OpenRaiser/Intern-Atlas huggingface dataset:
Identifying Regrettable Messages from Tweets
Authors: Lu Zhou, Wenbo Wang, Keke Chen
Venue: WWW
Year: 2015

Inappropriate tweets may cause severe damages on the authors' reputation or privacy. However, many users do not realize the potential damages when publishing such tweets. Published tweets have lasting effects that may not be completely eliminated by simple deletion, because other users may have read them or third-party tweet analysis platforms have cached them. In this paper, we study the problem of identifying regrettable tweets from normal individual users, with the ultimate goal of reducing the occurrences of regrettable tweets. We explore the contents of a set of tweets deleted by sample normal users to understand the regrettable tweets. With a set of features describing the identifiable reasons, we can develop classifiers to effectively distinguish such regrettable tweets from normal tweets.
A Universal Design Infrastructure for Multimodal Presentation of Materials in STEM Programs: Universal Design
Authors: Leyla Zhuhadar, Bryan Carson, Jerry Daday, Olfa Nasraoui
Venue: WWW
Year: 2015

We describe a proposed universal design infrastructure that aims at promoting better opportunities for students with disabilities in STEM programs to understand multimedia teaching material. The Accessible Educational STEM Videos Project aims to transform learning and teaching for students with disabilities through integrating synchronized captioned educational videos into undergraduate and graduate STEM disciplines. This Universal Video Captioning (UVC) platform will serve as a repository for uploading videos and scripts. The proposed infrastructure is a web-based platform that uses the latest WebDAV technology (Web-based Distributed Authoring and Versioning) to identify resources, users, and content. It consists of three layers: (i) an administrative management system; (ii) a faculty/staff user interface; and (iii) a transcriber user interface. We anticipate that by enriching it with captions or transcripts, the multimodal presentation of materials promises to help students with disabilities in STEM programs master the subject better and increase retention.
A Case Study on the Use of Semantic Web Technologies for Learner Guidance
Authors: Andrea Zielinski, Jürgen Bock
Venue: WWW
Year: 2015

Personalized learning pathways have been advocated by didactic experts to overcome the problem of disorientation and information overload in technology enhanced learning (TEL). They are not only relevant for providing user-adaptive navigational support, but can also be used for composing learning objects into new personalized courses (sequencing and assembly). In this paper we investigate, how Semantic Web technologies can effectively support these tasks, based on a proper representation of learning objects and courses according to didactic requirements. We claim that both eLearning tasks, adaptive navigation and course assembly, call for a representational model that can capture the syntax and semantics of learning pathways adequately. In particular: (1) a new type of navigation that takes into account ordering information and the hierarchical structure of an eLearning course complemented with adaptive constraints; (2) closely tied to it, a semantic layer to guarantee interoperability and validation of the correctness of the learning pathway descriptions. We investigate to what extend Semantic Web Languages like RDF/S and OWL are expressive enough to handle different aspects of learning pathways. While both share a structural similarity with DAGs, only OWL ontologies - formally underpinned by description logics (DLs) - are expressive enough to validate the correctness of the data and infer semantically related learning resources on the pathway. For tasks that are more related to the syntax of learning pathways, in particular navigation similar to a guided tour, we test the time efficiency on various synthetic OWL ontologies using the HermiT reasoner. Experimental results show that the course structure and the density of the knowledge graph impact on the performance. We claim that in a dynamically changing environment, where the computation of reachability of a vertex is computed on demand at run-time, OWL-based reasoning does not scale up well. Using a real-world case study from the eLearning domain, we compare an OWL 2 DL implementation with an equivalent graph algorithm implementation with respect to time efficiency.
Investigation of the Subjective Asymmetry of Social Interrelationship with Interactive Language
Authors: Bo Wang, Yanshu Yu, Peng Zhang
Venue: WWW
Year: 2016

Instead of studying the properties of social relationship from an objective view, in this paper, we focus on the two individuals' subjective and asymmetric opinions on their interrelationships. The sociolinguistics theories propose to characterize the individuals' opinions of their interrelationship with interactive language features. With this inspiration, we investigate the subjective asymmetry of the interrelationship with the asymmetry of the interactive language features including the frequency, quantity, quality and emotion. Experimental results with Enron email corpus provide suggestive evidences and thus reveal that the pair-wise language styles on an interrelationship are asymmetric, and this asymmetry can be a joint effect of the individuals' opinions of the interrelationship and their personal language habits. The results also indicate that the degree of the asymmetry could be related to the individuals' personality traits.
SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails
Authors: Martin Becker, Hauke Mewes, Andreas Hotho, Dimitar Dimitrov, Florian Lemmerich, Markus Strohmaier
Venue: WWW
Year: 2016

HypTrails is a bayesian approach for comparing different hypotheses about human trails on the web. While a standard implementation exists, it exposes performance issues when working with large-scale data. In this paper, we propose a distributed implementation of HypTrails based on Apache Spark taking advantage of several structural properties inherent to HypTrails. The performance improves substantially. Our implementation is publicly available.
Explainable Matrix Factorization for Collaborative Filtering
Authors: Behnoush Abdollahi, Olfa Nasraoui
Venue: WWW
Year: 2016

Explanations have been shown to increase the user's trust in recommendations in addition to providing other benefits such as scrutability, which is the ability to verify the validity of recommendations. Most explanation methods are designed for classical neighborhood-based Collaborative Filtering (CF) or rule-based methods. For the state of the art Matrix Factorization (MF) recommender systems, recent explanation methods, require an additional data source, such as item content data, in addition to rating data. In this paper, we address the case where no such additional data is available and propose a new Explainable Matrix Factorization (EMF) technique that computes an accurate top-$n$ recommendation list of items that are explainable. We also introduce new explanation quality metrics, that we call Mean Explainability Precision (MEP) and Mean Explainability Recall (MER).
Linked Data Profiling: Identifying the Domain of Datasets Based on Data Content and Metadata
Authors: Andrejs Abele
Venue: WWW
Year: 2016

Since the beginning of the Linked Open Data initiative, the number of published open datasets has gradually increased, but the datasets often do not contain description about content such as the dataset domain (e.g., medicine, cancer), when this information is available, it is usually coarse-grained e.g. organic-edunet contains the metadata about a collection of learning objects exposed through the Organic.Edunet portal, but it is classified as Life science. In this work we propose approaches that will provide a detailed description of existing datasets as well as linking assistance when publishing new datasets by generating detailed descriptions of the publishers dataset.
Linking Online Identities and Content in Connectivist MOOCs across Multiple Social Media Platforms
Authors: Rafa Absar, Anatoliy A. Gruzd, Caroline Haythornthwaite, Drew Paulin
Venue: WWW
Year: 2016

In this paper, we examine how multiple social media platforms are being used for formal and informal learning by examining data from two connectivist MOOCs (or cMOOCs). Our overarching goal is to develop and evaluate methods for learning analytics to detect and study collaborative learning processes. For this paper, we focus on how to link multiple online identities of learners and their contributions across several social media platforms in order to study their learning behaviours in open online environments. Many challenges were found in collection, processing, and analyzing the data; results are presented here to provide others with insight into such issues for examining data across multiple open media platforms.
Detecting and Mitigating the Effect of Manipulated Reputation on Online Social Networks
Authors: Anupama Aggarwal
Venue: WWW
Year: 2016

In recent times, online social networks (OSNs) are being used not only to communicate but to also create a public/social image. Artists, celebrities and even common people are using social networks to build their brand value and gain more visibility either amongst a restricted set of people or public. In order to enable user to connect to other users in the OSN and gain following and appreciation from them, various OSNs provide different social metrics to the user such as Facebook likes, Twitter followers and Tumblr reblogs. Hence, these metrics give a sense of social reputation to the OSN user. As more users are trying to leverage social media to create a brand value and become more influential, spammers are luring such users to help manipulate their social reputation with the help of paid service (black markets) or collusion networks. In this work, we aim to build a robust alternate social reputation system and detect users with manipulated social reputation. In order to do so, we first start by understanding the underlying structure of various sources of crowdsourced social reputation manipulation like blackmarkets, supply-driven microtask websites and collusion networks. We then build a mechanism for an early detection of users with manipulated social reputation. Our initial results are encouraging and substantiate the possibility of a robust social reputation system.
Understanding Smart Cities as Social Machines
Authors: Dirk Ahlers, Patrick Driscoll, Erica Löfström, John Krogstie, Annemie Wyckmans
Venue: WWW
Year: 2016

Smart Cities denote a stronger integration of information technology into the organisation of a city and the interaction and participation of its citizens. In developing the concept further, we propose to understand Smart Cities through the lens of Social Machines and thus stronger focus on the city as a socio-technical construct. We draw from an interdisciplinary background of computer science and urban planning to reexamine and combine existing theories and find a common understanding. We substantiate our claim to the validity of the concept of Smart-City-as-a-Social-Machine with a thorough literature study and comparison. We discuss the resulting system complexity issues and ways to address them. We further propose areas where this understanding can be useful in furthering research on both the Smart City and the Social Machine topics.
The Sensorial Map of the City
Authors: Luca Maria Aiello
Venue: WWW
Year: 2016

Our daily urban experiences are the product of our perceptions and senses, yet the complete sensorial range is strikingly absent from urban studies. Sight has been historically privileged over the other senses and urban studies. However, smell and sound have also a huge influence over how we perceive places, they impact our behavior, attitudes and health. Yet, city planning is concerned only with a few bad smells and with limiting noise levels. We propose a new way of capturing nuanced sensorial perceptions of cities from data implicitly generated by social media users and of producing detailed sensorial maps of our cities.
SoMap: Dynamic Clustering and Ranking of Geotagged Posts
Authors: Marie Al-Ghossein, Talel Abdessalem
Venue: WWW
Year: 2016

This demo presents SoMap, a web-based platform that provides new scalable methods to aggregate, analyse and valorise large collections of heterogeneous social data in urban contexts. The platform relies on geotagged data extracted from social networks and microblogging applications such as Instagram, Flickr and Twitter and on Points Of Interest gathered from OpenStreetMap. It could be very insightful and interesting for data scientists and decision-makers. SoMap enables dynamic clustering of filtered social data in order to display it on a map in a combined form. The key components of this platform are the clustering module, which relies on a scalable algorithm described in this paper, and the ranking algorithm that combines the popularity of the posts, their location and their link to the points of interest found in the neighbourhood. The system further detects mobility patterns by identifying and aggregating trajectories for all the users. SoMap will be demonstrated through several examples that highlight all of its functionalities and reveal its effectiveness and usefulness.
Semantic Enrichment for Local Search Engine using Linked Open Data
Authors: Mazen AlObaidi, Khalid Mahmood, Susan Sabra
Venue: WWW
Year: 2016

Local search engines are a vital part of presence of local businesses on the Internet. Local search engines improvement is an important element to ensure that local businesses can be found by millions of people whom are using web to find services. However, web presence can be disguised or not properly documented. Our approach will improve the effectiveness of local search, and increase the ranking of local businesses. We introduce an approach for enhancing local search engines efficiency in returning more accurate results. Our approach consists of semantically enriching the results of a query using Linked Open Data (LOD) web content. Our preliminary evaluation demonstrated evidence that our approach has a better search with more accurate results.
Time to ship: Some Examples from the Real-World
Authors: Omar Alonso
Venue: WWW
Year: 2016

There is new and exciting research work in analyzing and exploiting information from corpora with temporal information such as news, email, or social media. We review some of the current activities around extracting temporal information from social media and present a number of examples from real-world systems. We also outline a number of open problems and potential new research areas.
Structural Properties in δ-Hyperbolic Networks: Algorithmic Analysis and Implications
Authors: Hend Alrasheed
Venue: WWW
Year: 2016

In graph theory, the δ-hyperbolicity is a global property that shows how close a given graph's structure is to the tree's structure metrically. It embeds multiple properties that facilitate solving several problems that found to be hard in the general graph form. Interestingly, not only that δ-hyperbolicity provides an idea about the structure of the graph, but also it explains how information navigates throughout the network. Therefore, δ-hyperbolicity has several applications in diverse applied fields. My PhD dissertation focuses on analyzing and exploiting structural properties of hyperbolic networks for different applications.
Chains of Distrust: Towards Understanding Certificates Used for Signing Malicious Applications
Authors: Omar Alrawi, Aziz Mohaisen
Venue: WWW
Year: 2016

Digital certificates are key component of trust used by many operating systems. Modern operating systems implement a form of digital signature verification for various applications, including kernel driver installation, software execution, etc. Digital signatures rely on digital certificates that authenticate the signature, which then verify the validity of a given signature for a signed binary. Malware attempts to subvert the chain of trust through several techniques to achieve execution, evasion, and persistence. In this paper, we examine a large corpus of malware ($3.3$ million samples) to extract digital signatures and their corresponding certificates. We examine several characteristics of the digital certificates to study features in the process of malware authorship that will potentially be used for characterizing and classifying malware. We look at many features including the certificate's chain length, the issue and expiration year, the validity duration of a certificate, the issuing country, validity, top issuing certificate authorities (CAs), and others, highlighting potentially discriminatory features.
Machine Learning for Q&A Sites: State of the Art and Research Directions
Authors: Xavier Amatriain
Venue: WWW
Year: 2016

Q&A sites like Quora aim at growing the world's knowledge. In order to do this, they need to get the right questions to the right people to answer them, but also the existing answers to people who are interested in them. In order to accomplish this, they need to build a complex ecosystem where issues such as content quality, engagement, demand, interests, or reputation are taken into account. It is not possible to build a system like this unless most of the process are highly automated and scalable. The good news is that using high-quality data you can build machine learning solutions that can help address all of the previous requirements.
Improving Local Search with Open Geographic Data
Authors: Chuankai An, Dan Rockmore
Venue: WWW
Year: 2016

Local search helps users find certain types of business units (restaurant, gas stations, hospitals, etc.) in the surrounding area. However, some merchants might not have much online content (e.g. customer reviews, business descriptions, opening hours, telephone numbers, etc.). This can pose a problem for traditional local search algorithms such as vector space based approaches. With this difficulty in mind, in this paper we present an approach to local search that incorporates geographic open data. Using the publicly available {\em Yelp} dataset we are able to uncover patterns that link geographic features and user preferences. From this, we propose a model to infer user preferences that integrates geographic parameters. Through this model and estimation of user preference, we develop a new framework for ``local'' (in the sense of geography) search that offsets the absence of contexts regarding physical business units. Our initial analysis points to the meaningful integration of open geographic data in local search and points out several directions for further research.
Smart Service Portfolios: Do the Cities Follow Standards?
Authors: Leonidas G. Anthopoulos, Marijn Janssen, Vishanth Weerakkody
Venue: WWW
Year: 2016

Smart services concern the core element of a smart city, since they support the realization of urban "intelligence" in terms of people, economy, governance, environment, mobility and leaving. Smart services aim to enhance quality of life within a city and in this respect to improve "livability". The types and purposes of smart services cannot be easily pre-defined, since they are the outcome of innovation, which cannot be pre-defined either, but instead it is the product of citizens' and businesses' creativity. However, standard bodies that work on smart city definition have described smart city portfolios, which are suggested to city policy makers and potential entrepreneurs. The aim of this paper is to validate whether standardized smart service portfolios are being followed by smart cities in practice. In this regard, a set of more than 70 smart cities are examined and their smart services are matched to these portfolios. The outcomes are extremely important and leave space for future research in this regard.
Smart City and Smart Government: Synonymous or Complementary?
Authors: Leonidas G. Anthopoulos, Christopher G. Reddick
Venue: WWW
Year: 2016

Smart City is an emerging and multidisciplinary domain. It has been recently defined as innovation, not necessarily but mainly through information and communications technologies (ICT), which enhance urban life in terms of people, living, economy, mobility and governance. Smart government is also an emerging topic, which attracts increasing attention from scholars who work in public administration, political and information sciences. There is no widely accepted definition for smart government, but it appears to be the next step of e-government with the use of technology and innovation by governments for better performance. However, it is not clear whether these two terms co-exist or concern different domains. The aim of this paper is to investigate the term smart government and to clarify its meaning in relationship to the smart city. In this respect this paper performed a comprehensive literature review analysis and concluded that smart government is shown not to be synonymous with smart city. Our findings show that smart city has a dimension of smart government, and smart government uses smart city as an area of practice. The authors conclude that smart city is complimentary, part of larger smart government movement.
Destiny: A Cognitive Mobile Guide for the Olympics
Authors: Ana Paula Appel, Heloisa Candello, Beatriz Sonzzini Ribeiro de Souza, Bruna D. Andrade
Venue: WWW
Year: 2016

In this work we present Destiny a cognitive mobile guide for Olympics games in Brazil that identifies user characteristics to deliver content. It will help visitors, athletes, and athletes' parents to localize themselves and receive tailored historical, cultural and entertainment information at a particular point of interest (POI). The application will recommend places to go and show POI contents according to user context based on several attributes such as personality (closeness, curiosity, adventurous), nationality, favorite places already visited, trip length, price range and favorite sports
Exploring Cooperation with Social Machines
Authors: Sally Ann Applin, Michael D. Fischer
Venue: WWW
Year: 2016

As humans become more and more immersed in a networked world of connected and mobile devices, cooperation and sociability to achieve valued outcomes within geographic locales appears to be waning in favour of extended personal networks and interaction using semi-automated agents to support communications, transportation and other services. From a messaging structure that is complex, multiplexed and much of the time asynchronous, conditions emerge that disrupt symmetry of information exchange. People thus encounter circumstances that seem unpredictable given the information available to them, resulting in limited or failed cooperation and consequent quality of outcomes. We explore the role of Social Machines to support, change, and enhance human cooperation within a blended reality context.
Reverse Engineering SPARQL Queries
Authors: Marcelo Arenas, Gonzalo I. Diaz, Egor V. Kostylev
Venue: WWW
Year: 2016

Semantic Web systems provide open interfaces for end-users to access data via a powerful high-level query language, SPARQL. But users unfamiliar with either the details of SPARQL or properties of the target dataset may find it easier to query by example -- give examples of the information they want (or examples of both what they want and what they do not want) and let the system reverse engineer the desired query from the examples. This approach has been heavily used in the setting of relational databases. We provide here an investigation of the reverse engineering problem in the context of SPARQL. We first provide a theoretical study, formalising variants of the reverse engineering problem and giving tight bounds on its complexity. We next explain an implementation of a reverse engineering tool for positive examples. An experimental analysis of the tool shows that it scales well in the data size, number of examples, and in the size of the smallest query that fits the data. We also give evidence that reverse engineering tools can provide benefits on real-life datasets.