OpenRaiser/Intern-Atlas huggingface dataset:
Ad-hoc ride sharing application using continuous SPARQL queries
Authors: Debnath Mukherjee, Snehasis Banerjee, Prateep Misra
Venue: WWW
Year: 2012

In the existing ride sharing scenario, the ride taker has to cope with uncertainties since the ride giver may be delayed or may not show up due to some exigencies. A solution to this problem is discussed in this paper. The solution framework is based on gathering information from multiple streams such as traffic status on the ride giver's routes and the ride giver's GPS coordinates. Also, it maintains a list of alternative ride givers so as to almost guarantee a ride for the ride taker. This solution uses a SPARQL-based continuous query framework that is capable of sensing fast-changing real-time situation. It also has reasoning capabilities for handling ride taker's preferences. The paper introduces the concept of user-managed windows that is shown to be required for this solution. Finally we show that the performance of the application is enhanced by designing the application with short incremental queries.
Tweeting about the tsunami?: mining twitter for information on the tohoku earthquake and tsunami
Authors: Akiko Murakami, Tetsuya Nasukawa
Venue: WWW
Year: 2012

On 11th March 2011, a 9.0-magnitude megathrust earthquake occurred in the ocean near Japan. This was the first large-scale natural disaster in Japan since the broad adoption of social media tools (such as Facebook and Twitter). In particular, Twitter is suitable for broadcasting information, naturally making it the most frequently used social medias when disasters strike. This paper presents a topical analysis using text mining tools and shows the tools' effectiveness for the analysis of social media data analysis after a disaster. Though an ad hoc system without prepared resources was useful, an improved system with some syntactic pattern dictionaries showed better results.
Characterization of social media response to natural disasters
Authors: Seema Nagar, Aaditeshwar Seth, Anupam Joshi
Venue: WWW
Year: 2012

Online social networking websites such as Twitter and Facebook often serve a breaking-news role for natural disasters: these websites are among the first ones to mention the news, and because they are visited by millions of users regularly the websites also help communicate the news to a large mass of people. In this paper, we examine how news about these disasters spreads on the social network. In addition to this, we also examine the countries of the Tweeting users. We examine Twitter logs from the 2010 Philippines typhoon, the 2011 Brazil flood and the 2011 Japan earthquake. We find that although news about the disaster may be initiated in multiple places in the social network, it quickly finds a core community that is interested in the disaster, and has little chance to escape the community via social network links alone. We also find evidence that the world at large expresses concern about such largescale disasters, and not just countries geographically proximate to the epicenter of the disaster. Our analysis has implications for the design of fund raising campaigns through social networking websites.
A unified approach to learning task-specific bit vector representations for fast nearest neighbor search
Authors: Vinod Nair, Dhruv Kumar Mahajan, Sundararajan Sellamanickam
Venue: WWW
Year: 2012

Fast nearest neighbor search is necessary for a variety of large scale web applications such as information retrieval, nearest neighbor classification and nearest neighbor regression. Recently a number of machine learning algorithms have been proposed for representing the data to be searched as (short) bit vectors and then using hashing to do rapid search. These algorithms have been limited in their applicability in that they are suited for only one type of task -- e.g. Spectral Hashing learns bit vector representations for retrieval, but not say, classification. In this paper we present a unified approach to learning bit vector representations for many applications that use nearest neighbor search. The main contribution is a single learning algorithm that can be customized to learn a bit vector representation suited for the task at hand. This broadens the usefulness of bit vector representations to tasks beyond just conventional retrieval. We propose a learning-to-rank formulation to learn the bit vector representation of the data. LambdaRank algorithm is used for learning a function that computes a task-specific bit vector from an input data vector. Our approach outperforms state-of-the-art nearest neighbor methods on a number of real world text and image classification and retrieval datasets. It is scalable and learns a 32-bit representation on 1.46 million training cases in two days.
Predicting information diffusion on social networks with partial knowledge
Authors: Anis Najar, Ludovic Denoyer, Patrick Gallinari
Venue: WWW
Year: 2012

Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions are nonrealistic for many propagation processes extracted from Social Websites. We address the problem of predicting information propagation when the network diffusion structure is unknown and without making any closed world assumption. Instead of modeling a diffusion process, we propose to directly predict the final propagation state of the information over a whole user set. We describe a general model, able to learn predicting which users are the most likely to be contaminated by the information knowing an initial state of the network. Different instances are proposed and evaluated on artificial datasets.
QAque: faceted query expansion techniques for exploratory search using community QA resources
Authors: Atsushi Otsuka, Yohei Seki, Noriko Kando, Tetsuji Satoh
Venue: WWW
Year: 2012

Recently, query suggestions have become quite useful in web searches. Most provide additional and correct terms based on the initial query entered by users. However, query suggestions often recommend queries that differ from the user's search intentions due to different contexts. In such cases, faceted query expansions and their usages are quite efficient. In this paper, we propose faceted query expansion methods using the resources of Community Question Answering (CQA), which is social network service (SNS) that shares user knowledge. In a CQA site, users can post questions in a suitable category. Others answer them based on the category framework. Thus, the CQA "category" makes a "facet" of the query expansion. In addition, the time of year when the question was posted plays an important role in understanding its context. Thus, such seasonality creates another "facet" of the query expansion. We implement two-dimensional faceted query expansion methods based on the results of the Latent Dirichlet Allocation (LDA) analysis of CQA resources. The question articles deriving query expansion are provided for choosing appropriate terms by users. Our sophisticated evaluations using actual and long-term CQA resources, such as "Yahoo! CHIEBUKURO," demonstrate that most parts of the CQA questions are posted in periodicity and in bursts.
Information integration over time in unreliable and uncertain environments
Authors: Aditya Pal, Vibhor Rastogi, Ashwin Machanavajjhala, Philip Bohannon
Venue: WWW
Year: 2012

Often an interesting true value such as a stock price, sports score, or current temperature is only available via the observations of noisy and potentially conflicting sources. Several techniques have been proposed to reconcile these conflicts by computing a weighted consensus based on source reliabilities, but these techniques focus on static values. When the real-world entity evolves over time, the noisy sources can delay, or even miss, reporting some of the real-world updates. This temporal aspect introduces two key challenges for consensus-based approaches: (i) due to delays, the mapping between a source's noisy observation and the real-world update it observes is unknown, and (ii) missed updates may translate to missing values for the consensus problem, even if the mapping is known. To overcome these challenges, we propose a formal approach that models the history of updates of the real-world entity as a hidden semi-Markovian process (HSMM). The noisy sources are modeled as observations of the hidden state, but the mapping between a hidden state (i.e. real-world update) and the observation (i.e. source value) is unknown. We propose algorithms based on Gibbs Sampling and EM to jointly infer both the history of real-world updates as well as the unknown mapping between them and the source values. We demonstrate using experiments on real-world datasets how our history-based techniques improve upon history-agnostic consensus-based approaches.
Social media meta-API: leveraging the content of social networks
Authors: George Papadakis, Konstantinos Tserpes, Emmanuel Sardis, Magdalini Kardara, Athanasios Papaoikonomou, Fotis Aisopos
Venue: WWW
Year: 2012

Social Network (SN) environments are the ideal future service marketplaces. It is well known and documented that SN users are increasing at a tremendous pace. Taking advantage of these social dynamics as well as the vast volumes, of amateur content generated every second, is a major step towards creating a potentially huge market of services. In this paper, we describe the external web services that SocIoS project is researching and developing, and will support with the Social Media community. Aiming to support the end users of SNs, to enhance their transactions with more automated ways, and with the advantage for better production and performance in their workflows over SNs inputs and content, this work presents the main architecture, functionality, and benefits per external service. Finally, introduces the end user, into the new era of SNs with business applicability and better social transactions over SNs content.
H2RDF: adaptive query processing on RDF data in the cloud
Authors: Nikolaos Papailiou, Ioannis Konstantinou, Dimitrios Tsoumakos, Nectarios Koziris
Venue: WWW
Year: 2012

In this work we present H2RDF, a fully distributed RDF store that combines the MapReduce processing framework with a NoSQL distributed data store. Our system features two unique characteristics that enable efficient processing of both simple and multi-join SPARQL queries on virtually unlimited number of triples: Join algorithms that execute joins according to query selectivity to reduce processing; and adaptive choice among centralized and distributed (MapReduce-based) join execution for fast query responses. Our system efficiently answers both simple joins and complex multivariate queries and easily scales to 3 billion triples using a small cluster of 9 worker nodes. H2RDF outperforms state-of-the-art distributed solutions in multi-join and nonselective queries while achieving comparable performance to centralized solutions in selective queries. In this demonstration we showcase the system's functionality through an interactive GUI. Users will be able to execute predefined or custom-made SPARQL queries on datasets of different sizes, using different join algorithms. Moreover, they can repeat all queries utilizing a different number of cluster resources. Using real-time cluster monitoring and detailed statistics, participants will be able to understand the advantages of different execution schemes versus the input data as well as the scalability properties of H2RDF over both the data size and the available worker resources.
C4PS: colors for privacy settings
Authors: Thomas Paul, Martin Stopczynski, Daniel Puscher, Melanie Volkamer, Thorsten Strufe
Venue: WWW
Year: 2012

The ever increasing popularity of Facebook and other Online Social Networks has left a wealth of personal and private data on the web, aggregated and readily accessible for broad and automatic retrieval. Protection from both undesired recipients and harvesting by crawlers is implemented by access control, manually configured by the user and owner of the data. Several studies demonstrate that default settings cause an unnoticed over-sharing and that users have trouble understanding and configuring adequate privacy settings. We developed an improved interface for privacy settings in Facebook by mainly applying color coding for different groups, providing easy access to the privacy settings, and applying the principle of common practices. Using a lab study, we show that the new approach increases the usability significantly.
Collective intelligence as a source for machine learning self-supervision
Authors: Saulo D. de S. Pedro, Estevam R. Hruschka Jr.
Venue: WWW
Year: 2012

The recent growth of virtual communities, social web and information sharing gives to information retrieval and machine learning systems a source of information referred as the "wisdom of crowds". In this work we show that this information could be used not only as a source of knowledge but as a way to bring intelligent systems closer to users by using their opinion as part of the knowledge acquisition/validation allowing self-supervision. For that we have implemented a validation system for the NELL (Never-Ending Language Learner) system using the question answering platform given by the Yahoo! Answers web community. Moreover, we focus in this paper, in the validation of first order rules induced by NELL using its Rule Learning (RL) algorithm. This paper presents the main motivations for using a QA forum instead of other web-based validation sources; describes the proposed approach with a "Macro QA"-based component named SS-Crowd (self-supervisor agent based on the wisdom of crowds) and brings and discusses the obtained results and how they can impact in a never-ending learning system like NELL in which self-supervision plays a crucial role.
Lightweight automatic face annotation in media pages
Authors: Dmitri Perelman, Edward Bortnikov, Ronny Lempel, Roman Sandler
Venue: WWW
Year: 2012

Labeling human faces in images contained in Web media stories enables enriching the user experience offered by media sites. We propose a lightweight framework for automatic image annotation that exploits named entities mentioned in the article to significantly boost the accuracy of face recognition. While previous works in the area labor to train comprehensive offline visual models for a pre-defined universe of candidates, our approach models the people mentioned in a given story on the y, using a standard Web image search engine as an image sampling mechanism. We overcome multiple sources of noise introduced by this ad-hoc process, to build a fast and robust end-to-end system from off-the-shelf error-prone text analysis and machine vision components. In experiments conducted on approximately 900 faces depicted in 500 stories from a major celebrity news website, we were able to correctly label 81.5% of the faces while mislabeling 14.8% of them.
k-Centralities: local approximations of global measures based on shortest paths
Authors: Jürgen Pfeffer, Kathleen M. Carley
Venue: WWW
Year: 2012

A lot of centrality measures have been developed to analyze different aspects of importance. Some of the most popular centrality measures (e.g. betweenness centrality, closeness centrality) are based on the calculation of shortest paths. This characteristic limits the applicability of these measures for larger networks. In this article we elaborate on the idea of bounded-distance shortest paths calculations. We claim criteria for k-centrality measures and we introduce one algorithm for calculating both betweenness and closeness based centralities. We also present normalizations for these measures. We show that k-centrality measures are good approximations for the corresponding centrality measures by achieving a tremendous gain of calculation time and also having linear calculation complexity O(n) for networks with constant average degree. This allows researchers to approximate centrality measures based on shortest paths for networks with millions of nodes or with high frequency in dynamically changing networks.
Paraimpu: a platform for a social web of things
Authors: Antonio Pintus, Davide Carboni, Andrea Piras
Venue: WWW
Year: 2012

The Web of Things is a scenario where potentially billions of connected smart objects communicate using the Web protocols, HTTP in primis. A Web of Things envisioning and design has raised several research issues, from protocols adoption and communication models to architectural styles and social aspects facing. In this demo we present the prototype of a scalable architecture for a large scale social Web of Things for smart objects and services, named Paraimpu. It is a Web-based platform which allows to add, use, share and inter-connect real HTTP-enabled smart objects and "virtual" things like services on the Web and social networks. Paraimpu defines and uses few strong abstractions, in order to allow mash-ups of heterogeneous things introducing powerful rules for data adaptation. Adding and inter-connecting objects is supported through user friendly models and features.
Modeling the flow and change of information on the web
Authors: Nataliia Pobiedina
Venue: WWW
Year: 2012

The proposed PhD work approaches the problem of information flow and change on the Web. To model temporal dynamics both of the Web structure and its content, the author proposes to apply the framework of stochastic graph transformation systems. This framework is currently widely used in software engineering and model checking. A quantitative and qualitative evaluation of the framework will be performed during a case study of the short-term temporal behavior of economics news on selected English news websites and blogs over selected time period.
Periodic transfers in mobile applications: network-wide origin, impact, and optimization
Authors: Feng Qian, Zhaoguang Wang, Yudong Gao, Junxian Huang, Alexandre Gerber, Zhuoqing Morley Mao, Subhabrata Sen, Oliver Spatscheck
Venue: WWW
Year: 2012

Cellular networks employ a specific radio resource management policy distinguishing them from wired and Wi-Fi networks. A lack of awareness of this important mechanism potentially leads to resource-inefficient mobile applications. We perform the first network-wide, large-scale investigation of a particular type of application traffic pattern called periodic transfers where a handset periodically exchanges some data with a remote server every t seconds. Using packet traces containing 1.5 billion packets collected from a commercial cellular carrier, we found that periodic transfers are very prevalent in today's smartphone traffic. However, they are extremely resource-inefficient for both the network and end-user devices even though they predominantly generate very little traffic. This somewhat counter-intuitive behavior is a direct consequence of the adverse interaction between such periodic transfer patterns and the cellular network radio resource management policy. For example, for popular smartphone applications such as Facebook, periodic transfers account for only 1.7% of the overall traffic volume but contribute to 30% of the total handset radio energy consumption. We found periodic transfers are generated for various reasons such as keep-alive, polling, and user behavior measurements. We further investigate the potential of various traffic shaping and resource control algorithms. Depending on their traffic patterns, applications exhibit disparate responses to optimization strategies. Jointly using several strategies with moderate aggressiveness can eliminate almost all energy impact of periodic transfers for popular applications such as Facebook and Pandora.
Automated semantic tagging of speech audio
Authors: Yves Raimond, Chris Lowis, Roderick Hodgson, Jonathan Tweed
Venue: WWW
Year: 2012

The BBC is currently tagging programmes manually, using DBpedia as a source of tag identifiers, and a list of suggested tags extracted from the programme synopsis. These tags are then used to help navigation and topic-based search of programmes on the BBC website. However, given the very large number of programmes available in the archive, most of them having very little metadata attached to them, we need a way to automatically assign tags to programmes. We describe a framework to do so, using speech recognition, text processing and concept tagging techniques. We describe how this framework was successfully applied to a very large BBC radio archive. We demonstrate an application using automatically extracted tags to aid discovery of archive content.
Extracting advertising keywords from URL strings
Authors: Santosh Raju, Raghavendra Udupa
Venue: WWW
Year: 2012

Extracting advertising keywords from web-pages is important in keyword-based online advertising. Previous works have attempted to extract advertising keywords from the whole content of a web-page. However, in some scenarios, it is necessary to extract keywords from just the URL string itself. In this work, we propose an algorithm for extracting advertising keywords from the URL string alone. Our algorithm has applications in contextual and paid search advertising. We evaluate the effectiveness of our algorithm on publisher URLs and show that it produces very good quality keywords that are comparable with keywords produced by page based extractors.
Analyzing patterns of information cascades based on users' influence and posting behaviors
Authors: Geerajit Rattanaritnont, Masashi Toyoda, Masaru Kitsuregawa
Venue: WWW
Year: 2012

Nowadays people can share useful information on social networking sites such as Facebook and Twitter. The information is spread over the networks when it is forwarded or copied repeatedly from friends to friends. This phenomenon is so called "information cascade", and has been studied long time since it sometimes has an impact on the real world. Various social activities tends to have different ways of cascade on the social networks. Our focus in this study is on characterizing the cascade patterns according to users' influence and posting behaviors in various topics. The cascade patterns could be useful for various organizations to consider the strategy of public relations activities. We explore four measures which are cascade ratio, tweet ratio, time of tweet, and exposure curve. Our results show that hashtags in different topics have different cascade patterns in term of these measures. However, some hashtags even in the same topic have different cascade patterns. We discover that such kind of hidden relationship between topics can be surprisingly revealed by using only our four measures rather than considering tweet contents. Finally, our results also show that cascade ratio and time of tweet are the most effective measures to distinguish cascade patterns in different topics.
Interpreting contact details out of e-mail signature blocks
Authors: Gaëlle Recourcé
Venue: WWW
Year: 2012

This paper describes a fully automated process of address book enrichment by means of information extraction in e-mail signature blocks. The main issues we tackle are signature block detection, named entites tagging, mapping with a specific person, standardizing the details and auto-updating of the address book. We adopted a symbolic approach for NLP modules. We describe how the process was designed to handle multiple-type of errors (human or computer-driven) while aiming at 100% precision rate. Last, we tackle the question of automatic updating confronted to users rights over their own data.