OpenRaiser/Intern-Atlas huggingface dataset:
User community reconstruction using sampled microblogging data
Authors: Miki Enoki, Yohei Ikawa, Rudy Raymond
Venue: WWW
Year: 2012

User community recognition in social media services is important to identify hot topics or users' interests and concerns in a timely way when a disaster has occurred. In microblogging services, many short messages are posted every day and some of them represent replies or forwarded messages between users. We extract such conversational messages to link the users as a user network and regard the strongly-connected components in the network as indicators of user communities. However, using all of the microblog data for user community extraction is too costly and requires too much storage space when decomposing strongly-connected components. In contrast, using sampled data may miss some user connections and thus divide one user community into pieces. In this paper, we propose a method for user community reconstruction using the lexical similarity of the messages and the user's link information between separate communities.
DOHA: scalable real-time web applications through adaptive concurrent execution
Authors: Aiman Erbad, Norman C. Hutchinson, Charles Krasic
Venue: WWW
Year: 2012

Browsers have become mature execution platforms enabling web applications to rival their desktop counterparts. An important class of such applications is interactive multimedia: games, animations, and interactive visualizations. Unlike many early web applications, these applications are latency sensitive and processing (CPU and graphics) intensive. When demands exceed available resources, application quality (e.g., frame rate) diminishes because it is hard to balance timeliness and utilization. The quality of ambitious web applications is also limited by single-threaded execution prevalent in the Web. Applications need to scale their quality, and thereby scale processing load, based on the resources that are available. We refer to this as scalable quality.
Smart places: multi-agent based virtual community management system
Authors: Muhammad Fahad, Olivier Boissier, Pierre Maret, Christophe Gravier
Venue: WWW
Year: 2012

Virtual Communities (VC) are synergetic digital spaces to build collaborations and exchange knowledge between people sharing similar interests or goals. They respond to human needs such as information sharing, friendship, and recreation. In this paper, we present a VC Management System, based on a Multi-Agent Oriented Programming (MAOP) approach. Using the MAOP JaCaMo platform, we make possible to install a decentralized and open management of those communities in a context of smart cities. Participants of a virtual community are each supported by a Jason agent that encapsulates the logic and the control of the user participation to a VC (such as publishing posts, notifying members, etc.). A set of CArtAgO artifacts provides the basic functionalities and operations giving access to the basic functionalities for knowledge exchange in such VC. Agents can exploit those artifacts to execute their tasks while achieving their individual and collective goals. The global coordination strategies enforcing behaviors of the participants of a VC are expressed as multi-agent normative organizations managed by the Moise framework.
How effective is targeted advertising?
Authors: Ayman Farahat, Michael C. Bailey
Venue: WWW
Year: 2012

Advertisers are demanding more accurate estimates of the impact of targeted advertisements, yet no study proposes an appropriate methodology to analyze the effectiveness of a targeted advertising campaign, and there is a dearth of empirical evidence on the effectiveness of targeted advertising as a whole. The targeted population is more likely to convert from advertising so the response lift between the targeted and untargeted group to the advertising is likely an overestimate of the impact of targeted advertising. We propose a difference-in-differences estimator to account for this selection bias by decomposing the impact of targeting into selection bias and treatment effects components. Using several large-scale online advertising campaigns, we test the effectiveness of targeted advertising on brand-related searches and clickthrough rates. We find that the treatment effect on the targeted group is about twice as large for brand-related searches, but naively estimating this effect without taking into account selection bias leads to an overestimation of the lift from targeting on brand-related searches by almost 1,000%.
HeterRank: addressing information heterogeneity for personalized recommendation in social tagging systems
Authors: Wei Feng, Jianyong Wang
Venue: WWW
Year: 2012

A social tagging system provides users an effective way to collaboratively annotate and organize items with their own tags. A social tagging system contains heterogenous information like users' tagging behaviors, social networks, tag semantics and item profiles. All the heterogenous information helps alleviate the cold start problem due to data sparsity. In this paper, we model a social tagging system as a multi-type graph and propose a graph-based ranking algorithm called HeterRank for tag recommendation. Experimental results on three publicly available datasets, i.e., CiteULike, Last.fm and Delicious prove the effectiveness of HeterRank for tag recommendation with heterogenous information.
The multilingual web: report on multilingualweb initiative
Authors: David Filip, Dave Lewis, Felix Sasaki
Venue: WWW
Year: 2012

We report on the MultilingualWeb initiative, a collaboration between the W3C Internationalization Activity and the European Commission, realized as a series of EC-funded projects. We review the outcomes of "MultilingualWeb", which conducted 4 workshops analyzing "gaps" within Web standardization that currently hinder multilinguality. Gap analysis led to formation of "MultilingualWeb-LT" - project and W3C Working Group with cross industry representation that will address priority issues via standardization of interoperability metadata.
Semantic navigation on the web of data: specification of routes, web fragments and actions
Authors: Valeria Fionda, Claudio Gutierrez, Giuseppe Pirrò
Venue: WWW
Year: 2012

The massive semantic data sources linked in the Web of Data give new meaning to old features like navigation; introduce new challenges like semantic specification of Web fragments; and make it possible to specify actions relying on semantic data. In this paper we introduce a declarative language to face these challenges. Based on navigational features, it is designed to specify fragments of the Web of Data and actions to be performed based on these data. We implement it in a centralized fashion, and show its power and performance. Finally, we explore the same ideas in a distributed setting, showing their feasibility, potentialities and challenges.
Mobile web applications: bringing mobile apps and web together
Authors: Marie-Claire Forgue, Dominique Hazaël-Massieux
Venue: WWW
Year: 2012

The popularity of mobile applications is very high and still growing rapidly. These applications allow their users to stay connected with a large number of service providers in seamless fashion, both for leisure and productivity. But service prThe popularity of mobile applications is very high and still growing rapidly. These applications allow their users to stay connected with a large number of service providers in seamless fashion, both for leisure and productivity. But service providers suffer from the high fragmentation of mobile development platforms that force them to develop, maintain and deploy their applications in a large number of versions and formats. The Mobile Web Applications (MobiWebApp [1]) EU project aims to build on Europe's strength in mobile technologies to enable European research and industry to strengthen its position in Web technologies to be active and visible on the mobile applications market.
Personalised placement in networked video
Authors: Jeremy D. Foss, Benedita Malheiro, Juan-Carlos Burguillo
Venue: WWW
Year: 2012

Personalised video can be achieved by inserting objects into a video play-out according to the viewer's profile. Content which has been authored and produced for general broadcast can take on additional commercial service features when personalised either for individual viewers or for groups of viewers participating in entertainment, training, gaming or informational activities. Although several scenarios and use-cases can be envisaged, we are focussed on the application of personalised product placement. Targeted advertising and product placement are currently garnering intense interest in the commercial networked media industries. Personalisation of product placement is a relevant and timely service for next generation online marketing and advertising and for many other revenue generating interactive services. This paper discusses the acquisition and insertion of media objects into a TV video play-out stream where the objects are determined by the profile of the viewer. The technology is based on MPEG-4 standards using object based video and MPEG-7 for metadata. No proprietary technology or protocol is proposed. To trade the objects into the video play-out, a Software-as-a-Service brokerage platform based on intelligent agent technology is adopted. Agencies, libraries and service providers are represented in a commercial negotiation to facilitate the contractual selection and usage of objects to be inserted into the video play-out.
The CUBRIK project: human-enhanced time-aware multimedia search
Authors: Piero Fraternali, Marco Tagliasacchi, Davide Martinenghi, Alessandro Bozzon, Ilio Catallo, Eleonora Ciceri, Francesco Saverio Nucci, Vincenzo Croce, Ismail Sengör Altingövde, Wolf Siberski, Fausto Giunchiglia, Wolfgang Nejdl, Martha A. Larson, Ebroul Izquierdo, Petros Daras, Otto Chrons, Ralph Traphöner, Björn Decker, John Lomas, Patrick Aichroth, Jasminko Novak, Ghislain Sillaume, Fernando Sánchez-Figueroa, Carolina Salas-Parra
Venue: WWW
Year: 2012

The Cubrik Project is an Integrated Project of the 7th Framework Programme that aims at contributing to the multimedia search domain by opening the architecture of multimedia search engines to the integration of open source and third party content annotation and query processing components, and by exploiting the contribution of humans and communities in all the phases of multimedia search, from content processing to query processing and relevance feedback processing. The CUBRIK presentation will showcase the architectural concept and scientific background of the project and demonstrate an initial scenario of human-enhanced content and query processing pipeline.
WISER: a web-based interactive route search system for smartphones
Authors: Roi Friedman, Itsik Hefez, Yaron Kanza, Roy Levin, Eliyahu Safra, Yehoshua Sagiv
Venue: WWW
Year: 2012

Many smartphones, nowadays, use GPS to detect the location of the user, and can use the Internet to interact with remote location-based services. These two capabilities support online navigation that incorporates search. In this demo we presents WISER---a system for Web-based Interactive Search en Route. In the system, users perform route search by providing (1) a target location, and (2) search terms that specify types of geographic entities to be visited.
The webinos project
Authors: Christian Fuhrhop, John Lyle, Shamal Faily
Venue: WWW
Year: 2012

This poster paper describes the webinos project and presents the architecture and security features developed in webinos. It highlights the main objectives and concepts of the project and describes the architecture derived to achive the objectives.
Automatically learning gazetteers from the deep web
Authors: Tim Furche, Giovanni Grasso, Giorgio Orsi, Christian Schallhart, Cheng Wang
Venue: WWW
Year: 2012

Wrapper induction faces a dilemma: To reach web scale, it requires automatically generated examples, but to produce accurate results, these examples must have the quality of human annotations. We resolve this conflict with AMBER, a system for fully automated data extraction from result pages. In contrast to previous approaches, AMBER employs domain specific gazetteers to discern basic domain attributes on a page, and leverages repeated occurrences of similar attributes to group related attributes into records rather than relying on the noisy structure of the DOM. With this approach AMBER is able to identify records and their attributes with almost perfect accuracy (>98%) on a large sample of websites. To make such an approach feasible at scale, AMBER automatically learns domain gazetteers from a small seed set. In this demonstration, we show how AMBER uses the repeated structure of records on deep web result pages to learn such gazetteers. This is only possible with a highly accurate extraction system. Depending on its parametrization, this learning process runs either fully automatically or with human interaction. We show how AMBER bootstraps a gazetteer for UK locations in 4 iterations: From a small seed sample we achieve 94.4% accuracy in recognizing UK locations in the $4th$ iteration.
Domain adaptive answer extraction for discussion boards
Authors: Ankur Gandhe, Dinesh Raghu, Rose Catherine
Venue: WWW
Year: 2012

Answer extraction from discussion boards is an extensively studied problem. Most of the existing work is focused on supervised methods for extracting answers using similarity features and forum-specific features. Although this works well for the domain or forum data that it has been trained on, it is difficult to use the same models for a domain where the vocabulary is different and some forum specific features may not be available. In this poster, we report initial results of a domain adaptive answer extractor that performs the extraction in two steps: a) an answer recognizer identifies the sentences in a post which are likely to be answers, and b) a domain relevance module determines the domain significance of the identified answer. We use domain independent methodology that can be easily adapted to any given domain with minimum effort.
FindiLike: preference driven entity search
Authors: Kavita Ganesan, ChengXiang Zhai
Venue: WWW
Year: 2012

Traditional web search engines enable users to find documents based on topics. However, in finding entities such as restaurants, hotels and products, traditional search engines fail to suffice as users are often interested in finding entities based on structured attributes such as price and brand and unstructured information such as opinions of other web users. In this paper, we showcase a preference driven search system, that enables users to find entities of interest based on a set of structured preferences as well as unstructured opinion preferences. We demonstrate our system in the context of hotel search.
Towards multiple identity detection in social networks
Authors: Kahina Gani, Hakim Hacid, Ryan Skraba
Venue: WWW
Year: 2012

In this paper we discuss a piece of work which intends to provide some insights regarding the resolution of the hard problem of multiple identities detection. Based on hypothesis that each person is unique and identifiable whether in its writing style or social behavior, we propose a Framework relying on machine learning models and a deep analysis of social interactions, towards such detection.
Link prediction via latent factor BlockModel
Authors: Sheng Gao, Ludovic Denoyer, Patrick Gallinari
Venue: WWW
Year: 2012

In this paper we address the problem of link prediction in networked data, which appears in many applications such as social network analysis or recommender systems. Previous studies either consider latent feature based models but disregarding local structure in the network, or focus exclusively on capturing local structure of objects based on latent blockmodels without coupling with latent characteristics of objects. To combine the benefits of previous work, we propose a novel model that can incorporate the effects of latent features of objects and local structure in the network simultaneously. To achieve this, we model the relation graph as a function of both latent feature factors and latent cluster memberships of objects to collectively discover globally predictive intrinsic properties of objects and capture latent block structure in the network to improve prediction performance. Extensive experiments on several real world datasets suggest that our proposed model outperforms the other state of the art approaches for link prediction.
How shall we catch people's concerns in micro-blogging?
Authors: Heng Gao, Qiudan Li, Hongyun Bao, Shuangyong Song
Venue: WWW
Year: 2012

In micro-blogging, people talk about their daily life and change minds freely, thus by mining people's interest in micro-blogging, we will easily perceive the pulse of society. In this paper, we catch what people are caring about in their daily life by discovering meaningful communities based on probabilistic factor model (PFM). The proposed solution identifies people's interest from their friendship and content information. Therefore, it reveals the behaviors of people in micro-blogging naturally. Experimental results verify the effectiveness of the proposed model and show people's social life vividly.
Using toolbar data to understand Yahoo!: answers usage
Authors: Giovanni Gardelli, Ingmar Weber
Venue: WWW
Year: 2012

We use Yahoo! Toolbar data to gain insights into why people use Q&A sites. We look at questions asked on Yahoo! Answers and analyze both the pre-question behavior of users as well as their general online behavior. Our results indicate that there is a one-dimensional spectrum of users ranging from "social users" to "informational users" and that web search and Q&A sites complement each other, rather than compete. Concerning the pre-question behavior, users who first issue a question-related query are more likely to issue informational questions, rather than conversational ones, and such questions are less likely to attract an answer. Finally, we only find weak evidence for topical congruence between a user's questions and his web queries.
Why do you ask this?
Authors: Giovanni Gardelli, Ingmar Weber
Venue: WWW
Year: 2012

We use Yahoo!~Toolbar data to gain insights into why people use Q&A sites. For this purpose we look at tens of thousands of questions asked on both Yahoo!~Answers and on Wiki Answers. We analyze both the pre-question behavior of users as well as their general online behavior. Using an existing approach (Harper et al.), we classify questions into "informational" vs. "conversational". Finally, for a subset of users on Yahoo! Answers we also integrate age and gender into our analysis. Our results indicate that there is a one-dimensional spectrum of users ranging from "social users" to "informational users". In terms of demographics, we found that both younger and female users are more "social" on this scale, with older and male users being more "informational".
SnoopyTagging: recommending contextualized tags to increase the quality and quantity of meta-information
Authors: Wolfgang Gassler, Eva Zangerle, Martin Bürgler, Günther Specht
Venue: WWW
Year: 2012

Current mass-collaboration platforms use tags to annotate and categorize resources enabling eective search capabilities. However, as tags are freely chosen keywords, the resulting tag vocabulary is very heterogeneous. Another shortcoming of simple tags is that they do not allow for a specification of context to create meaningful metadata. In this paper we present the SnoopyTagging approach which supports the user in the process of creating contextualized tags while at the same time decreasing the heterogeneity of the tag vocabulary by facilitating intelligent self-learning recommendation algorithms.
Optimizing user exploring experience in emerging e-commerce products
Authors: Xiubo Geng, Xin Fan, Jiang Bian, Xin Li, Zhaohui Zheng
Venue: WWW
Year: 2012

E-commerce has emerged as a popular channel for Web users to conduct transaction over Internet. In e-commerce services, users usually prefer to discover information via querying over category browsing, since the hierarchical structure supported by category browsing can provide them a more effective and efficient way to find their interested properties. However, in many emerging e-commerce services, well-defined hierarchical structures are not always available; moreover, in some other e-commerce services, the pre-defined hierarchical structures are too coarse and less intuitive to distinguish properties according to users interests. This will lead to very bad user experience. In this paper, to address these problems, we propose a hierarchical clustering method to build the query taxonomy based on users' exploration behavior automatically, and further propose an intuitive and light-weight approach to construct browsing list for each cluster to help users discover interested items. The advantage of our approach is four folded. First, we build a hierarchical taxonomy automatically, which saves tedious human effort. Second, we provide a fine-grained structure, which can help user reach their interested items efficiently. Third, our hierarchical structure is derived from users' interaction logs, and thus is intuitive to users. Fourth, given the hierarchical structures, for each cluster, we present both frequently clicked items and retrieved results of queries in the category, which provides more intuitive items to users. We evaluate our work by applying it to the exploration task of a real-world e-commerce service, i.e. online shop for smart mobile phone's apps. Experimental results show that our clustering algorithm is efficient and effective to assist users to discover their interested properties, and further comparisons illustrate that the hierarchical topic browsing performs much better than existing category browsing approach (i.e. Android Market mobile apps category) in terms of information exploration.
Understanding and combating link farming in the twitter social network
Authors: Saptarshi Ghosh, Bimal Viswanath, Farshad Kooti, Naveen Kumar Sharma, Gautam Korlam, Fabrício Benevenuto, Niloy Ganguly, P. Krishna Gummadi
Venue: WWW
Year: 2012

Recently, Twitter has emerged as a popular platform for discovering real-time information on the Web, such as news stories and people’s reaction tothem. Like theWeb, Twitter has become a target for link farming, where users, especially spammers, try to acquire large numbers of follower links in the social network. Acquiring followers not only increases the size of a user’s direct audience, but also contributes to the perceived influence of the user, which in turn impacts the ranking of the user’s tweets by search engines. In this paper, we first investigate link farming in the Twitter network and then explore mechanisms to discourage the activity. To this end, we conducted a detailed analysis of links acquired by over 40,000 spammer accounts suspended by Twitter. We find that link farming is wide spread and that a majority of spammers ’ links are farmed from a small fraction of Twitter users, the social capitalists, who are themselves seeking to amass social capital and links by following back anyone who follows them. Our findings shed light on the social dynamics that are at the root of the link farming problem in Twitter network and they have important implications for future designs of link spam defenses. In particular, we show that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the problem by disincentivizing users from linking with other users simply to gain influence. Categories andSubject Descriptors H.3.5 [Online Information Services]: Web-based services;