OpenRaiser/Intern-Atlas huggingface dataset:
Planning the Smart City in China: Key Policy Issues and the Case of Dream Town in the City of Hangzhou
Authors: Iraklis Argyriou
Venue: WWW
Year: 2016

This paper offers a brief review of basic literature on smart cities to outline research issues in this field that require further attention from a governance perspective. It then maps the current urban planning landscape in China, a country which puts increasing emphasis on smart cities as a mechanism to promote sustainable development, in order to elicit key policy aspects that need to be considered in empirical analysis when "planning the Chinese smart city". The paper concludes by introducing a case of local innovation in the area of digital economy, the so-called Dream Town, undertaken in the city of Hangzhou as an illustrative example of urban China's current efforts on planning for smart city development.
For the DISTINCT Clause of SPARQL Queries
Authors: Medha Atre
Venue: WWW
Year: 2016

Evaluating SPARQL queries with the DISTINCT clause may become memory intensive due to the requirement of additional auxiliary data structures, like hash-maps, to discard the duplicates. DISTINCT queries make up to 16% of all the queries (e.g., DBPedia), and thus are non-negligible. In this poster we propose a novel method for such queries, by just manipulating the compressed bit-vector indexes called BitMats, for acyclic basic graph pattern (BGP) queries.
DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails
Authors: Martin Atzmueller, Andreas Schmidt, Mark Kibanov
Venue: WWW
Year: 2016

The analysis of sequential trails and patterns is a prominent research topic. However, typically only explicitly observed trails are considered. In contrast, this paper proposes the DASHTrails approach that enables the modeling and analysis of distribution-adapted sequential trails and hypotheses. It presents a method for deriving transition matrices given a probability distribution over certain events. We demonstrate the applicability of the proposed approach using real-world data in the mobility domain, i.e., car trajectories and spatio-temporal distributions on car accidents.
LDOW2016: 9th Workshop on Linked Data on the Web
Authors: Sören Auer, Tom Heath, Christian Bizer, Tim Berners-Lee
Venue: WWW
Year: 2016

The ninth workshop on Linked Data (LDOW2016) on the Web is held in Montreal, Quebec, Canada on April 12, 2016 and co-located with the 25rd International World Wide Web Conference (WWW2016). The Web is developing from a medium for publishing textual documents into a medium for sharing structured data. This trend is fueled on the one hand by the adoption of the Linked Data principles by a growing number of data providers. On the other hand, large numbers of websites have started to semantically mark up the content of their HTML pages and thus also contribute to the wealth of structured data available on the Web. The 9th Workshop on Linked Data on the Web aims to stimulate discussion and further research into the challenges of publishing, consuming, and integrating structured data from the Web as well as mining knowledge from the global Web of Data.
Mechanism Design for Mixed Bidders
Authors: Yoram Bachrach, Sofia Ceppi, Ian A. Kash, Peter B. Key, Mohammad Reza Khani
Venue: WWW
Year: 2016

The Generalized Second Price (GSP) auction has appealing properties when ads are simple (text based and identical in size), but does not generalize to richer ad settings, whereas truthful mechanisms such as VCG do. However, a straight switch from GSP to VCG incurs significant revenue loss for the search engine. We introduce a transitional mechanism which encourages advertisers to update their bids to their valuations, while mitigating revenue loss. In this setting, it is easier to propose first a payment function rather than an allocation function, so we give a general framework which guarantees incentive compatibility by requiring that the payment functions satisfy two specific properties. Finally, we analyze the revenue impacts of our mechanism on a sample of Bing data.
Context-Aware Friend Recommendation for Location Based Social Networks using Random Walk
Authors: Hakan Bagci, Pinar Karagoz
Venue: WWW
Year: 2016

The location-based social networks (LBSN) facilitate users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a random walk based context-aware friend recommendation algorithm (RWCFR). RWCFR considers the current context (i.e. current social relations, personal preferences and current location) of the user to provide personalized recommendations. Our LBSN model is an undirected unweighted graph model that represents users, locations, and their relationships. We build a graph according to the current context of the user depending on this LBSN model. In order to rank the recommendation scores of the users for friend recommendation, a random walk with restart approach is employed. We compare RWCFR with popularity-based, friend-based and expert-based baseline approaches. According to the results, our friend recommendation algorithm outperforms these approaches in all the tests.
PNCOIRank: Evaluating the Impact of Scholarly Articles with Positive and Negative Citations
Authors: Xiaomei Bai, Jun Zhang, Hai Cui, Zhaolong Ning, Feng Xia
Venue: WWW
Year: 2016

Evaluating the impact of an article is a significant topic and has attracted extensive attention. Citation-based assessment methods currently face a limitation, i.e. the anomalous citations patterns still remain poorly understand. To remedy this drawback, we propose a Positive and Negative Conflict of Interest (COI)-based Rank algorithm, named PNCOIRank, to acquire positive COI, negative COI, positive suspected COI and negative suspected COI relationships. We investigate the citation relationships by the following scholarly factors: citing times, the interval of citing time, collaboration times, the interval of collaboration time, and team of citing authors with the purpose of weakening the COI relationships in citation network. A weighted PageRank is finally constructed and employed, with HITS algorithm to assess the impact of articles. Through experiments on American Physical Society (APS) dataset, we show that PNCOIRank significantly outperforms the existing methods in terms of recommendation intensity.
Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior
Authors: Samuel Barbosa, Dan Cosley, Amit Sharma, Roberto M. Cesar Jr.
Venue: WWW
Year: 2016

Online communities provide a fertile ground for analyzing people's behavior and improving our understanding of social processes. Because both people and communities change over time, we argue that analyses of these communities that take time into account will lead to deeper and more accurate results. Using Reddit as an example, we study the evolution of users based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between users---yearly cohorts---we find wide differences in people's behavior, including comment activity, effort, and survival. Further, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson's Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward.
Look Before You Shame: A Study on Shaming Activities on Twitter
Authors: Rajesh Basak, Niloy Ganguly, Shamik Sural, Soumya K. Ghosh
Venue: WWW
Year: 2016

Online social networks (OSNs) are often flooded with scathing remarks against individuals or businesses on their perceived wrongdoing. This paper studies three such events to get insight into various aspects of shaming done through twitter. An important contribution of our work is categorization of shaming tweets, which helps in understanding the dynamics of spread of online shaming events. It also facilitates automated segregation of shaming tweets from non-shaming ones.
Leveraging Crowdsourcing for the Thematic Annotation of the Qur'an
Authors: Amna Basharat, Ismailcem Budak Arpinar, Khaled Rasheed
Venue: WWW
Year: 2016

In this paper, we illustrate how we leverage crowdsourcing to create workflows for knowledge engineering in specialized and knowledge intensive domains. We undertake the special case of the Arabic script of the Qur'an, a widely studied manuscript, and attempt to employ crowdsourcing methods for its thematic annotation at the sub-verse level, for which, there is no standardized knowledge model available to date. We demonstrate that our proposed method presents feasibility to achieve reliable annotations in an efficient and scalable manner. The proposed methodology and framework is meant to be generalizable to other knowledge intensive and specialized domains.
Where Can I Buy a Boulder?: Searching for Offline Retail Locations
Authors: Sandro Bauer, Filip Radlinski, Ryen W. White
Venue: WWW
Year: 2016

People commonly need to purchase things in person, from large garden supplies to home decor. Although modern search systems are very effective at finding online products, little research attention has been paid to helping users find places that sell a specific product offline. For instance, users searching for an apron are not typically directed to a nearby kitchen store by a standard search engine. In this paper, we investigate "where can I buy"-style queries related to in-person purchases of products and services. Answering these queries is challenging since little is known about the range of products sold in many stores, especially those which are smaller in size. To better understand this class of queries, we first present an in-depth analysis of typical offline purchase needs as observed by a major search engine, producing an ontology of such needs. We then propose ranking features for this new problem, and learn a ranking function that returns stores most likely to sell a queried item or service, even if there is very little information available online about some of the stores. Our final contribution is a new evaluation framework that combines distance with store relevance in measuring the effectiveness of such a search system. We evaluate our method using this approach and show that it outperforms a modern web search engine.
Finding All Maximal Paths In Web User Sessions
Authors: Murat Ali Bayir, Ismail Hakki Toroslu
Venue: WWW
Year: 2016

This paper introduces a new method for the session construction problem, which is the first main step of the web usage mining process. The proposed method is capable of extracting all possible maximal navigation sequences of web users. Through experiments, it is shown that when our new technique is used, it outperforms previous approaches in web usage mining applications such as next-page prediction.
SAVE-SD 2016: Second Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data
Authors: Alejandra N. González-Beltrán, Francesco Osborne, Silvio Peroni
Venue: WWW
Year: 2016

The second edition of the Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data (SAVE-SD 2016) is held in Montreal, Canada on April 11, 2016 and co-located with the 25th International World Wide Web Conference. Its main goal is bringing together publishers, companies and researchers working on semantics, analytics and visualisation of scholarly data in order to bridge the gap between the theoretical/academic and practical/industrial aspects in this field.
Understanding the Competitive Landscape of News Providers on Social Media
Authors: Devipsita Bhattacharya, Sudha Ram
Venue: WWW
Year: 2016

Social media has emerged as a mechanism for online news propagation. This in turn has changed the competitive landscape of news providers, a landscape that was previously partitioned based on the traditional channels of news dispersion. The channels of news distribution refer to - television, newspaper, magazine, radio, news agency and online only. In this paper, we examine similarities and differences in news propagation patterns on social media based on the primary channel of a news provider. We collected news article propagation activity data from Twitter for 32 news providers over a three-week period and analyzed their propagation networks. Our analysis shows that the structural properties of the propagation networks are statistically different based on the type of primary channel. Our study has useful implications for understanding the competition between news providers in an online environment.
Modeling a Retweet Network via an Adaptive Bayesian Approach
Authors: Bin Bi, Junghoo Cho
Venue: WWW
Year: 2016

Twitter (and similar microblogging services) has become a central nexus for discussion of the topics of the day. Twitter data contains rich content and structured information on users' topics of interest and behavior patterns. Correctly analyzing and modeling Twitter data enables the prediction of the user behavior and preference in a variety of practical applications, such as tweet recommendation and followee recommendation. Although a number of models have been developed on Twitter data in prior work, most of these only model the tweets from users, while neglecting their valuable retweet information in the data. Models would enhance their predictive power by incorporating users' retweet content as well as their retweet behavior. In this paper, we propose two novel Bayesian nonparametric models, URM and UCM, on retweet data. Both of them are able to integrate the analysis of tweet text and users' retweet behavior in the same probabilistic framework. Moreover, they both jointly model users' interest in tweet and retweet. As nonparametric models, URM and UCM can automatically determine the parameters of the models based on input data, avoiding arbitrary parameter settings. Extensive experiments on real-world Twitter data show that both URM and UCM are superior to all the baselines, while UCM further outperforms URM, confirming the appropriateness of our models in retweet modeling.
Exploiting Green Energy to Reduce the Operational Costs of Multi-Center Web Search Engines
Authors: Roi Blanco, Matteo Catena, Nicola Tonellotto
Venue: WWW
Year: 2016

Carbon dioxide emissions resulting from fossil fuels (brown energy) combustion are the main cause of global warming due to the greenhouse effect. Large IT companies have recently increased their efforts in reducing the carbon dioxide footprint originated from their data center electricity consumption. On one hand, better infrastructure and modern hardware allow for a more efficient usage of electric resources. On the other hand, data-centers can be powered by renewable sources (green energy) that are both environmental friendly and economically convenient. In this paper, we tackle the problem of targeting the usage of green energy to minimize the expenditure of running multi-center Web search engines, i.e., systems composed by multiple, geographically remote, computing facilities.
A Neural Click Model for Web Search
Authors: Alexey Borisov, Ilya Markov, Maarten de Rijke, Pavel Serdyukov
Venue: WWW
Year: 2016

Understanding user browsing behavior in web search is key to improving web search effectiveness. Many click models have been proposed to explain or predict user clicks on search engine results. They are based on the probabilistic graphical model (PGM) framework, in which user behavior is represented as a sequence of observable and hidden events. The PGM framework provides a mathematically solid way to reason about a set of events given some information about other events. But the structure of the dependencies between the events has to be set manually. Different click models use different hand-crafted sets of dependencies. We propose an alternative based on the idea of distributed representations: to represent the user's information need and the information available to the user with a vector state. The components of the vector state are learned to represent concepts that are useful for modeling user behavior. And user behavior is modeled as a sequence of vector states associated with a query session: the vector state is initialized with a query, and then iteratively updated based on information about interactions with the search engine results. This approach allows us to directly understand user browsing behavior from click-through data, i.e., without the need for a predefined set of rules as is customary for PGM-based click models. We illustrate our approach using a set of neural click models. Our experimental results show that the neural click model that uses the same training data as traditional PGM-based click models, has better performance on the click prediction task (i.e., predicting user click on search engine results) and the relevance prediction task (i.e., ranking documents by their relevance to a query). An analysis of the best performing neural click model shows that it learns similar concepts to those used in traditional click models, and that it also learns other concepts that cannot be designed manually.
Enriching Topic Modelling with Users' Histories for Improving Tag Prediction in Q&A Systems
Authors: Glenn Boudaer, Johan Loeckx
Venue: WWW
Year: 2016

The automatic attribution of tags in Question & Answering (Q&A) systems like Stack Exchange can significantly reduce the human effort in tagging as well as improve the consistency among users. Existing approaches typically either rely on Natural Language Processing solely or employ collaborative filtering techniques. In this paper, we attempt to combine the best of both worlds by investigating whether incorporating a personal profile, consisting of a user's history or its social network can significantly improve the predictions of state-of-the-art text-based methods. Our research has found that enriching content-based text features with this personal profile allows to trade-off the precision of predictions for recall and as such improve the "exact match" (predicting the number of tags and the tags themselves correctly) in a multi-label setting from a baseline of 18.2% text-only to 54.3%.
Structural Normalisation Methods for Improving Best Answer Identification in Question Answering Communities
Authors: Grégoire Burel, Paul Mulholland, Harith Alani
Venue: WWW
Year: 2016

Nowadays, Question Answering (Q&A) websites are popular source of information for finding answers to all kind of questions. Due to this popularity it is critical to help the identification of best answers to existing questions for simplifying the access to relevant information.
An Empirical Study of Web Cookies
Authors: Aaron Cahn, Scott Alfeld, Paul Barford, S. Muthukrishnan
Venue: WWW
Year: 2016

Web cookies are used widely by publishers and 3rd parties to track users and their behaviors. Despite the ubiquitous use of cookies, there is little prior work on their characteristics such as standard attributes, placement policies, and the knowledge that can be amassed via 3rd party cookies. In this paper, we present an empirical study of web cookie characteristics, placement practices and information transmission. To conduct this study, we implemented a lightweight web crawler that tracks and stores the cookies as it navigates to websites. We use this crawler to collect over 3.2M cookies from the two crawls, separated by 18 months, of the top 100K Alexa web sites. We report on the general cookie characteristics and add context via a cookie category index and website genre labels. We consider privacy implications by examining specific cookie attributes and placement behavior of 3rd party cookies. We find that 3rd party cookies outnumber 1st party cookies by a factor of two, and we illuminate the connection between domain genres and cookie attributes. We find that less than 1% of the entities that place cookies can aggregate information across 75% of web sites. Finally, we consider the issue of information transmission and aggregation by domains via 3rd party cookies. We develop a mathematical framework to quantify user information leakage for a broad class of users, and present findings using real world domains. In particular, we demonstrate the interplay between a domain's footprint across the Internet and the browsing behavior of users, which has significant impact on information transmission.
Language Identification for Social Media: Short Messages and Transliteration
Authors: Pedro Miguel Dias Cardoso, Anindya Roy
Venue: WWW
Year: 2016

Conversations on social media and microblogging websites such as Twitter typically consist of short and noisy texts. Due to the presence of slang, misspellings, and special elements such as hashtags, user mentions and URLs, such texts present a challenging case for the task of language identification. Furthermore, the extensive use of transliteration for languages such as Arabic and Russian that do not use Latin script raises yet another problem.
Embedded Metadata and the Digital Lifecycle of Images: Methodological Challenges
Authors: Nathalie Casemajor
Venue: WWW
Year: 2016

This paper analyses the specificities of metadata embedded in photographic images. It investigates how embedded metadata can help studying the usage patterns and conditions of circulation of images on digital networks.
Automatic Extraction of Indicators of Compromise for Web Applications
Authors: Onur Catakoglu, Marco Balduzzi, Davide Balzarotti
Venue: WWW
Year: 2016

Indicators of Compromise (IOCs) are forensic artifacts that are used as signs that a system has been compromised by an attack or that it has been infected with a particular malicious software. In this paper we propose for the first time an automated technique to extract and validate IOCs for web applications, by analyzing the information collected by a high-interaction honeypot.
"What Is the City but the People?": Exploring Urban Activity Using Social Web Traces
Authors: Emre Çelikten, Géraud Le Falher, Michael Mathioudakis
Venue: WWW
Year: 2016

We demonstrate Geotopics, a system to explore geographical patterns of urban activity. The system collects publicly shared check-ins generated by Foursquare users, that reveal who spends time where, when, and on what type of activity. It then employs sparse probabilistic modeling techniques to learn associations between different regions of a city and multi-feature descriptions of urban activity. Through a web interface, users of the system can select a city of interest and explore visualizations that highlight how different types of activity are spatially and temporally distributed in the city.
A Machine learning Filter for Relation Extraction
Authors: Kevin Lange Di Cesare, Michel Gagnon, Amal Zouaq, Ludovic Jean-Louis
Venue: WWW
Year: 2016

The TAC KBP English slot filling track is an evaluation campaign that targets the extraction of 41 pre-identified relations related to specific named entities. In this work, we present a machine learning filter whose aim is to enhance the precision of relation extractors while minimizing the impact on recall. Our approach aims at filtering relation extractors' output using a binary classifier based on a wide array of features including syntactic, lexical and statistical features. We experimented the classifier on 14 of the 18 participating systems in the TAC KBP English slot filling track 2013. The results show that our filter is able to improve the precision of the best 2013 system by nearly 20\% and improve the F1-score for 17 relations out of 33 considered.