OpenRaiser/Intern-Atlas huggingface dataset:
Pick-a-crowd: tell me what you like, and i'll tell you what to do
Authors: Djellel Eddine Difallah, Gianluca Demartini, Philippe Cudré-Mauroux
Venue: WWW
Year: 2013

Crowdsourcing allows to build hybrid online platforms that combine scalable information systems with the power of human intelligence to complete tasks that are difficult to tackle for current algorithms. Examples include hybrid database systems that use the crowd to fill missing values or to sort items according to subjective dimensions such as picture attractiveness. Current approaches to Crowdsourcing adopt a pull methodology where tasks are published on specialized Web platforms where workers can pick their preferred tasks on a first-come-first-served basis. While this approach has many advantages, such as simplicity and short completion times, it does not guarantee that the task is performed by the most suitable worker. In this paper, we propose and extensively evaluate a different Crowdsourcing approach based on a push methodology. Our proposed system carefully selects which workers should perform a given task based on worker profiles extracted from social networks. Workers and tasks are automatically matched using an underlying categorization structure that exploits entities extracted from the task descriptions on one hand, and categories liked by the user on social platforms on the other hand. We experimentally evaluate our approach on tasks of varying complexity and show that our push methodology consistently yield better results than usual pull strategies.
Compact explanation of data fusion decisions
Authors: Xin Luna Dong, Divesh Srivastava
Venue: WWW
Year: 2013

Despite the abundance of useful information on the Web, different Web sources often provide conflicting data, some being out-of-date, inaccurate, or erroneous. Data fusion aims at resolving conflicts and finding the truth. Advanced fusion techniques apply iterative MAP (Maximum A Posteriori) analysis that reasons about trustworthiness of sources and copying relationships between them. Providing explanations for such decisions is important for a better understanding, but can be extremely challenging because of the complexity of the analysis during decision making.
Co-operative content adaptation framework: satisfying consumer and content creator in resource constrained browsing
Authors: Ayush Dubey, Pradipta De, Kuntal Dey, Sumit Mittal, Vikas Agarwal, Malolan Chetlur, Sougata Mukherjea
Venue: WWW
Year: 2013

Mobile Web is characterized by two salient features, ubiquitous access to content and limited resources, like bandwidth and battery. Since most web pages are designed for the wired Internet, it is challenging to adapt the pages seamlessly to ensure a satisfactory mobile web experience. Content heavy web pages lead to longer load time on mobile browsers. Pre-defined load order of items in a page does not adapt to mobile browsing habits, where user looks for different snippets of a page to load under different contexts. Web content adaptation for mobile web has mainly focused on the user to define her preferences for content. We propose a framework where content creator is additionally included in guiding the adaptation. Allowing content creator to specify importance of items in a page also helps in factoring her incentives by pushing revenue generating content. We present mechanisms to enable cooperative content adaptation. Preliminary results show the efficacy of cooperative content adaptation in resource constrained mobile browsing scenario.
Twitter (R)evolution: privacy, free speech and disclosure
Authors: Lilian Edwards, Andrea M. Matwyshyn
Venue: WWW
Year: 2013

Using Twitter as a case study, this paper sets forth the legal tensions faced by social networks that seek to defend privacy interests of users. Recent EC and UN initiatives have begun to suggest an increased role for corporations as protectors of human rights. But, as yet, binding rather than voluntary obligations of this kind under international human rights law seem either non-existent or highly conflicted, and structural limitations to such a shift may currently exist under both U.S. and UK law. Companies do not face decisions regarding disclosure in a vacuum, rather they face them constrained by existing obligations under (sometimes conflicting) legal demands. Yet, companies such as Twitter are well-positioned to be advocates for consumers' interests in these legal debates. Using several recent corporate disclosure decisions regarding user identity as illustration, this paper places questions of privacy, free speech and disclosure in broader legal context. More scholarship is needed on the mechanics of how online intermediaries, especially social media, manage their position as crucial speech platforms in democratic as well as less democratic regimes.
Effective analysis, characterization, and detection of malicious web pages
Authors: Birhanu Eshete
Venue: WWW
Year: 2013

The steady evolution of the Web has paved the way for miscreants to take advantage of vulnerabilities to embed malicious content into web pages. Up on a visit, malicious web pages steal sensitive data, redirect victims to other malicious targets, or cease control of victim's system to mount future attacks. Approaches to detect malicious web pages have been reactively effective at special classes of attacks like drive-by-downloads. However, the prevalence and complexity of attacks by malicious web pages is still worrisome. The main challenges in this problem domain are (1) fine-grained capturing and characterization of attack payloads (2) evolution of web page artifacts and (3) exibility and scalability of detection techniques with a fast-changing threat landscape. To this end, we proposed a holistic approach that leverages static analysis, dynamic analysis, machine learning, and evolutionary searching and optimization to effectively analyze and detect malicious web pages. We do so by: introducing novel features to capture fine-grained snapshot of malicious web pages, holistic characterization of malicious web pages, and application of evolutionary techniques to fine-tune learning-based detection models pertinent to evolution of attack payloads. In this paper, we present key intuition and details of our approach, results obtained so far, and future work.
Crime applications and social machines: crowdsourcing sensitive data
Authors: Maire Byrne Evans, Kieron O&apos, Hara, Thanassis Tiropanis, Craig Webber
Venue: WWW
Year: 2013

The authors explore some issues with the United Kingdom (U.K.) crime reporting and recording systems which currently produce Open Crime Data. The availability of Open Crime Data seems to create a potential data ecosystem which would encourage crowdsourcing, or the creation of social machines, in order to counter some of these issues. While such solutions are enticing, we suggest that in fact the theoretical solution brings to light fairly compelling problems, which highlight some limitations of crowdsourcing as a means of addressing Berners-Lee's "social constraint." The authors present a thought experiment -- a Gendankenexperiment - in order to explore the implications, both good and bad, of a social machine in such a sensitive space and suggest a Web Science perspective to pick apart the ramifications of this thought experiment as a theoretical approach to the characterisation of social machines.
Detecting malware with graph-based methods: traffic classification, botnets, and facebook scams
Authors: Michalis Faloutsos
Venue: WWW
Year: 2013

In this talk, we highlight two topics on security from our lab. First, we address the problem of Internet traffic classification (e.g. web, filesharing, or botnet?). We present a fundamentally different approach to classifying traffic that studies the network wide behavior by modeling the interactions of users as a graph. By contrast, most previous approaches use statistics such as packet sizes and inter-packet delays. We show how our approach gives rise to novel and powerful ways to: (a) visualize the traffic, (b) model the behavior of applications, and (c) detect abnormalities and attacks. Extending this approach, we develop ENTELECHEIA, a botnet-detection method. Tests with real data suggests that our graph-based approach is very promising.
Learning to annotate tweets with crowd wisdom
Authors: Wei Feng, Jianyong Wang
Venue: WWW
Year: 2013

In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. However, two problems remain unsolved during an annotation: (1) Users have no way to know whether some related hashtags have already been created. (2) Users have their own way to categorize tweets. Thus personalization is needed. To address the above problems, we develop a statistical model for Personalized Hashtag Recommendation. With millions of "tweet, hashtag" pairs being generated everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Our model considers rich auxiliary information like URLs, locations, social relation, temporal characteristics of hashtag adoption, etc. We show our model successfully outperforms existing methods on real datasets crawled from Twitter.
Resilience of dynamic overlays through local interactions
Authors: Stefano Ferretti
Venue: WWW
Year: 2013

This paper presents a self-organizing protocol for dynamic (unstructured P2P) overlay networks, which allows to react to the variability of node arrivals and departures. Through local interactions, the protocol avoids that the departure of nodes causes a partitioning of the overlay. We show that it is sufficient to have knowledge about 1st and 2nd neighbours, plus a simple interaction P2P protocol, to make unstructured networks resilient to node faults. A simulation assessment over different kinds of overlay networks demonstrates the viability of the proposal.
Identifying, understanding and detecting recurring, harmful behavior patterns in collaborative Wikipedia editing: doctoral proposal
Authors: Fabian Flöck
Venue: WWW
Year: 2013

In this doctoral proposal, we describe an approach to identify recurring, collective behavioral mechanisms in the collaborative interactions of Wikipedia editors that have the potential to undermine the ideals of quality, neutrality and completeness of article content. We outline how we plan to parametrize these patterns in order to understand their emergence and evolution and measure their effective impact on content production in Wikipedia. On top of these results we intend to build end-user tools to increase the transparency of the evolution of articles and equip editors with more elaborated quality monitors. We also sketch out our evaluation plans and report on already accomplished tasks.
Ontology based feature level opinion mining for portuguese reviews
Authors: Larissa A. de Freitas, Renata Vieira
Venue: WWW
Year: 2013

This paper presents a thesis whose goal is to propose and evaluate methods to identify polarity in Portuguese user generated reviews according to features described in domain ontologies (experiments will consider movie and hotel ontologies Movie Ontology1 and Hontology2).
Can we use linked data semantic annotators for the extraction of domain-relevant expressions?
Authors: Michel Gagnon, Amal Zouaq, Ludovic Jean-Louis
Venue: WWW
Year: 2013

Semantic annotation is the process of identifying expressions in texts and linking them to some semantic structure. In particular, Linked data-based Semantic Annotators are now becoming the new Holy Grail for meaning extraction from unstructured documents. This paper presents an evaluation of the main linked data-based annotators available with a focus on domain topics and named entities. In particular, we compare the ability of each tool to annotate relevant domain expressions in text. The paper also proposes a combination of annotators through voting methods and machine learning. Our results show that some linked-data annotators, especially Alchemy, can be considered as a useful resource for topic extraction. They also show that a substantial increase in recall can be achieved by combining the annotators with a weighted voting scheme. Finally, an interesting result is that by removing Alchemy from the combination, or by combining only the more precise annotators, we get a significant increase in precision, at the cost of a lower recall.
Who broke the news?: an analysis on first reports of news events
Authors: Matthias Gallé, Jean-Michel Renders, Eric Karstens
Venue: WWW
Year: 2013

We present a data-driven study on which sources were the first to report on news events. For this, we implemented a news-aggregator that included a large number of established news sources and covered one year of data. We present a novel framework that is able to retrieve a large number of events and not only the most salient ones, while at the same time making sure that they are not exclusively of local impact.
TagVisor: extending web pages with interaction events to support presentation in digital signage
Authors: Marcio dos Santos Galli, Eduardo Pezutti Beletato Santos
Venue: WWW
Year: 2013

New interaction experiences are fundamentally changing the way we interact with the web. Emerging touch-based devices and a variety of web-connected appliances represents challenges that prevents the seamless reach of web resources originally tailored for the standard browser experience. This paper explores how web pages can be re-purposed and become interactive presentations that effectively supports communication in scenarios such as digital signage and other presentation use cases. We will cover the TagVisor project which is a JavaScript run-time that uses modern animation effects and provides an HTML5 extension approach to support the authoring of visual narratives using plain web pages.
PrefixSolve: efficiently solving multi-source multi-destination path queries on RDF graphs by sharing suffix computations
Authors: Sidan Gao, Kemafor Anyanwu
Venue: WWW
Year: 2013

Uncovering the "nature" of the connections between a set of entities e.g. passengers on a flight and organizations on a watchlist can be viewed as a Multi-Source Multi-Destination (MSMD) Path Query problem on labeled graph data models such as RDF. Using existing graph-navigational path finding techniques to solve MSMD problems will require queries to be decomposed into multiple single-source or destination path subqueries, each of which is solved independently. Navigational techniques on disk-resident graphs typically generate very poor I/O access patterns for large, disk-resident graphs and for MSMD path queries, such poor access patterns may be repeated if common graph exploration steps exist across subqueries.
Cross-lingual web spam classification
Authors: András Garzó, Bálint Daróczy, Tamás Kiss, Dávid Siklósi, András A. Benczúr
Venue: WWW
Year: 2013

While Web spam training data exists in English, we face an expensive human labeling procedure if we want to filter a Web domain in a different language. In this paper we overview how existing content and link based classification techniques work, how models can be "translated" from English into another language, and how language-dependent and independent methods combine. In particular we show that simple bag-of-words translation works very well and in this procedure we may also rely on mixed language Web hosts, i.e. those that contain an English translation of part of the local language text. Our experiments are conducted on the ClueWeb09 corpus as the training English collection and a large Portuguese crawl of the Portuguese Web Archive. To foster further research, we provide labels and precomputed values of term frequencies, content and link based features for both ClueWeb09 and the Portuguese data.
XML validation: looking backward - strongly typed and flexible XML processing are not incompatible
Authors: Pierre Genevès, Nabil Layaïda
Venue: WWW
Year: 2013

One major concept in web development using XML is validation: checking whether some document instance fulfills structural constraints described by some schema. Over the last few years, there has been a growing debate about XML validation, and two main schools of thought emerged about the way it should be done. On the one hand, some advocate the use of validation with respect to complete grammar-based descriptions such as DTDs and XML Schemas. On the other hand, motivated by a need for greater flexibility, others argue for no validation at all, or prefer the use of lightweight constraint languages such as Schematron with the aim of validating only required constraints, while making schema descriptions more compositional and more reusable.
Temporal summarization of event-related updates in wikipedia
Authors: Mihai Georgescu, Dang Duc Pham, Nattiya Kanhabua, Sergej Zerr, Stefan Siersdorfer, Wolfgang Nejdl
Venue: WWW
Year: 2013

Wikipedia is a free multilingual online encyclopedia covering a wide range of general and specific knowledge. Its content is continuously maintained up-to-date and extended by a supporting community. In many cases, real-world events influence the collaborative editing of Wikipedia articles of the involved or affected entities. In this paper, we present Wikipedia Event Reporter, a web-based system that supports the entity-centric, temporal analytics of event-related information in Wikipedia by analyzing the whole history of article updates. For a given entity, the system first identifies peaks of update activities for the entity using burst detection and automatically extracts event-related updates using a machine-learning approach. Further, the system determines distinct events through the clustering of updates by exploiting different types of information such as update time, textual similarity, and the position of the updates within an article. Finally, the system generates the meaningful temporal summarization of event-related updates and automatically annotates the identified events in a timeline.
When tolerance causes weakness: the case of injection-friendly browsers
Authors: Yossi Gilad, Amir Herzberg
Venue: WWW
Year: 2013

We present a practical off-path TCP-injection attack for connections between current, non-buggy browsers and web-servers. The attack allows web-cache poisoning with malicious objects; these objects can be cached for long time period, exposing any user of that cache to XSS, CSRF and phishing attacks.
Zero-cost labelling with web feeds for weblog data extraction
Authors: George Gkotsis, Karen Stepanyan, Alexandra I. Cristea, Mike Joy
Venue: WWW
Year: 2013

Data extraction from web pages often involves either human intervention for training a wrapper or a reduced level of granularity in the information acquired. Even though the study of social media has drawn the attention of researchers, weblogs remain a part of the web that cannot be harvested efficiently. In this paper, we propose a fully automated approach in generating a wrapper for weblogs, which exploits web feeds for cheap labelling of weblog properties. Instead of performing a pairwise comparison between posts, the model matches the values of the web feeds against their corresponding HTML elements retrieved from multiple weblog posts. It adopts a probabilistic approach for deriving a set of rules and automating the process of wrapper generation. Our evaluation shows that our approach is robust, accurate and efficient in handling different types of weblogs.
Exploration in web science: instruments for web observatories
Authors: Marie Joan Kristine Gloria, Deborah L. McGuinness, Joanne S. Luciano, Qingpeng Zhang
Venue: WWW
Year: 2013

The following contribution highlights selected work conducted by Rensselaer Polytechnic Institute's Web Science Research Center. (RPI WSRC). Specifically, it brings to light four different themed Web Observatories - Science Data, Health and Life Sciences, Open Government, and Social Spaces. Each of these observatories serves as a repository of data, tools, and methods that help answer complicated questions in each of these research areas. We present six case studies featuring tools and methods developed by RPI WSRC to aide in the exploration, discovery, and analysis of large data sets. These case studies along with our web observatory developments are aimed to increase our understanding of web science in general and to serve as test beds for our research.
Using topic models for Twitter hashtag recommendation
Authors: Fréderic Godin, Viktor Slavkovikj, Wesley De Neve, Benjamin Schrauwen, Rik Van de Walle
Venue: WWW
Year: 2013

Since the introduction of microblogging services, there has been a continuous growth of short-text social networking on the Internet. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. Twitter, one of the largest microblogging sites, allows users to make use of hashtags to categorize their posts. However, the majority of tweets do not contain tags, which hinders the quality of the search results. In this paper, we propose a novel method for unsupervised and content-based hashtag recommendation for tweets. Our approach relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets. The advantage of our approach is the use of a topic distribution to recommend general hashtags.
The cost of annoying ads
Authors: Daniel G. Goldstein, R. Preston McAfee, Siddharth Suri
Venue: WWW
Year: 2013

Display advertisements vary in the extent to which they annoy users. While publishers know the payment they receive to run annoying ads, little is known about the cost such ads incur due to user abandonment. We conducted a two-experiment investigation to analyze ad features that relate to annoyingness and to put a monetary value on the cost of annoying ads. The first experiment asked users to rate and comment on a large number of ads taken from the Web. This allowed us to establish sets of annoying and innocuous ads for use in the second experiment, in which users were given the opportunity to categorize emails for a per-message wage and quit at any time. Participants were randomly assigned to one of three different pay rates and also randomly assigned to categorize the emails in the presence of no ads, annoying ads, or innocuous ads. Since each email categorization constituted an impression, this design, inspired by Toomim et al., allowed us to determine how much more one must pay a person to generate the same number of impressions in the presence of annoying ads compared to no ads or innocuous ads. We conclude by proposing a theoretical model which relates ad quality to publisher market share, illustrating how our empirical findings could affect the economics of Internet advertising.
Creating a billion-scale searchable web archive
Authors: Daniel Gomes, Miguel Costa, David Cruz, João Miranda, Simão Fontes
Venue: WWW
Year: 2013

Web information is ephemeral. Several organizations around the world are struggling to archive information from the web before it vanishes. However, users demand efficient and effective search mechanisms to access the already vast collections of historical information held by web archives. The Portuguese Web Archive is the largest full-text searchable web archive publicly available. It supports search over 1.2 billion files archived from the web since 1996. This study contributes with an overview of the lessons learned while developing the Portuguese Web Archive, focusing on web data acquisition, ranking search results and user interface design. The developed software is freely available as an open source project. We believe that sharing our experience obtained while developing and operating a running service will enable other organizations to start or improve their web archives.