OpenRaiser/Intern-Atlas huggingface dataset:
Pushing the Frontier: Exploring the African Web Ecosystem
Authors: Rodérick Fanou, Gareth Tyson, Pierre François, Arjuna Sathiaseelan
Venue: WWW
Year: 2016

It is well known that Africa's mobile and fixed Internet infrastructure is progressing at a rapid pace. A flurry of recent research has quantified this, highlighting the expansion of its underlying connectivity network. However, improving the infrastructure is not useful without appropriately provisioned services to utilise it. This paper measures the availability of web content infrastructure in Africa. Whereas others have explored web infrastructure in developed regions, we shed light on practices in developing regions. To achieve this, we apply a comprehensive measurement methodology to collect data from a variety of sources. We focus on a large content delivery network to reveal that Africa's content infrastructure is, indeed, expanding. However, we find much web content is still served from the US and Europe. We discover that many of the problems faced are actually caused by significant inter-AS delays in Africa, which contribute to local ISPs not sharing their cache capacity. We discover that a related problem is the poor DNS configuration used by some ISPs, which confounds the attempts of providers to optimise their delivery. We then explore a number of other websites to show that large web infrastructure deployments are a rarity in Africa and that even regional websites host their services abroad. We conclude by making suggestions for improvements.
Personality Traits and the Relationship with (Non-) Disclosure Behavior on Facebook
Authors: Bruce Ferwerda, Markus Schedl, Marko Tkalcic
Venue: WWW
Year: 2016

Applications increasingly use personality traits to provide a personalized service to the user. To acquire personality, social media trails showed to be a reliable source. However, until now, analysis of social media trails have been focusing on \textit{what} has been disclosed: content of disclosed items. These methods fail to acquire personality when there is a lack of content (non-disclosure). In this study we do not look at the disclosed content, but whether disclosure occurred or not. We extracted 40 items of different Facebook profile sections that users can disclose or not disclose. We asked participants to indicate to which extent they disclose the items in an online survey, and additionally asked them to fill in a personality questionnaire. Among 100 participants we found that users' personality can be predicted by solely looking at whether they disclose particular sections of their profiles. This allows for personality acquisition when content is missing.
TribeFlow: Mining & Predicting User Trajectories
Authors: Flavio Figueiredo, Bruno Ribeiro, Jussara M. Almeida, Christos Faloutsos
Venue: WWW
Year: 2016

Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). Moreover, what users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges we propose TribeFlow, a method designed to cope with the complex challenges of learning personalized predictive models of non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow is a general method that can perform next product recommendation, next song recommendation, next location prediction, and general arbitrary-length user trajectory prediction without domain-specific knowledge. TribeFlow is more accurate and up to 413x faster than top competitors.
Predicting Drug-Drug Interactions Through Similarity-Based Link Prediction Over Web Data
Authors: Achille Fokoue, Oktie Hassanzadeh, Mohammad Sadoghi, Ping Zhang
Venue: WWW
Year: 2016

Drug-Drug Interactions (DDIs) are a major cause of preventable adverse drug reactions and a huge burden on public health and the healthcare system. On the other hand, there is a large amount of drug-related (open) data published on the Web, describing various properties of drugs and their relationships to other drugs, genes, diseases, and related concepts and entities. In this demonstration, we describe an end-to-end system we have designed to take in various Web data sources as input and provide as output a prediction of DDIs along with an explanation of why two drugs may interact. The system first creates a knowledge graph out of input data sources through large-scale semantic integration, and then performs link prediction among drug entities in the graph through large-scale similarity analysis and machine learning. The link prediction is performed using a logistic regression model over several similarity matrices built using different drug similarity measures. We present both the efficient link prediction framework implemented in Apache Spark, and our APIs and Web interface for predicting DDIs and exploring their potential causes and nature.
Watson Concept Insights: A Conceptual Association Framework
Authors: Michele M. Franceschini, Livio B. Soares, Luis Alfonso Lastras-Montaño
Venue: WWW
Year: 2016

Watson Concept Insights (WCI) is a service that was recently made publicly available by IBM. WCI provides an information retrieval framework that is designed to facilitate search and exploration of text documents, and is particularly effective on sparse data sets. Its methodology consists of first defining a dictionary of concepts which are interconnected in a concept graph and then modeling a document by predicting its relevance to any given concept in the concept graph using the concepts that are directly mentioned in the document itself. This technique in effect increases the document recall for any given query, even for very sparse data sets, exposing the user to a variety of connections between their query and a data set of interest.
From Diversity-based Prediction to Better Ontology & Schema Matching
Authors: Avigdor Gal, Haggai Roitman, Tomer Sagi
Venue: WWW
Year: 2016

Ontology & schema matching predictors assess the quality of matchers in the absence of an exact match. We propose MCD (Match Competitor Deviation), a new diversity-based predictor that compares the strength of a matcher confidence in the correspondence of a concept pair with respect to other correspondences that involve either concept. We also propose to use MCD as a regulator to optimally control a balance between Precision and Recall and use it towards 1:1 matching by combining it with a similarity measure that is based on solving a maximum weight bipartite graph matching (MWBM). Optimizing the combined measure is known to be an NP-Hard problem. Therefore, we propose CEM, an approximation to an optimal match by efficiently scanning multiple possible matches, using rare event estimation. Using a thorough empirical study over several benchmark real-world datasets, we show that MCD outperforms other state-of-the-art predictor and that CEM significantly outperform existing matchers.
Geography of Emotion: Where in a City are People Happier?
Authors: Luciano Gallegos, Kristina Lerman, Arthur Huang, David García
Venue: WWW
Year: 2016

During the last years, researchers explored the geographic and environmental factors that affect happiness. More recently, location-sharing services provided by the social media has given an unprecedented access to geo-located data for studying the interplay between these factors on a much bigger scale. Do location-sharing services help in turn at distinguishing emotions in places within a city? Which aspects contribute better at understanding happier places? To answer these questions, we use data from Foursquare location-sharing service to identify areas within a major US metropolitan area with many check-ins, i.e., areas that people like to use. We then use data from the Twitter microblogging platform to analyze the properties of these areas. Specifically, we have extracted a large corpus of geo-tagged messages, called tweets, from a major metropolitan area and linked them US Census data through their locations. This allows us to measure the sentiment expressed in tweets that are posted from a specific area, and also use that area's demographic properties in analysis. Our results reveal that areas with many check-ins are different from other areas within the metropolitan region. In particular, these areas have happier tweets, which also encourage people living in it or from other areas to commute longer distances to these places. These findings shed light on the influence certain places play within a city regarding people's emotions and mobility, which in turn can be used for city planners for designing happier and more equitable cities.
The Liquid.js Framework for Migrating and Cloning Stateful Web Components across Multiple Devices
Authors: Andrea Gallidabino, Cesare Pautasso
Venue: WWW
Year: 2016

We are heading toward an era in which users own more than one single Web-enabled device. These devices range from smart phones, tablets and personal computers to smart Web-enabled devices found in houses and cars. The access mechanisms and usage patterns of Web applications are changing accordingly, as users interact more and more with Web applications through all their devices, even if the majority of Web applications are not ready to offer a good user experience taking full advantage of multiple devices. In this demonstration we introduce Liquid.js, a framework whose goal is to enable Web developers to take advantage of multiple heterogeneous devices and offer to their users a liquid user experience, whereby any device can be used sequentially or concurrently with Web applications that can effortlessly roam from one device to another. This way, as highlighted in the demonstration users do not need to stop and resume their work on their Web application as they migrate and clone them across different devices. The demo will also show how developers can easily add such liquid behavior to any Polymer Web component.
Discouraging Abusive Behavior in Privacy-Preserving Online Social Networking Applications
Authors: Álvaro García-Recuero
Venue: WWW
Year: 2016

In this position paper we present the challenge of detecting abuse in a modern Online Social Network (OSN) while balancing data utility and privacy, with the goal of limiting the amount of user sensitive information processed during data collection, extraction and analysis. While we are working with public domain data available in a contemporary OSN, our goal is to design a thorough method for future alternative OSN designs that both protect user's sensitive information and discourage abuse.
The QWERTY Effect on the Web: How Typing Shapes the Meaning of Words in Online Human-Computer Interaction
Authors: David García, Markus Strohmaier
Venue: WWW
Year: 2016

The QWERTY effect postulates that the keyboard layout influences word meanings by linking positivity to the use of the right hand and negativity to the use of the left hand. For example, previous research has established that words with more right hand letters are rated more positively than words with more left hand letters by human subjects in small scale experiments. In this paper, we perform large scale investigations of the QWERTY effect on the web. Using data from eleven web platforms related to products, movies, books, and videos, we conduct observational tests whether a hand-meaning relationship can be found in text interpretations by web users. Furthermore, we investigate whether writing text on the web exhibits the QWERTY effect as well, by analyzing the relationship between the text of online reviews and their star ratings in four additional datasets. Overall, we find robust evidence for the QWERTY effect both at the point of text interpretation (decoding) and at the point of text creation (encoding). We also find under which conditions the effect might not hold. Our findings have implications for any algorithmic method aiming to evaluate the meaning of words on the web, including for example semantic or sentiment analysis, and show the existence of "dactilar onomatopoeias" that shape the dynamics of word-meaning associations. To the best of our knowledge, this is the first work to reveal the extent to which the QWERTY effect exists in large scale human-computer interaction on the web.
With a Little Help from my Neighbors: Person Name Linking Using the Wikipedia Social Network
Authors: Johanna Geiß, Michael Gertz
Venue: WWW
Year: 2016

Driven by the popularity of social networks, there has been an increasing interest in employing such networks in the context of named entity linking. In this paper, we present a novel approach to person name disambiguation and linking that uses a large-scale social network extracted from the English Wikipedia. First, possible candidate matches for an ambiguous person name are determined. With each candidate match, a network substructure is associated. Based on the similarity between these network substructures and the latent network of an ambiguous person name in a document, we propose an efficient ranking method to resolve the ambiguity. We demonstrate the effectiveness of our approach, resulting in an overall precision of over 96% for disambiguating person names and linking them to real world entities.
Tell Me About Yourself: The Malicious CAPTCHA Attack
Authors: Nethanel Gelernter, Amir Herzberg
Venue: WWW
Year: 2016

We present the malicious CAPTCHA attack, allowing a rogue website to trick users into unknowingly disclosing their private information. The rogue site displays the private information to the user in obfuscated manner, as if it is a CAPTCHA challenge; the user is unaware that solving the CAPTCHA, results in disclosing private information. This circumvents the Same Origin Policy (SOP), whose goal is to prevent access by rogue sites to private information, by exploiting the fact that many websites allow display of private information (to the user), upon requests from any (even rogue) website. Information so disclosed includes name, phone number, email and physical addresses, search history, preferences, partial credit card numbers, and more. The vulnerability is common and the attack works for many popular sites, including nine out of the ten most popular websites. We evaluated the attack using IRB-approved, ethical user experiments.
THUMP: Semantic Analysis on Trajectory Traces to Explore Human Movement Pattern
Authors: Shreya Ghosh, Soumya K. Ghosh
Venue: WWW
Year: 2016

Exploring human movement pattern from raw GPS traces is an interesting and challenging task. This paper aims at analysing a large volume of GPS data in spatio-temporal context, clustering trajectories using geographic and semantic location information and identifying different categories of people. It tries to exploit the fact that human moves with an intent. The proposed framework yields encouraging results using a large scale GPS dataset of Microsoft GeoLife.
Stweeler: A Framework for Twitter Bot Analysis
Authors: Zafar Gilani, Liang Wang, Jon Crowcroft, Mário Almeida, Reza Farahbakhsh
Venue: WWW
Year: 2016

The WWW has seen a massive growth in variety and usage of OSNs. The rising population of users on Twitter and its open nature has made it an ideal platform for various kinds of opportunistic pursuits, such as news and emergency communication, business promotion, political campaigning, spamming and spreading malicious content. Most of these opportunistic pursuits are exploited through automated programs, known as bots. In this study we propose a framework (Stweeler) to study bot impact and influence on Twitter from systems and social media perspectives.
Smart City Energy Planning: Integrating Data and Tools
Authors: João Pedro Gouveia, Júlia Seixas, George Giannakidis
Venue: WWW
Year: 2016

This paper presents an innovative analytical framework to address incomplete interpretations and dispersed data of the energy system in cities, which usually generate multiple inefficiencies. Integrative city planning takes the city energy system from the supply to the demand while considering its spatial representativeness, and drives optimal cost-efficient assessment towards future sustainable energy targets. This holistic approach delivers more adequate policies and measures towards higher energy use efficiency. The proposed analytical framework has been developed within the INSMART EU funded project and focuses on data gathering procedures and data processing tools and models, covering a wide range of city's energy consumers, as residential buildings, transport and utilities. The results, mapped into a GIS, can be further exploited either for awareness increase of citizens and for decision support of city energy planners.
The Case for Robotic Wireless Networks
Authors: Mahanth Gowda, Ashutosh Dhekne, Romit Roy Choudhury
Venue: WWW
Year: 2016

This paper explores the possibility of injecting mobility into wireless network infrastructure. We envision WiFi access points on wheels that move to optimize user performance. Movements need not be all around the floor, neither do they have to operate on batteries. As a first step, WiFi APs at home could remain tethered to power and Ethernet outlets while moving in small areas (perhaps under the couch). If such systems prove successful, perhaps future buildings and cities could offer explicit support for network infrastructure mobility. This paper begins with a higher level discussion of robotic wireless networks -- the opportunities and the hurdles -- and then pivots by developing a smaller slice of the vision through a system called iMob. With iMob, a WiFi AP is mounted on a Roomba robot and made to periodically move within a 2x2 sqft region. The core research questions pertain to finding the best location to move to, such that the SNRs from its clients are strong, and the interferences from other APs are weak. Our measurements show that the richness of wireless multipath offers significant opportunities -- even within a 2x2 sqft region, locations exist that are 1.7x better than the average location in terms of throughput. When multiple APs in a neighborhood coordinate, the gains can be even higher. In sum, although infrastructure mobility has been discussed in the context of Google Balloons, ad hoc networks, and delay tolerant networks, we believe that the possibility of moving our personal devices in homes and offices is relatively unexplored, and could open doors to new kinds of innovation.
Who are We Modelling: Bots or Humans?
Authors: Anatoliy A. Gruzd
Venue: WWW
Year: 2016

Computational techniques such as Behavioural Analytics (BA) have been extremely effective at transforming social media data into useful insights for applications such as recommender systems [1] and customer relation management [2]. However, due to the rise of smarter, more sophisticated social bots [3] and the increasing reliance on algorithmic filtering (which nudges online users to make certain choices and take specific actions) [4], it begs the question, if we are using data from social media for modelling, are we modelling human behavior in social media or simply reverse engineering how bots and other algorithms operate?
Lexical Normalization of Spanish Tweets
Authors: Jhon Adrián Cerón-Guzmán, Elizabeth León-Guzmán
Venue: WWW
Year: 2016

Twitter data have brought new opportunities to know what happens in the world in real-time, and conduct studies on the human subjectivity on a diversity of issues and topics at large scale, which would not be feasible using traditional methods. However, as well as these data represent a valuable source, a vast amount of noise can be found in them. Because of the brevity of texts and the widespread use of mobile devices, non-standard word forms abound in tweets, which degrade the performance of Natural Language Processing tools. In this paper, a lexical normalization system of tweets written in Spanish is presented. The system suggests normalization candidates for out-of-vocabulary (OOV) words based on similarity of graphemes or phonemes. Using contextual information, the best correction candidate for a word is selected. Experimental results show that the system correctly detects OOV words and the most of cases suggests the proper corrections. Together with this, results indicate a room for improvement in the correction candidate selection. Compared with other methods, the overall performance of the system is above-average and competitive to different approaches in the literature.
Piglet: Interactive and Platform Transparent Analytics for RDF & Dynamic Data
Authors: Stefan Hagedorn, Kai-Uwe Sattler
Venue: WWW
Year: 2016

Data analytics has gained more and more focus during recent years and many data processing platforms have been developed. They all provide a powerful but often complex API that users have to learn. Furthermore, results can only be stored or printed, without any possibility for visualization.
Using the SPENCE Model of Online/Offline Community to Analyse Sociality of Social Machines
Authors: Caroline A. Halcrow, Leslie Carr, Susan Halford
Venue: WWW
Year: 2016

Online/offline community (O/OC), the integrated performance of community in a blend of online/offline activities is increasingly prevalent as online systems organise, mediate and broadcast forms of communal engagement. O/OCs are social machines where the focus is on the social achievement, rather than the computational outcomes, of the combined human-technical infrastructure. An O/OC model SPENCE is proposed as an analytical tool for describing social machines from the perspective of sociality. Twitter is a technical infrastructure and social network of shared online/offline community phenomena that is also a social machine combining social participation with conventional forms of machine-based computation. Drawing from the extensive Twitter research literature, a sample of papers are analysed against SPENCE, demonstrating the clarity of the organisation of inter-relating themes of a range of perspectives in current Twitter research. It is concluded that SPENCE provides a lens of synthesis for the sociality dimension of a social machine and can be used in taxonomic activities (such as the social machines observatory) to differentiate social machines.
Web Science, Social Media and Education
Authors: Wendy Hall
Venue: WWW
Year: 2016

Over the last 25 years the Web has evolved into a critical global infrastructure. Since its emergence in the 1990s, it has exploded into hundreds of billions of pages that touch almost all aspects of modern life. Little appreciated, however, is the fact that the Web is more than the sum of its pages and it is more than its technical protocols. Vast emergent properties have arisen that are transforming society. Web Science is the study of the Web as a socio-technical system. As the Web becomes increasingly significant in all our lives, studying it from an interdisciplinary perspective becomes even more important.