OpenRaiser/Intern-Atlas huggingface dataset:
System for surveillance and investigation of disease outbreaks
Authors: Deleer Barazanji, Pär Bjelkmar
Venue: WWW
Year: 2014

Information technology contributes greatly in improving people's health. Through our interaction with different communication channels such as social media, telephone calls or purchasing over-the-counter medicines, we emit signals and leave trails of information related to our health. Information that can be used to understand our health situation. Some of these communication channels are more structured, filtered and suited for evaluation, as a result they require less demanding filtration and analysis than others. One such channel is the Swedish National Telephone Health Service 1177, where professional healthcare personnel assist and give advice to callers. The main aim of this work is to detect point source outbreaks. For this purpose a project called Event-based Surveillance System (ESS) was initiated to develop a system for surveillance and detection using the former mentioned information source. The system is currently running and is used to notify local authorities whenever a deviation in the telephone traffic pattern is recorded.
Collective attention to social media evolves according to diffusion models
Authors: Christian Bauckhage, Kristian Kersting, Bashir Rastegarpanah
Venue: WWW
Year: 2014

We investigate patterns of adoption of 175 social media services and Web businesses using data from Google Trends. For each service, we collect aggregated search frequencies from 45 countries as well as global averages. This results in more than 8.000 time series which we analyze using economic diffusion models. The models are found to provide accurate and statistically significant fits to the data and show that collective attention to social media grows and subsides in a highly regular manner. Regularities persist across regions, cultures, and topics and thus hint at general mechanisms that govern the adoption of Web-based services.
YouTube4Two: socializing video platform for two co-present people
Authors: Alessio Bellino, Giorgio De Michelis, Flavio De Paoli
Venue: WWW
Year: 2014

YouTube4Two is an application that exploits the YouTube media library (through its API) to demonstrate a new style of social interaction. Two co-present people can share a video and act autonomously to navigate the related-video and comment lists, and search for videos. The novelty is that they can use their own smartphones connected via Internet to control the shared application. The application has been designed according to the responsive-web-design (RWD) principle to smoothly pass from desktop interface (controlled by mouse and keyboard) to smartphone interface (with touch control). YouTube4Two introduces the multi-device responsive Web design (MD-RWD) style that extends the RWD style by introducing the separation between displayed content (on a shared screen) and displayed control commands (on personal smartphones) to support shared control over an application.
Why not, WINE?
Authors: Sourav S. Bhowmick, Aixin Sun, Ba Quan Truong
Venue: WWW
Year: 2014

Despite considerable progress in recent years on Tag-based Social Image Retrieval (TagIR), state-of-the-art TagIR systems fail to provide a systematic framework for end users to ask why certain images are not in the result set of a given query and provide an explanation for such missing results. However, such why-not questions are natural when expected images are missing in the query results returned by a TagIR system. In this demonstration, we present a system called WINE (Why-not questIon aNswering Engine) which takes the first step to systematically answer the why-not questions posed by end-users on TagIR systems. It is based on three explanation models, namely result reordering, query relaxation, and query substitution, that enable us to explain a variety of why-not questions. Our answer not only involves the reason why desired images are missing in the results but also suggestion on how the search query can be altered so that the user can view these missing images in sufficient number.
LiveCities: revealing the pulse of cities by location-based social networks venues and users analysis
Authors: Alberto Del Bimbo, Andrea Ferracani, Daniele Pezzatini, Federico D&apos, Amato, Martina Sereni
Venue: WWW
Year: 2014

It would be very difficult even for a resident to characterise the social dynamics of a city and to reveal to foreigners the evolving activity patterns which occur in its various areas. To address this problem, however, large amount of data produced by location-based social networks (LBSNs) can be exploited and combined effectively with techniques of user profiling. The key idea we introduce in this demo is to improve city areas and venues classification using semantics extracted both from places and from the online profiles of people who frequent those places. We present the results of our methodology in LiveCities, a web application which shows the hidden character of several italian cities through clustering and information visualisations paradigms. In particular we give in-depth insights of the city of Florence, IT, for which the majority of the data in our dataset have been collected. The system provides personal recommendation of areas and venues matching user interests and allows the free exploration of urban social dynamics in terms of people lifestyle, business, demographics, transport etc. with the objective to uncover the real `pulse' of the city. We conducted a qualitative validation through an online questionnaire with 28 residents of Florence to understand the shared perception of city areas by its inhabitants and to check if their mental maps align to our results. Our evaluation shows how considering also contextual semantics like people profiles of interests in venues categorisation can improve clustering algorithms and give good insights of the endemic characteristics and behaviours of the detected areas.
Community structure for efficient information flow in 'ToS;DR', a social machine for parsing legalese
Authors: Reuben Binns, David Matthews
Venue: WWW
Year: 2014

This paper presents a case study of 'Terms-of-Service; Didn't Read', a social machine to curate, parse, and rate website terms and privacy policies. We examine the relationships between its human contributors and machine counterparts to determine community structure and information flow.
Acquiring commonsense knowledge for sentiment analysis using human computation
Authors: Marina Boia, Claudiu Cristian Musat, Boi Faltings
Venue: WWW
Year: 2014

Many Artificial Intelligence tasks need commonsense knowledge. Extracting this knowledge with statistical methods would require huge amounts of data, so human computation offers a better alternative. We acquire contextual knowledge for sentiment analysis by asking workers to indicate the contexts that influence the polarities of sentiment words. The increased complexity of the task causes some workers to give superficial answers. To increase motivation, we make the task more engaging by packaging it as a game. With the knowledge compiled from only a small set of answers, we already halve the gap between machine and human performance. This proves the strong potential of human computation for acquiring commonsense knowledge.
Visualising data in web observatories: a proposal for visual analytics development & evaluation
Authors: Paul Booth, Wendy Hall, Nicholas Gibbins, Spyros Galanis
Venue: WWW
Year: 2014

Web Observatories use innovative analytic processes to gather insights from observed data and use the Web as a platform for publishing interactive data visualisations. Recordable events associated with interactivity on the Web provide an opportunity to openly evaluate the utility of these artefacts, assessing fitness for purpose and observing their use. The three principles presented in this paper propose a community evaluation approach to innovation in visual analytics and visualisation for Web Observatories through code sharing, the capturing of semantically enriched interaction data and by openly stating the intended goals of all visualisation work. The potential of this approach is exampled with a set of front-end tools suitable for adoption by the majority of Web Observatories as a means of visualising data on the Web as part the shared, open, and community-driven developmental process. The paper outlines the method for capturing user interaction data as a series of semantic events, which can be used to identify improvements in both the structure and functionality of visualisations. Such refinements in user behaviour are proposed as part of a new methodology that introduces Economics as an evaluation tool for visual analytics.
Relevant change detection: a framework for the precise extraction of modified and novel web-based content as a filtering technique for analysis engines
Authors: Kevin Borgolte, Christopher Kruegel, Giovanni Vigna
Venue: WWW
Year: 2014

Tracking the evolution of websites has become fundamental to the understanding of today's Internet. The automatic reasoning of how and why websites change has become essential to developers and businesses alike, in particular because the manual reasoning has become impractical due to the sheer number of modifications that websites undergo during their operational lifetime, including but not limited to rotating advertisements, personalized content, insertion of new content, or removal of old content. Prior work in the area of change detection, such as XyDiff, X-Diff or AT&T's internet difference engine, focused mainly on ``diffing'' XML-encoded literary documents or XML-encoded databases. Only some previous work investigated the differences that must be taken into account to accurately extract the difference between HTML documents for which the markup language does not necessarily describe the content but is used to describe how the content is displayed instead. Additionally, prior work identifies all changes to a website, even those that might not be relevant to the overall analysis goal, in turn, they unnecessarily burden the analysis engine with additional workload.
A study of the online profile of enterprise users in professional social networks
Authors: Alessandro Bozzon, Hariton Efstathiades, Geert-Jan Houben, Robert-Jan Sips
Venue: WWW
Year: 2014

Understanding the impact of corporate information publicly distributed on the Web is becoming more and more crucial. In this paper we report the result of a study that involved 130 IBM employees: we explored the correctness and extent of organisational information that can be observed from the online profiles of a company's employees. Our work contributes new insights to the study of social networks by showing that, even by considering a small fraction of the available online data, it is possible to discover accurate information about an organisation, its structure, and the factors that characterise the social reach of their employees.
Community-based crowdsourcing
Authors: Marco Brambilla, Stefano Ceri, Andrea Mauri, Riccardo Volonterio
Venue: WWW
Year: 2014

This paper is focused on community-based crowdsourcing applications, i.e. the ability of spawning crowdsourcing tasks upon multiple communities of performers, thus leveraging the peculiar characteristics and capabilities of the community members. We show that dynamic adaptation of crowdsourcing campaigns to community behaviour is particularly relevant. We demonstrate that this approach can be very effective for obtaining answers from communities, with very different size, precision, delay and cost, by exploiting the social networking relations and the features of the crowdsourcing task. We show the approach at work within the CrowdSearcher platform, which allows configuring and dynamically adapting crowdsourcing campaigns tailored to different communities. We report on an experiment demonstrating the effectiveness of the approach.
Status and friendship: mechanisms of social network evolution
Authors: Christina Brandt, Jure Leskovec
Venue: WWW
Year: 2014

We examine the evolution of five social networking sites where complex networks of social relationships developed: Twitter, Flickr, DeviantArt, Delicious, and Yahoo! Answers. We study the differences and similarities in edge creation mechanisms in these social networks. We find large differences in edge reciprocation rates and overall structure of the underlying networks. We demonstrate that two mechanisms can explain these disparities: directed triadic closure, which leads to networks that show characteristics of status-oriented behavior, and reciprocation, which leads to friendship-oriented behavior. We develop a model that demonstrates how variances in these mechanisms lead to characteristic differences in the expression of network subgraph motifs. Lastly, we show how a user's future popularity, her indegree, can be predicted based on her initial edge creation behavior.
Addictive links: engaging students through adaptive navigation support and open social student modeling
Authors: Peter Brusilovsky
Venue: WWW
Year: 2014

Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. Over the last 8 years we have explored a lesser known effect of adaptive annotation -- its ability to significantly increase student engagement in working with non-mandatory educational content. In the presence of adaptive link annotation, students tend to access significantly more learning content; they stay with it longer, return to it more often and explore a wider variety of learning resources. This talk will present an overview of our exploration of the addictive links effect in many course-long studies, which we ran in several domains (C, SQL and Java programming), for several types of learning content (quizzes, problems, interactive examples). The first part of the talk will review our exploration of a more traditional knowledge-based personalization approach and the second part will focus on more recent studies of social navigation and open social student modeling.
Learning conflict resolution strategies for cross-language Wikipedia data fusion
Authors: Volha Bryl, Christian Bizer
Venue: WWW
Year: 2014

In order to efficiently use the ever growing amounts of structured data on the web, methods and tools for quality-aware data integration should be devised. In this paper we propose an approach to automatically learn the conflict resolution strategies, which is a crucial step in large-scale data integration. The approach is implemented as an extension of the Sieve data quality assessment and fusion framework. We apply and evaluate our approach on the use case of fusing data from 10 language editions of DBpedia, a large-scale structured knowledge base extracted from Wikipedia. We also propose a method for extracting rich provenance metadata for each DBpedia fact, which is later used in data fusion.
Identifying fraudulently promoted online videos
Authors: Vlad Bulakh, Christopher W. Dunn, Minaxi Gupta
Venue: WWW
Year: 2014

Fraudulent product promotion online, including online videos, is on the rise. In order to understand and defend against this ill, we engage in the fraudulent video economy for a popular video sharing website, YouTube, and collect a sample of over 3,300 fraudulently promoted videos and 500 bot profiles that promote them. We then characterize fraudulent videos and profiles and train supervised machine learning classifiers that can successfully differentiate fraudulent videos and profiles from legitimate ones.
Identifying social roles in reddit using network structure
Authors: Cody Buntain, Jennifer Golbeck
Venue: WWW
Year: 2014

As social networks and the user-generated content that populates them continue to grow in prevalence, size, and influence, understanding how users interact and produce this content becomes increasingly important. Insight into these community dynamics could prove valuable for measuring content trust, providing role-based group recommendations, or evaluating group stability and growth. To this end, we explore user posting behavior on reddit, a large social networking site comprised of many sub-communities in which a user may participate simultaneously. We demonstrate that the well-known "answer-person" role is present in the reddit community, provide an exposition on an automated method for identifying this role based solely on user interactions (foregoing expensive content analysis), and show that users rarely exhibit significant participation in more than one communities.
Personal APIs as an enabler for designing and implementing people as social machines
Authors: Vanilson Arruda Burégio, Leandro F. Nascimento, Nelson Souto Rosa, Silvio R. L. Meira
Venue: WWW
Year: 2014

In this paper, we extend the initial classification scheme for Social Machines (SM) by including Personal APIs as a new SM-related topic of research inquiry. Personal APIs basically refer to the use of Open Application Programming Interfaces Open APIs) to programmatically access information about a person (e.g., personal basic info, health-related statistics, busy data) and/or trigger his/her human capabilities in a standardized way. Here, we provide an overview of some existing Personal APIs and show how this approach can be used to enable the design and implementation of people as individual SMs on the Web. A proof-of-concept system that demonstrates these ideas is also outlined in this paper.
Quantising contribution effort in online communities
Authors: Grégoire Burel, Yulan He
Venue: WWW
Year: 2014

We describe the Joint Effort-Topic (JET) model and the Author Joint Effort-Topic (aJET) model that estimate the effort required for users to contribute on different topics. We propose to learn word-level effort taking into account term preference over time and use it to set the priors of our models. Since there is no gold standard which can be easily built, we evaluate them by measuring their abilities to validate expected behaviours such as correlations between user contributions and the associated effort.
Crowdcrawling approach for community based plagiarism detection service
Authors: Sergey Butakov
Venue: WWW
Year: 2014

In the era of exponentially growing web and exploding online education the problem of digital plagiarism has become one of the most burning ones in many areas. Efficient internet plagiarism detection tools should have a capacity similar to that of conventional web search engines. This requirement makes commercial plagiarism detection services expensive and therefore less accessible to smaller education institutions. This work-in-progress paper proposes the concept of crowdcrawling as a tool to distribute the most laborious part of the web search among community servers thus providing scalability and sustainability to the community driven plagiarism detection. It outlines roles for community members depending on the resources they are willing to contribute to the service.
Quite a mess in my cookie jar!: leveraging machine learning to protect web authentication
Authors: Stefano Calzavara, Gabriele Tolomei, Michele Bugliesi, Salvatore Orlando
Venue: WWW
Year: 2014

Browser-based defenses have recently been advocated as an effective mechanism to protect web applications against the threats of session hijacking, fixation, and related attacks. In existing approaches, all such defenses ultimately rely on client-side heuristics to automatically detect cookies containing session information, to then protect them against theft or otherwise unintended use. While clearly crucial to the effectiveness of the resulting defense mechanisms, these heuristics have not, as yet, undergone any rigorous assessment of their adequacy. In this paper, we conduct the first such formal assessment, based on a gold set of cookies we collect from 70 popular websites of the Alexa ranking. To obtain the gold set, we devise a semi-automatic procedure that draws on a novel notion of authentication token, which we introduce to capture multiple web authentication schemes. We test existing browser-based defenses in the literature against our gold set, unveiling several pitfalls both in the heuristics adopted and in the methods used to assess them. We then propose a new detection method based on supervised learning, where our gold set is used to train a binary classifier, and report on experimental evidence that our method outperforms existing proposals. Interestingly, the resulting classification, together with our hands-on experience in the construction of the gold set, provides new insight on how web authentication is implemented in practice.
Concept-level sentiment analysis: a world wide web conference 2014 tutorial
Authors: Erik Cambria
Venue: WWW
Year: 2014

The WWW'14 tutorial on Concept-Level Sentiment Analysis aims to provide its participants means to efficiently design models, techniques, tools, and services for concept-level sentiment analysis and their commercial realizations. The tutorial draws on insights resulting from the recent IEEE Intelligent Systems special issues on Concept-Level Opinion and Sentiment Analysis and the IEEE CIM special issue on Computational Intelligence for Natural Language Processing. The tutorial includes a hands-on session to illustrate how to build a concept-level opinion-mining engine step-by-step, from semantic parsing to concept-level reasoning.
The (un)supervised detection of overlapping communities as well as hubs and outliers via (bayesian) NMF
Authors: Xiaochun Cao, Xiao Wang, Di Jin, Yixin Cao, Dongxiao He
Venue: WWW
Year: 2014

The detection of communities in various networks has been considered by many researchers. Moreover, it is preferable for a community detection method to detect hubs and outliers as well. This becomes even more interesting and challenging when taking the unsupervised assumption, that is, we do not assume the prior knowledge of the number K of communities. In this poster, we define a novel model to identify overlapping communities as well as hubs and outliers. When K is given, we propose a normalized symmetric nonnegative matrix factorization algorithm to learn the parameters of the model. Otherwise, we introduce a Bayesian symmetric nonnegative matrix factorization to learn the parameters of the model, while determining K. Our experiment indicate its superior performance on various networks.
Hippocampus: answering memory queries using transactive search
Authors: Michele Catasta, Alberto Tonon, Djellel Eddine Difallah, Gianluca Demartini, Karl Aberer, Philippe Cudré-Mauroux
Venue: WWW
Year: 2014

Memory queries denote queries where the user is trying to recall from his/her past personal experiences. Neither Web search nor structured queries can effectively answer this type of queries, even when supported by Human Computation solutions. In this paper, we propose a new approach to answer memory queries that we call Transactive Search: The user-requested memory is reconstructed from a group of people by exchanging pieces of personal memories in order to reassemble the overall memory, which is stored in a distributed fashion among members of the group. We experimentally compare our proposed approach against a set of advanced search techniques including the use of Machine Learning methods over the Web of Data, online Social Networks, and Human Computation techniques. Experimental results show that Transactive Search significantly outperforms the effectiveness of existing search approaches for memory queries.