OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing video access patterns in mainstream media portals
Authors: Lucas C. O. Miranda, Rodrygo L. T. Santos, Alberto H. F. Laender
Venue: WWW
Year: 2013

Watching online videos is part of the daily routine of a considerable fraction of Internet users nowadays. Understanding the patterns of access to these videos is paramount for improving the capacity planning for video providers, the conversion rate for advertisers, and the relevance of the whole online video watching experience for end users. While much research has been conducted to analyze video access patterns in user-generated content (UGC), little is known of how such patterns manifest in mainstream media (MSM) portals. In this paper, we perform the first large-scale analysis of video access patterns in MSM portals. As a case study, we analyze interaction logs across a total of 38 Brazilian MSM portals, including six of the largest portals in the country, over a period of eight weeks. Our analysis reveals interesting static and temporal video access patterns in MSM portals, which we compare and contrast to the access patterns reported for UGC websites. Overall, our analysis provides several insights for an improved understanding of video access on the Internet beyond UGC websites.
The FLDA model for aspect-based opinion mining: addressing the cold start problem
Authors: Samaneh Moghaddam, Martin Ester
Venue: WWW
Year: 2013

Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously. These models are normally trained at the item level, i.e., a model is learned for each item separately. Learning a model per item is fine when the item has been reviewed extensively and has enough training data. However, in real-life data sets such as those from Epinions.com and Amazon.com more than 90% of items have less than 10 reviews, so-called cold start items. State-of-the-art LDA models for aspect-based opinion mining are trained at the item level and therefore perform poorly for cold start items due to the lack of sufficient training data. In this paper, we propose a probabilistic graphical model based on LDA, called Factorized LDA (FLDA), to address the cold start problem. The underlying assumption of FLDA is that aspects and ratings of a review are influenced not only by the item but also by the reviewer. It further assumes that both items and reviewers can be modeled by a set of latent factors which represent their aspect and rating distributions. Different from state-of-the-art LDA models, FLDA is trained at the category level and learns the latent factors using the reviews of all the items of a category, in particular the non cold start items, and uses them as prior for cold start items. Our experiments on three real-life data sets demonstrate the improved effectiveness of the FLDA model in terms of likelihood of the held-out test set. We also evaluate the accuracy of FLDA based on two application-oriented measures.
Unveiling Zeus: automated classification of malware samples
Authors: Abedelaziz Mohaisen, Omar Alrawi
Venue: WWW
Year: 2013

Malware family classification is an age old problem that many Anti-Virus (AV) companies have tackled. There are two common techniques used for classification, signature based and behavior based. Signature based classification uses a common sequence of bytes that appears in the binary code to identify and detect a family of malware. Behavior based classification uses artifacts created by malware during execution for identification. In this paper we report on a unique dataset we obtained from our operations and classified using several machine learning techniques using the behavior-based approach. Our main class of malware we are interested in classifying is the popular Zeus malware. For its classification we identify 65 features that are unique and robust for identifying malware families. We show that artifacts like file system, registry, and network features can be used to identify distinct malware families with high accuracy - in some cases as high as 95 percent.
SAMOA: a platform for mining big data streams
Authors: Gianmarco De Francisci Morales
Venue: WWW
Year: 2013

Social media and user generated content are causing an ever growing data deluge. The rate at which we produce data is growing steadily, thus creating larger and larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these continuous streams of user activities. The value of these streams relies in their freshness and relatedness to ongoing events. However, current (de-facto standard) solutions for big data analysis are not designed to deal with evolving streams.
Synthetic review spamming and defense
Authors: Alex Morales, Huan Sun, Xifeng Yan
Venue: WWW
Year: 2013

Online reviews are widely adopted in many websites such as Amazon, Yelp, and TripAdvisor. Positive reviews can bring significant financial gains, while negative ones often cause sales loss. This fact, unfortunately, results in strong incentives for opinion spam to mislead readers. Instead of hiring humans to write deceptive reviews, in this work, we bring into attention an automated, low-cost process for generating fake reviews, variations of which could be easily employed by evil attackers in reality. To the best of our knowledge, we are the first to expose the potential risk of machine-generated deceptive reviews. Our simple review synthesis model uses one truthful review as a template, and replaces its sentences with those from other reviews in a repository. The fake reviews generated by this mechanism are extremely hard to detect: Both the state-of-the-art machine detectors and human readers have an error rate of 35%-48%. A novel defense method that leverages the difference of semantic flows between fake and truthful reviews is developed, reducing the detection error rate to approximately 22%. Nevertheless, it is still a challenging research task to further decrease the error rate.
Quill: a collaborative design assistant for cross platform web application user interfaces
Authors: Vivian Genaro Motti, Dave Raggett
Venue: WWW
Year: 2013

Web application development teams face an increasing burden when they need to come up with a consistent user interface across different platforms with different characteristics, for example, desktop, smart phone and tablet devices. This is going to get even worse with the adoption of HTML5 on TVs and cars. This short paper describes a browser-based collaborative design assistant that does the drudge work of ensuring that the user interfaces are kept in sync across all of the target platforms and with changes to the domain data and task models. This is based upon an expert system that dynamically updates the user interface design to reflect the developer's decisions. This is implemented in terms of constraint propagation and search through the design space. An additional benefit is the ease of providing accessible user interfaces in conjunction with assistive technologies.
Incorporating author preference in sentiment rating prediction of reviews
Authors: Subhabrata Mukherjee, Gaurab Basu, Sachindra Joshi
Venue: WWW
Year: 2013

Traditional works in sentiment analysis do not incorporate author preferences during sentiment classification of reviews. In this work, we show that the inclusion of author preferences in sentiment rating prediction of reviews improves the correlation with ground ratings, over a generic author independent rating prediction model. The overall sentiment rating prediction for a review has been shown to improve by capturing facet level rating. We show that this can be further developed by considering author preferences in predicting the facet level ratings, and hence the overall review rating. To the best of our knowledge, this is the first work to incorporate author preferences in rating prediction.
Intent classification of voice queries on mobile devices
Authors: Subhabrata Mukherjee, Ashish Verma, Kenneth Ward Church
Venue: WWW
Year: 2013

Mobile query classification faces the usual challenges of encountering short and noisy queries as in web search. However, the task of mobile query classification is made difficult by the presence of more inter-active and personalized queries like map, command and control, dialogue, joke etc. Voice queries are made more difficult than typed queries due to the errors introduced by the automatic speech recognizer. This is the first paper, to the best of our knowledge, to bring the complexities of voice search and intent classification together. In this paper, we propose some novel features for intent classification, like the url's of the search engine results for the given query. We also show the effectiveness of other features derived from the part-of-speech information of the query and search engine results, in proposing a multi-stage classifier for intent classification. We evaluate the classifier using tagged data, collected from a voice search android application, where we achieve an average of 22% f-score improvement per category, over the commonly used bag-of-words baseline.
Characterizing branching processes from sampled data
Authors: Fabricio Murai, Bruno F. Ribeiro, Donald F. Towsley, Krista Gile
Venue: WWW
Year: 2013

Branching processes model the evolution of populations of agents that randomly generate offspring (children). These processes, more patently Galton-Watson processes, are widely used to model biological, social, cognitive, and technological phenomena, such as the diffusion of ideas, knowledge, chain letters, viruses, and the evolution of humans through their Y-chromosome DNA or mitochondrial RNA. A practical challenge of modeling real phenomena using a Galton-Watson process is the choice of the offspring distribution, which must be measured from the population. In most cases, however, directly measuring the offspring distribution is unrealistic due to lack of resources or the death of agents. So far, researchers have relied on informed guesses to guide their choice of offspring distribution. In this work we propose two methods to estimate the offspring distribution from real sampled data. Using a small sampled fraction of the agents and instrumented with the identity of the ancestors of the sampled agents, we show that accurate offspring distribution estimates can be obtained by sampling as little as 14% of the population.
RESLVE: leveraging user interest to improve entity disambiguation on short text
Authors: Elizabeth L. Murnane, Bernhard Haslhofer, Carl Lagoze
Venue: WWW
Year: 2013

We address the Named Entity Disambiguation (NED) problem for short, user-generated texts on the social Web. In such settings, the lack of linguistic features and sparse lexical context result in a high degree of ambiguity and sharp performance drops of nearly 50% in the accuracy of conventional NED systems. We handle these challenges by developing a model of user-interest with respect to a personal knowledge context; and Wikipedia, a particularly well-established and reliable knowledge base, is used to instantiate the procedure. We conduct systematic evaluations using individuals' posts from Twitter, YouTube, and Flickr and demonstrate that our novel technique is able to achieve substantial performance gains beyond state-of-the-art NED methods.
A proximity-based fallback model for hybrid web recommender systems
Authors: Jaeseok Myung
Venue: WWW
Year: 2013

Although there are numerous websites that provide recommendation services for various items such as movies, music, and books, most of studies on recommender systems only focus on one specific item type. As recommender sites expand to cover several types of items, though, it is important to build a hybrid web recommender system that can handle multiple types of items.
Ad impression forecasting for sponsored search
Authors: Abhirup Nath, Shibnath Mukherjee, Prateek Jain, Navin Goyal, Srivatsan Laxman
Venue: WWW
Year: 2013

A typical problem for a search engine (hosting sponsored search service) is to provide the advertisers with a forecast of the number of impressions his/her ad is likely to obtain for a given bid. Accurate forecasts have high business value, since they enable advertisers to select bids that lead to better returns on their investment. They also play an important role in services such as automatic campaign optimization. Despite its importance the problem has remained relatively unexplored in literature. Existing methods typically overfit to the training data, leading to inconsistent performance. Furthermore, some of the existing methods cannot provide predictions for new ads, i.e., for ads that are not present in the logs. In this paper, we develop a generative model based approach that addresses these drawbacks. We design a Bayes net to capture inter-dependencies between the query traffic features and the competitors in an auction. Furthermore, we account for variability in the volume of query traffic by using a dynamic linear model. Finally, we implement our approach on a production grade MapReduce framework and conduct extensive large scale experiments on substantial volumes of sponsored search data from Bing. Our experimental results demonstrate significant advantages over existing methods as measured using several accuracy/error criteria, improved ability to provide estimates for new ads and more consistent performance with smaller variance in accuracies. Our method can also be adapted to several other related forecasting problems such as predicting average position of ads or the number of clicks under budget constraints.
Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts
Authors: Vidhya Navalpakkam, LaDawn Jentzsch, Rory Sayres, Sujith Ravi, Amr Ahmed, Alexander J. Smola
Venue: WWW
Year: 2013

As search pages are becoming increasingly complex, with images and nonlinear page layouts, understanding how users examine the page is important. We present a lab study on the effect of a rich informational panel to the right of the search result column, on eye and mouse behavior. Using eye and mouse data, we show that the flow of user attention on nonlinear page layouts is different from the widely believed top-down linear examination order of search results. We further demonstrate that the mouse, like the eye, is sensitive to two key attributes of page elements -- their position (layout), and their relevance to the user's task. We identify mouse measures that are strongly correlated with eye movements, and develop models to predict user attention (eye gaze) from mouse activity. These findings show that mouse tracking can be used to infer user attention and information flow patterns on search pages. Potential applications include ranking, search page optimization, and UI evaluation.
Understanding and decreasing the network footprint of catch-up tv
Authors: Gianfranco Nencioni, Nishanth Sastry, Jigna Chandaria, Jon Crowcroft
Venue: WWW
Year: 2013

"Catch-up", or on-demand access of previously broadcast TV content over the public Internet, constitutes a significant fraction of peak time network traffic. This paper analyses consumption patterns of nearly 6 million users of a nationwide deployment of a catch-up TV service, to understand the network support required. We find that catch-up has certain natural scaling properties compared to traditional TV: The on-demand nature spreads load over time, and users have much higher completion rates for content streams than previously reported. Users exhibit strong preferences for serialised content, and for specific genres.
Sorry, i don't speak SPARQL: translating SPARQL queries into natural language
Authors: Axel-Cyrille Ngonga Ngomo, Lorenz Bühmann, Christina Unger, Jens Lehmann, Daniel Gerber
Venue: WWW
Year: 2013

Over the past years, Semantic Web and Linked Data technologies have reached the backend of a considerable number of applications. Consequently, large amounts of RDF data are constantly being made available across the planet. While experts can easily gather information from this wealth of data by using the W3C standard query language SPARQL, most lay users lack the expertise necessary to proficiently interact with these applications. Consequently, non-expert users usually have to rely on forms, query builders, question answering or keyword search tools to access RDF data. However, these tools have so far been unable to explicate the queries they generate to lay users, making it difficult for these users to i) assess the correctness of the query generated out of their input, and ii) to adapt their queries or iii) to choose in an informed manner between possible interpretations of their input. This paper addresses this drawback by presenting SPARQL2NL, a generic approach that allows verbalizing SPARQL queries, i.e., converting them into natural language. Our framework can be integrated into applications where lay users are required to understand SPARQL or to generate SPARQL queries in a direct (forms, query builders) or an indirect (keyword search, question answering) manner. We evaluate our approach on the DBpedia question set provided by QALD-2 within a survey setting with both SPARQL experts and lay users. The results of the 115 filled surveys show that SPARQL2NL can generate complete and easily understandable natural language descriptions. In addition, our results suggest that even SPARQL experts can process the natural language representation of SPARQL queries computed by our approach more efficiently than the corresponding SPARQL queries. Moreover, non-experts are enabled to reliably understand the content of SPARQL queries.
Modeling uncertain provenance and provenance of uncertainty in W3C PROV
Authors: Tom De Nies, Sam Coppens, Erik Mannens, Rik Van de Walle
Venue: WWW
Year: 2013

This paper describes how to model uncertain provenance and provenance of uncertain things in a flexible and unintrusive manner using PROV, W3C's new standard for provenance. Three new attributes with clearly defined values and semantics are proposed. Modeling this information is an important step towards the modeling and derivation of trust from resources whose provenance is described using PROV.
Towards automatic assessment of the social media impact of news content
Authors: Tom De Nies, Gerald Haesendonck, Fréderic Godin, Wesley De Neve, Erik Mannens, Rik Van de Walle
Venue: WWW
Year: 2013

In this paper, we investigate the possibilities to estimate the impact the content of a news article has on social media, and in particular on Twitter. We propose an approach that makes use of captured and temporarily stored microposts found in social media, and compares their relevance to an arbitrary news article. These results are used to derive key indicators of the social media impact of the specified content. We describe each step of our approach, provide a first implementation, and discuss the most imminent challenges and discussion points.
Bitsquatting: exploiting bit-flips for fun, or profit?
Authors: Nick Nikiforakis, Steven Van Acker, Wannes Meert, Lieven Desmet, Frank Piessens, Wouter Joosen
Venue: WWW
Year: 2013

Over the last fifteen years, several types of attacks against domain names and the companies relying on them have been observed. The well-known cybersquatting of domain names gave way to typosquatting, the abuse of a user's mistakes when typing a URL in her browser's address bar. Recently, a new attack against domain names surfaced, namely bitsquatting. In bitsquatting, an attacker leverages random bit-errors occurring in the memory of commodity computers and smartphones, to redirect Internet traffic to attacker-controlled domains.
Linked services infrastructure: a single entry point for online media related to any linked data concept
Authors: Lyndon J. B. Nixon
Venue: WWW
Year: 2013

In this submission, we describe the Linked Services Infrastructure (LSI). It uses Semantic Web Service technology to map individual concepts (identified by Linked Data URIs) to sets of online media content aggegrated from heterogeneous Web APIs. It exposes this mapping service in a RESTful API and returns RDF based responses for further processing if desired. The LSI can be used as a general purpose tool for user agents to retrieve different online media resources to illustrate a concept to a user.
The importance of linked media to the future web: lime 2013 keynote talk - a proposal for the linked media research agenda
Authors: Lyndon J. B. Nixon
Venue: WWW
Year: 2013

If the future Web will be able to fully leverage the scale and quality of online media, a Web scale layer of structured, interlinked media annotations is needed, which we will call Linked Media, inspired by the Linked Data movement for making structured, interlinked descriptions of resources better available online. Mobile and tablet devices, as well as connected TVs, introduce novel application domains that will benefit from broad understanding and acceptance of Linked Media standards. In the keynote, I will provide an overview of current practices and specification efforts in the domain of video and Web content integration, drawing from the LinkedTV1 and MediaMixer2 projects. From this, I will present a vision for a Linked Media layer on the future Web will can empower new media-centric applications in a world of ubiquitous online multimedia.
Connected media experiences: web based interactive video using linked data
Authors: Lyndon J. B. Nixon, Matthias Bauer, Cristian Bara
Venue: WWW
Year: 2013

This demo submission presents a set of tools and an extended framework with API for enabling the semantically empowered enrichment of online video with Web content. As audiovisual media is increasingly transmitted online, new services deriving added value from such material can be imagined. For example, combining it with other material elsewhere on the Web which is related to it or enhances it in a meaningful way, to the benefit of the owner of the original content, the providers of the content enhancing it and the end consumer who can access and interact with these new services. Since the services are built around providing new experiences through connecting different related media together, we consider such services to be Connected Media Experiences (ConnectME). This paper presents a toolset for ConnectME - an online annotation tool for video and a HTML5-based enriched video player - as well as the ConnectME framework which enables these media experiences to be generated on the server side with semantic technology.