Jure Leskovec
Venue: WWW
Year: 2013

The real-time information on news sites, blogs and social networking sites changes dynamically and spreads rapidly through the Web. Developing methods for handling such information at a massive scale requires that we think about how information content varies over time, how it is transmitted, and how it mutates as it spreads.
Automatically generated spam detection based on sentence-level topic information
Authors: Yoshihiko Suhara, Hiroyuki Toda, Shuichi Nishioka, Seiji Susaki
Venue: WWW
Year: 2013

Spammers use a wide range of content generation techniques with low quality pages known as content spam to achieve their goals. We argue that content spam must be tackled using a wide range of content quality features. In this paper, we propose novel sentence-level diversity features based on the probabilistic topic model. We combine them with other content features to build a content spam classifier. Our experiments show that our method outperforms the conventional methods.