OpenRaiser/Intern-Atlas huggingface dataset:
Comparing web feeds and tweets for emergency management
Authors: Robert Power, Bella Robinson, Catherine Wise
Venue: WWW
Year: 2013

This paper describes ongoing work with the Australian Government to assemble information from a collection of web feeds describing emergency incidents of interest for emergency managers. The developed system, the Emergency Response Intelligence Capability (ERIC) tool, has been used to gather information about emergency events during the Australian summer of 2012/13. The web feeds are an authoritative source of structured information summarising incidents that includes links to emergency services web sites containing further details about the events underway.
Utility discounting explains informational website traffic patterns before a hurricane
Authors: Ben Priest, Kevin Gold
Venue: WWW
Year: 2013

We demonstrate that psychological models of utility discounting can explain the pattern of increased hits to weather websites in the days preceding a predicted weather disaster. We parsed the HTTP request lines issued by the web proxy for a mid-sized enterprise leading up to a hurricane, filtering for visits to weather-oriented websites. We fit four discounting models to the observed activity and found that our data matched hyperboloid models extending hyperbolic discounting.
Predictive web automation assistant for people with vision impairments
Authors: Yury Puzis, Yevgen Borodin, Rami Puzis, I. V. Ramakrishnan
Venue: WWW
Year: 2013

The Web is far less usable and accessible for people with vision impairments than it is for sighted people. Web automation, a process of automating browsing actions on behalf of the user, has the potential to bridge the divide between the ways sighted and people with vision impairment access the Web; specifically, it can enable the latter to breeze through web browsing tasks that beforehand were slow, hard, or even impossible to accomplish. Typical web automation requires that the user record a macro, a sequence of browsing steps, so that these steps can be automated in the future by replaying the macro. However, for people with vision impairment, automation with macros is not usable.
Urban: crowdsourcing for the good of London
Authors: Daniele Quercia
Venue: WWW
Year: 2013

For the last few years, we have been studying existing social media sites and created new ones in the context of London. By combining what Twitter users in a variety of London neighborhoods talk about with census data, we showed that neighborhood deprivation was associated (positively and negatively) with use of emotion words (sentiment) [2] and with specific topics [5]. Users in more deprived neighborhoods tweeted about wedding parties, matters expressed in Spanish/Portuguese, and celebrity gossips. By contrast, those in less deprived neighborhoods tweeted about vacations, professional use of social media, environmental issues, sports, and health issues. Also, upon data about 76 million London underground and overground rail journeys, we found that people from deprived areas visited both other deprived areas and prosperous areas, while residents of better-off communities tended to only visit other privileged neighborhoods - suggesting a geographic segregation effect [1, 6]. More recently, we created and launched two crowdsourcing websites. First, we launched urbanopticon.org, which extracts Londoners' mental images of the city. By testing which places are remarkable and unmistakable and which places represent faceless sprawl, we were able to draw the recognizability map of London. We found that areas with low recognizability did not fare any worse on the economic indicators of income, education, and employment, but they did significantly suffer from social problems of housing deprivation, poor living conditions, and crime [4]. Second, we launched urbangems.org. This crowdsources visual perceptions of quiet, beauty and happiness across the city using Google Street View pictures.
Psychological maps 2.0: a web engagement enterprise starting in London
Authors: Daniele Quercia, João Paulo Pesce, Virgílio A. F. Almeida, Jon Crowcroft
Venue: WWW
Year: 2013

Planners and social psychologists have suggested that the recognizability of the urban environment is linked to people's socio-economic well-being. We build a web game that puts the recognizability of London's streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram's). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where "web science" meets the "smart city" agenda.
REDACT: a framework for sanitizing RDF data
Authors: Jyothsna Rachapalli, Vaibhav Khadilkar, Murat Kantarcioglu, Bhavani Thuraisingham
Venue: WWW
Year: 2013

Resource Description Framework (RDF) is the foundational data model of the Semantic Web, and is essentially designed for integration of heterogeneous data from varying sources. However, lack of security features for managing sensitive RDF data while sharing may result in privacy breaches, which in turn, result in loss of user trust. Therefore, it is imperative to provide an infrastructure to secure RDF data. We present a set of graph sanitization operations that are built as an extension to SPARQL. These operations allow one to sanitize sensitive parts of an RDF graph and further enable one to build more sophisticated security and privacy features, thus allowing RDF data to be shared securely.
Spanish knowledge base generation for polarity classification from masses
Authors: Arturo Montejo-Ráez, Manuel Carlos Díaz-Galiano, José Manuel Perea Ortega, Luis Alfonso Ureña López
Venue: WWW
Year: 2013

This work presents a novel method for the generation of a knowledge base oriented to Sentiment Analysis from the continuous stream of published micro-blogs in social media services like Twitter. The method is simple in its approach and has shown to be effective compared to other knowledge based methods for Polarity Classification. Due to independence from language, the method has been tested on different Spanish corpora, with a minimal effort in the lexical resources involved. Although for two of the three studied corpora the obtained results did not improve those officially obtained on the same corpora, it should be noted that this is an unsupervised approach and the accuracy levels achieved were close to those levels obtained with well-known supervised algorithms.
Scalable processing of flexible graph pattern queries on the cloud
Authors: Padmashree Ravindra, Kemafor Anyanwu
Venue: WWW
Year: 2013

Flexible exploration of large RDF datasets with unknown relationships can be enabled using 'unbound-property' graph pattern queries. Relational-style processing of such queries using normalized relations results in redundant information in intermediate results due to the repetition of adjoining bound (fixed) properties. Such redundancy negatively impacts the disk I/O, network transfer costs, and the required disk space while processing RDF query workloads on MapReduce-based systems. This work proposes packing and lazy unpacking strategies to minimize the redundancy in intermediate results while processing unbound-property queries. In addition to keeping the results compact, this work evaluates RDF queries using the Nested TripleGroup Data Model and Algebra (NTGA) that enables shorter MapReduce execution workflows. Experimental results demonstrate the benefit of this work over RDF query processing using relational-style systems such as Apache Pig and Hive.
A sensitive Twitter earthquake detector
Authors: Bella Robinson, Robert Power, Mark A. Cameron
Venue: WWW
Year: 2013

This paper describes early work at developing an earthquake detector for Australia and New Zealand using Twitter. The system is based on the Emergency Situation Awareness (ESA) platform which provides all-hazard information captured, filtered and analysed from Twitter. The detector sends email notifications of evidence of earthquakes from Tweets to the Joint Australian Tsunami Warning Centre.
uTrack: track yourself! monitoring information on online social media
Authors: Tiago Rodrigues, Prateek Dewan, Ponnurangam Kumaraguru, Raquel Cardoso de Melo Minardi, Virgílio A. F. Almeida
Venue: WWW
Year: 2013

The past one decade has witnessed an astounding outburst in the number of online social media (OSM) services, and a lot of these services have enthralled millions of users across the globe. With such tremendous number of users, the amount of content being generated and shared on OSM services is also enormous. As a result, trying to visualize all this overwhelming amount of content, and gain useful insights from it has become a challenge. In this work, we present uTrack, a personalized web service to analyze and visualize the diffusion of content shared by users across multiple OSM platforms. To the best of our knowledge, there exists no work which concentrates on monitoring information diffusion for personal accounts. Currently, uTrack monitors and supports logging in from Facebook, Twitter, and Google+. Once granted permissions by the user, uTrack monitors all URLs (like videos, photos, news articles) the user has shared in all OSM services supported, and generates useful visualizations and statistics from the collected data.
Linking data in and outside a scientific publishing house
Authors: Sweitze Roffel
Venue: WWW
Year: 2013

Publishing has undergone many changes since the 1960's, often driven by rapid technological development. Technology impacts the creation and dissemination of knowledge only to a certain extent, and in this talk I'll try to give a publisher's perspective of some technological drivers impacting Academic publishing today, and how the many actors involved are learning to cooperate as well as compete in an increasingly distributed environment to better turn information into knowledge. Technically, organizationally, and with regard to shared standards and infrastructure.
Observing social machines part 1: what to observe?
Authors: David De Roure, Clare Hooper, Megan Meredith-Lobay, Kevin R. Page, Ségolène M. Tarte, Don Cruickshank, Catherine De Roure
Venue: WWW
Year: 2013

As a scoping exercise in the design of our Social Machines Observatory we consider the observation of Social Machines "in the wild", as illustrated through two scenarios. More than identifying and classifying individual machines, we argue that we need to study interactions between machines and observe them throughout their lifecycle. We suggest that purpose may be a key notion to help identify individual Social Machines in composed systems, and that mixed observation methods will be required. This exercise provides a basis for later work on how we instrument and observe the ecosystem.
Lists as coping strategy for information overload on Twitter
Authors: Simon de la Rouviere, Kobus Ehlers
Venue: WWW
Year: 2013

When following too many users on microblogging services, information overload occurs due to increased and varied communication activity. Users then either leave, or employ coping strategies to continue benefiting from the service. Through a crawl of 31 684 random users from Twitter and a qualitative survey with 115 respondents, it has been determined that by using lists as an information management coping strategy (filtering and compartmentalising varied communication activity), users are capable of following more users and experience fewer symptoms of information overload.
Analyzing linguistic structure of web search queries
Authors: Rishiraj Saha Roy
Venue: WWW
Year: 2013

It is believed that Web search queries are becoming more structurally complex over time. However, there has been no systematic study that quantifies such characteristics. In this thesis, we propose that queries are evolving into a unique linguistic system. We demonstrate proof of this hypothesis by examining the structure of Web queries by applying well-established techniques from natural language understanding. Preliminary results of these experiments show quantitative and qualitative proof that queries are not just some form of text between random sequences of words and natural language - they have distinct properties of their own.
Place value: word position shifts vital to search dynamics
Authors: Rishiraj Saha Roy, Anusha Suresh, Niloy Ganguly, Monojit Choudhury
Venue: WWW
Year: 2013

With fast changing information needs in today's world, it is imperative that search engines precisely understand and exploit temporal changes in Web queries. In this work, we look at shifts in preferred positions of segments in queries over an interval of four years. We find that such shifts can predict key changes in usage patterns, and explain the observed increase in query lengths. Our findings indicate that recording positional statistics can be vital for understanding user intent in Web search queries.
Revised mutual information approach for german text sentiment classification
Authors: Farag Saad, Brigitte Mathiak
Venue: WWW
Year: 2013

The significant increase in content of online social media such as product reviews, blogs, forums etc., have led to an increasing attention to sentiment analysis tools and approaches that make use of mining this substantially growing content. The aim of this paper is to develop a robust classification approach of customer reviews based on a self-annotated domain-specific corpus by applying a statistical approach i.e., mutual information. First, subjective words in each test sentence are identified. Second, ambiguous adjectives such as high, low, large, many etc., are disambiguated based on their accompanying noun using a conditional mutual information approach. Third, a mutual information approach is applied to find the sentiment orientation (polarity) of the identified subjective words based on analyzing their statistical relationship with the manually annotated sentiment labels within a sizeable sentiment training data. Fourth, since negation plays a significant role in flipping the sentiment polarity of an identified sentiment word, we estimate the role of negation in affecting the classification accuracy. Finally, the identified polarity for each test sentence is evaluated against experts' annotation.
Collective matrix factorization for co-clustering
Authors: Mrinmaya Sachan, Shashank Srivastava
Venue: WWW
Year: 2013

We outline some matrix factorization approaches for co- clustering polyadic data (like publication data) using non-negative factorization (NMF). NMF approximates the data as a product of non-negative low-rank matrices, and can induce desirable clustering properties in the matrix factors through a flexible range of constraints. We show that simultaneous factorization of one or more matrices provides potent approaches for co-clustering.
Regional analysis of user interactions on social media in times of disaster
Authors: Takeshi Sakaki, Fujio Toriumi, Kousuke Shinoda, Kazuhiro Kazama, Satoshi Kurihara, Itsuki Noda, Yutaka Matsuo
Venue: WWW
Year: 2013

Social media attract attention for sharing information, especially Twitter, which is now being used in times of disasters. In this paper, we perform regional analysis of user interactions on Twittter during the Great East Japan Earthquake and arrived at the following two conclusions:People diffused much more information after the earthquake, especially in the heavily-damaged areas; People communicated with nearby users but diffused information posted by distant users. We conclude that social media users changed their behavior to widely diffuse information.
Carbon dating the web: estimating the age of web resources
Authors: Hany SalahEldeen, Michael L. Nelson
Venue: WWW
Year: 2013

In the course of web research it is often necessary to estimate the creation datetime for web resources (in the general case, this value can only be estimated). While it is feasible to manually establish likely datetime values for small numbers of resources, this becomes infeasible if the collection is large. We present "carbon date", a simple web application that estimates the creation date for a URI by polling a number of sources of evidence and returning a machine-readable structure with their respective values. To establish a likely datetime, we poll bitly for the first time someone shortened the URI, topsy for the first time someone tweeted the URI, a Memento aggregator for the first time it appeared in a public web archive, Google's time of last crawl, and the Last-Modified HTTP response header of the resource itself. We also examine the backlinks of the URI as reported by Google and apply the same techniques for the resources that link to the URI. We evaluated our tool on a gold standard data set of 1200 URIs in which the creation date was manually verified. We were able to estimate a creation date for 75.90% of the resources, with 32.78% having the correct value. Given the different nature of the URIs, the union of the various methods produces the best results. While the Google last crawl date and topsy account for nearly 66% of the closest answers, eliminating the web archives or Last-Modified from the results produces the largest overall negative impact on the results. The carbon date application is available for download or use via a web API.
History-aware critiquing-based conversational recommendation
Authors: Yasser Salem, Jun Hong
Venue: WWW
Year: 2013

In this paper we present a new approach to critiquing-based conversational recommendation, which we call History-Aware Critiquing (HAC). It takes a case-based reasoning approach by reusing relevant recommendation sessions of past users to short-cut the recommendation session of the current user. It selects relevant recommendation sessions from a case base that contains the successful recommendation sessions of past users. A past recommendation session can be selected if it contains similar recommended items to the ones in the current session and its critiques sufficiently overlap with the critiques so far in the current session. HAC extends experience-based critiquing (EBC).
Solving electrical networks to incorporate supervision in random walks
Authors: Mrinmaya Sachan, Dirk Hovy, Eduard H. Hovy
Venue: WWW
Year: 2013

Random walks is one of the most popular ideas in computer science. A critical assumption in random walks is that the probability of the walk being at a given vertex at a time instance converges to a limit independent of the start state. While this makes it computationally efficient to solve, it limits their use to incorporate label information. In this paper, we exploit the connection between Random Walks and Electrical Networks to incorporate label information in classification, ranking, and seed expansion.
Exploring student predictive model that relies on institutional databases and open data instead of traditional questionnaires
Authors: Farhana Sarker, Thanassis Tiropanis, Hugh C. Davis
Venue: WWW
Year: 2013

Research in student retention and progression to completion is traditionally survey-based, where researchers collect data through questionnaires and interviewing students. The major issues with survey-based study are the potentially low response rates and cost. Nevertheless, a large number of datasets that could inform the questions that students are explicitly asked in surveys is commonly available in the external open datasets. This paper describes a new student predictive model for student progression that relies on the data available in institutional internal databases and external open data, without the need for surveys. The results of empirical study for undergraduate students in their first year of study shows that this model can perform as well as or even out-perform traditional survey-based ones.
Real-time user modeling and prediction: examples from youtube
Authors: Ramesh R. Sarukkai
Venue: WWW
Year: 2013

Real-time analysis and modeling of users for improving engagement, and interaction is a burgeoning area of interest with applications to web sites, social networks and mobile applications. Apart from scalability issues, this domain poses a number of modeling and algorithmic challenges. In this talk, as an illustrative example, we present DAL, a system that leverages real-time user activity/signals for dynamic ad loads, and designed to improve the overall user experience on YouTube. This system uses machine learning to optimize for user activity during a visit and helps decide on real-time advertising policies dynamically for the user. We conclude the talk with challenges and opportunities in this important area of real-time user analysis and social modeling.
A model for green design of online news media services
Authors: Daniel Schien, Paul Shabajee, Stephen G. Wood, Chris Preist
Venue: WWW
Year: 2013

The use of information and communication technology and the web-based products it provides is responsible for significant emissions of greenhouse gases. In order to enable the reduction of emissions during the design of such products, it is necessary to estimate as accurately as possible their carbon impact over the entire product system. In this work we describe a new method which combines models of energy consumption during the use of digital media with models of the behavior of the audience. We apply this method to conduct an assessment of the annual carbon emissions for the product suite of a major international news organization. We then demonstrate its use for green design by evaluating the impacts of five different interventions on the product suite. We find that carbon footprint of the online newspaper amounts to approximately 7700 tCO2e per year, of which 75% are caused by the user devices. Among the evaluated scenarios a significant uptake of eReaders in favor of PCs has the greatest reduction potential. Our results also show that even a significant reduction of data volume on a web page would only result in small overall energy savings.
Social media, journalism and the public
Authors: Steve Schifferes
Venue: WWW
Year: 2013

This paper draws on the parallels between the current period and other periods of historic change in journalism to examine what is new in today's world of social media and what continuities there are with the past. It examines the changing relationship between the public and the press and how it is being continuously reinterpreted. It addresses the questions of whether we are the beginning or end of a process of revolutionary media change.
Verifying news on the social web: challenges and prospects
Authors: Steve Schifferes, Nic Newman
Venue: WWW
Year: 2013

The problem of verification is the key issue for journalists who use social media. This paper argues for the importance of a user-centered approach in finding solutions to this problem. Because journalists have different needs for different types of stories, there is no one magic bullet that can verify social media. Any tool will need to have a multi-faceted approach to the problem, and will have to be adjustable to suit the particular needs of individual journalists and news organizations.