OpenRaiser/Intern-Atlas huggingface dataset:
Employing Robot Journalists: Legal Implications, Considerations and Recommendations
Authors: Pieter-Jan Ombelet, Aleksandra Kuczerawy, Peggy Valcke
Venue: WWW
Year: 2016

Algorithmic processes that convert data into narrative news texts allow news rooms to publish stories with limited to no human intervention (Carlson, 2015, p. 416). The new trend creates many opportunities, but also raises significant legal questions. Aside from financial benefits, further refinement could make the smart algorithms capable of writing less standard, maybe even opinion, pieces. The responsible human merely needs to define clear questions about what the algorithm needs to discuss in the article and in what manner. But how does it square with the traditional rules of publishing, editorial control and the privacy and data protection framework? This paper analyses the legal implications when employing robot journalists. More specifically, the question of authorship for algorithmic output and the liability issues that could arise when the algorithmic output includes unlawful personal data processing as well as inaccurate, harmful or even illegal content will be assessed. The analysis is performed analyzing European legislation on copyright and data protection and applying Belgian legislation on press liability as a consistent country example to support certain legal considerations and conclusions. Furthermore, the paper answers the question as to how publishers could prevent the creation of inaccurate content by the algorithms they use.
Discovery of Topical Authorities in Instagram
Authors: Aditya Pal, Amaç Herdagdelen, Sourav Chatterji, Sumit Taank, Deepayan Chakrabarti
Venue: WWW
Year: 2016

Instagram has more than 400 million monthly active accounts who share more than 80 million pictures and videos daily. This large volume of user-generated content is the application's notable strength, but also makes the problem of finding the authoritative users for a given topic challenging. Discovering topical authorities can be useful for providing relevant recommendations to the users. In addition, it can aid in building a catalog of topics and top topical authorities in order to engage new users, and hence provide a solution to the cold-start problem. In this paper, we present a novel approach that we call the Authority Learning Framework (ALF) to find topical authorities in Instagram. ALF is based on the self-described interests of the follower base of popular accounts. We infer regular users' interests from their self-reported biographies that are publicly available and use Wikipedia pages to ground these interests as fine-grained, disambiguated concepts. We propose a generalized label propagation algorithm to propagate the interests over the follower graph to the popular accounts. We show that even if biography-based interests are sparse at an individual user level they provide strong signals to infer the topical authorities and let us obtain a high precision authority list per topic. Our experiments demonstrate that ALF performs significantly better at user recommendation task compared to fine-tuned and competitive methods, via controlled experiments, in-the-wild tests, and over an expert-curated list of topical authorities.
TRecSo: Enhancing Top-k Recommendation With Social Information
Authors: Chanyoung Park, Donghyun Kim, Jinoh Oh, Hwanjo Yu
Venue: WWW
Year: 2016

Due to the data sparsity problem, social network information is often additionally used to improve the performance of recommender system. While most existing works exploit social information to reduce the rating prediction error, e.g., RMSE, a few had aimed to improve the top-k ranking prediction accuracy. This paper proposes a novel top-k oriented recommendation method, TRecSo, which incorporates social information into recommendation by modeling two different roles of users as trusters and trustees while considering the structural information of the network. Empirical studies on real-world datasets demonstrate that TRecSo leads to remarkable improvement compared to previous methods in top-k recommendation.
Technological Ecosystems' Role in Preventing Neo-Feudalism in Smart-City Informatization
Authors: Alois Paulin
Venue: WWW
Year: 2016

The paper argues that the Smart City idea lacks grounding in shared base technology and instead yields black-box artefacts. The reliance on black-box systems in public governance is considered a great hazard since it may result in sinecures, stifles democratic control of the public domain, and results in neo-feudal monopolies. Base technology (such as the WWW technology stack) on the other hand is use-neutral, implementation-neutral, open, and teach-/learn-able, thus enabling the emergence of cascading technological ecosystems, which can drive large-scale economic and societal progress. The concepts of a primary, secondary, and tertiary technological ecosystem are introduced to delineate the role and importance of base technology. The paper calls for stronger focus on Smart City foundational research and a change in culture from quick fixes to solutions that would survive generations.
Correlational Analysis Between School Performance and Municipal Indicators in Brazil Supported by Linked Open Data
Authors: Bruno Elias Penteado
Venue: WWW
Year: 2016

The advance in quality of public education is a challenge to public managers in contemporary society. In this sense, many studies point to the strong influence of socioeconomical factors in school performance but it is a challenge to select proper data to perform analyses on this matter. In tandem, it has happening a growth in provision of big quantities of educational indicators data, but in isolate cases, and by different agencies of Brazilian government. For this work, we use both education and economic indicators for analysis. The following socioeconomical indicators were selected: municipal human development index (MHDI), social vulnerability index (SVI), Gini coefficient and variables extracted from DBpedia, as part of the connection of this data to the Web of data: GDP per capita and municipal population. These data were used as independent variables to look into their correlations with Brazilian Basic Education Development Index (IDEB) performances at municipal level, supported by the application of linked open data principles. OpenRefine was used to extract the data from different sources, convert to RDF triples and then the mapping of the variables to existing ontologies and vocabularies in this domain, aiming at the reuse of existing semantics. The correlational analysis of the variables showed coherence with the literature about the theme, with significative magnitude between IDEB performances and the indicators related to income and parent education (SVI and HDI), besides moderate relations with the other varibles, except for the municipal population. Finally, the consolidated dataset, enriched by information extracted DBpedia was made available by a SPARQL endpoint for queries of humans and software agents, allowing other applications and researchers to explore the data from other platforms.
Address Geocoding using Street Profiles for Local Search
Authors: Michael Peterman, Omar Benomar, Hacene Mechedou, Félix-Hervé Bachand
Venue: WWW
Year: 2016

Geocoding is the process of converting addresses to geocoordinates. It is widely used in several fields such as public health to monitor socioeconomic inequalities for example or in Geographical Information Systems (GIS) to be able to use with its provided features. In this work, we describe a method to create an address geocoder from a free and open government street lines data source. The address geocoder transforms a street address into a location typically measured in latitude-longitude coordinates. The address geocoder is used in a search engine to relate spatial data to search results and improve accuracy.
A Work-Flow for Empirical Exploration of Security Events
Authors: Martin Pirker, Andreas Nusser
Venue: WWW
Year: 2016

As the internet continuously expands, information security research is a never ending challenge. It is impossible to know all internet participants, protocols and their applications. Instead, security research focuses on empirically collected real-world data; stores, processes, transforms and analyses the data, in order to learn from it and its anomalies --- security issues --- as they happen. This paper presents one practical work-flow for collection and processing of security related data. It present a hard- and software setup, experiences made, and estimates future developments. This gives others the opportunity to learn and identify areas for improvement, especially those in the early stages of setting up a research project based on empirically gathered data.
RECAP: Building Relatedness Explanations on the Web
Authors: Giuseppe Pirrò, Alfredo Cuzzocrea
Venue: WWW
Year: 2016

We describe RECAP, a tool that, given a pair of entities defined in some Knowledge Graph (KG), builds an explanation, that is, a graph (of manageable size) reflecting their relatedness. Explanations enable to discover new knowledge and browse toward other entities of interest. We discuss different kinds of explanations based on information theory and diversity. The KG-agnostic approach adopted by RECAP, which retrieves the necessary information via SPARQL queries, makes it readily usable on a variety of KGs.
Knowledge Extraction in Web Media: At The Frontier of NLP, Machine Learning and Semantics
Authors: Julien Plu
Venue: WWW
Year: 2016

We identify two main factors that can cause numerous difficulties when developing a generic entity linking system: i) the amount of data currently available on the Web that do not stop to increase and where a large part comes in the form of natural language texts; ii) the velocity at which data is published that may impose to process streams of text in near real-time. Social media platforms such as Twitter, Facebook or LinkedIn become a reliable source of news and play a key role for being aware of events around the world. Encyclopedia and newspaper articles contain general knowledge of our world and they can be used to explain concepts and known entities. Videos can be associated with subtitles and images may have captions. Depending on where a text comes from, it can have different properties such as a specific language, style of writing or topic. In this research, we present a preliminary framework based on a novel hybrid architecture for an entity linking system, that combines methods from the Natural Language Processing (NLP), information retrieval and semantic fields. In particular, we propose a modular approach in order to be as independent as possible of the text to be processed. Our evaluation suggests that this framework can outperform the state-of-the-art systems or show encouraging results on three datasets: OKE2015, #Micropost 2014 and #Micropost 2015. We identify the current limitations and we provide promising future research directions.
Towards a Ubiquitous Model of an Individual in Social Machines
Authors: Shivani Poddar, Sindhu Kiranmai Ernala, Navjyoti Singh, Ashin Samvara
Venue: WWW
Year: 2016

It is the activities of individuals that lead to formation and changes within any social system. Hence, the "social" component in a social machine (socio-technical machine) can be understood constructively from the conception of an individual as a machine. In this work, we present a stochastic finite state machine model of an individual based on Abhidhamma tradition of Buddhism. The machine models moment to moment states of consciousness of an individual in terms of the Buddhist formal ontology that constitutes an individual. Thus, the key contribution of our research is a ubiquitous framework of an individual which unifies the idea of a human agent across all possible social machines. It is shown that from web data of a particular individual this machine can be populated. We expound how our model solves issues pertaining to varied temporal granules and sparsity of data. We further illustrate through an example as to how our approach can unify the conceptualizations of an individual from the numerous ideologies and definitions of a social machine. As a part of our future work, we hope to align this proposed stochastic machine with social observatories on internet.
How Powerful are You?: gSPIN: Bringing Power Analysis to Your Finger Tips
Authors: Vinodkumar Prabhakaran, Michael Saltzman, Owen Rambow
Venue: WWW
Year: 2016

We present the SPIN system, a computational tool to detect linguistic and dialog structure patterns in a social interaction that reveal the underlying power relations between its participants. The SPIN system labels sentences in an interaction with their dialog acts (i.e., communicative intents), detects instances of overt display of power, and predicts social power relations between its participants. We also describe a Google Chrome browser extension, namely gSPIN, to illustrate an exciting use-case of the SPIN system, which will be demonstrated at the demo session during the conference.
On the Discovery of Success Trajectories of Authors
Authors: Dinesh Pradhan, Tanmoy Chakraborty, Saswata Pandit, Subrata Nandi
Venue: WWW
Year: 2016

Understanding the qualitative patterns of research endeavor of scientific authors in terms of publication count and their impact (citation) is important in order to quantify success trajectories. Here, we examine the career profile of authors in computer science and physics domains and discover at least six different success trajectories in terms of normalized citation count in longitudinal scale. Initial observations of individual trajectories lead us to characterize the authors in each category. We further leverage this trajectory information to build a two-stage stratification model to predict future success of an author at the early stage of her career. Our model outperforms the baseline with an average improvement of 15.68% for both the datasets.
BBookX: Design of an Automated Web-based Recommender System for the Creation of Open Learning Content
Authors: Bart Pursel, Chen Liang, Shuting Wang, Zhaohui Wu, Kyle Williams, Benjamin Bräutigam, Sherwyn Saul, Hannah Williams, Kyle Bowen, C. Lee Giles
Venue: WWW
Year: 2016

We describe BBookX, a web-based tool that uses a human-computing approach to facilitate the creation of open source textbooks. The goal of BBookX is to create a system that can search various Open Educational Resource (OER) repositories such as Wikipedia, based on a set of user-generated criteria, and return various resources that can be combined, remixed, and re-used to support specific learning goals. As BBookX is a work-in-progress, we are in the midst of a design-based research study, where user testing guided multiple rounds of iteration in the design of the user interface (UI) as well as the query engine. From an interface perspective, the challenges we present are the matching of the UI to users' mental models from similar systems, as well as educating users how to best work with the algorithms in an iterative manner to find and refine content for inclusion into open textbooks.
Who Benefits from the "Sharing" Economy of Airbnb?
Authors: Giovanni Quattrone, Davide Proserpio, Daniele Quercia, Licia Capra, Mirco Musolesi
Venue: WWW
Year: 2016

Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of ``algorithmic regulation''.
SMARTBUS: A Web Application for Smart Urban Mobility and Transportation
Authors: Sudha Ram, Yun Wang, Faiz Currim, Fan Dong, Ezequiel Dantas, Luiz Alberto Sabóia
Venue: WWW
Year: 2016

Systematic evaluation is crucial to the management and development of smart urban transportation, as it allows transportation planners to better understand the impact of their decisions and design targeted interventions to improve efficiency. Implementation of smart and adaptable public transportation is an important challenge in developing cities and newly industrialized economies where growth characteristics contribute to and can be impacted by factors like overcrowding and travel delays. In this paper, we focus on bus transportation, and present the design and implementation of a 3-layer web-based system for performance evaluation and decision support. This is part of a "Smart Cities" initiative, which is an international collaboration between academia and government. The first layer estimates fundamental indicators such as bus travel time and passenger demands by integrating heterogeneous data sources. A novel bus-stop network is then designed in the second layer, which enables the derivation of passenger patterns in public transit using network analysis. The third layer provides decision support by analyzing causal relationships between indicators. The proposed web-based system called SMARTBUS is being developed and validated with the city of Fortaleza in Brazil. We believe the use of generally available urban transportation data makes our methodology adaptable and customizable for other cities.
What Makes a Music Track Popular in Online Social Networks?
Authors: Jing Ren, Jialie Shen, Robert J. Kauffman
Venue: WWW
Year: 2016

Tens of thousands of music tracks are uploaded to the Internet every day through social networks that focus on music and videos, as well as portal websites. While some of the content has been popular for decades, some tracks that have just been released have been completely ignored. So what makes a music track popular? Can we predict the popularity of a music track before it is released? In this research, we will focus on an online music social network, Last.fm, and investigate three key factors of a music track that may have impact on its popularity. They include: the music content, the artist reputation and the social context of the music. The results suggest that we can predict the future popularity of music with around 80% accuracy using just these three factors. We also found out that in the social networks scenario, the content of the music seems to be an surprisingly important factor that determines the popularity of a track online.
Combining NLP And Semantics For Mining Software Technologies From Research Publications
Authors: Hélène de Ribaupierre, Francesco Osborne, Enrico Motta
Venue: WWW
Year: 2016

The natural language processing (NLP) community has developed a variety of methods for extracting and disambiguating information from research publications. However, they usually focus only on standard research entities such as authors, affiliations, venues, references and keywords. We propose a novel approach, which combines NLP and semantic technologies for generating from the text of research publications an OWL ontology describing software technologies used or introduced by researchers, such as applications, systems, frameworks, programming languages, and formats. The method was tested on a sample of 300 publications in the Semantic Web field, yielding promising results.
FindYou: A Personal Location Privacy Auditing Tool
Authors: Christopher J. Riederer, Daniel Echickson, Stephanie Huang, Augustin Chaintreau
Venue: WWW
Year: 2016

The ubiquitous availability of location data to smartphone apps and online social networks has caused the collection of such information to grow at an unprecedented rate. However, the discriminative power and potential uses of this data collection is not always clear to the end user. In this work, we present FindYou, a web-based application that gives users the ability to perform a location data privacy audit. FindYou lets users import and visualize the location data collected by popular web services in order to understand what these companies know or can easily infer about them. Additionally, FindYou gives users the option to donate their data to the scientific community, creating new mobile datasets linked to user properties that will be open to use by academic institutions. We hope that FindYou will increase awareness of the privacy issues surrounding the collection and use of location data, the potential problem of "digital red-lining", and also create valuable new datasets with the full informed consent of interested users.
Just in Time: Controlling Temporal Performance in Crowdsourcing Competitions
Authors: Markus Rokicki, Sergej Zerr, Stefan Siersdorfer
Venue: WWW
Year: 2016

Many modern data analytics applications in areas such as crisis management, stock trading, and healthcare, rely on components capable of nearly real-time processing of streaming data produced at varying rates. In addition to automatic processing methods, many tasks involved in those applications require further human assessment and analysis. However, current crowdsourcing platforms and systems do not support stream processing with variable loads. In this paper, we investigate how incentive mechanisms in competition based crowdsourcing can be employed in such scenarios. More specifically, we explore techniques for stimulating workers to dynamically adapt to both anticipated and sudden changes in data volume and processing demand, and we analyze effects such as data processing throughput, peak-to-average ratios, and saturation effects. To this end, we study a wide range of incentive schemes and utility functions inspired by real world applications. Our large-scale experimental evaluation with more than 900 participants and more than 6200 hours of work spent by crowd workers demonstrates that our competition based mechanisms are capable of adjusting the throughput of online workers and lead to substantial on-demand performance boosts.
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve
Authors: Maja R. Rudolph, Joseph G. Ellis, David M. Blei
Venue: WWW
Year: 2016

Many online companies sell advertisement space in second-price auctions with reserve. In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price. We use historical auction data with features to fit a predictor of the best reserve price. This problem is delicate - the structure of the auction is such that a reserve price set too high is much worse than a reserve price set too low. To address this we develop objective variables, an approach for combining probabilistic modeling with optimal decision-making. Objective variables are "hallucinated observations" that transform the revenue maximization task into a regularized maximum likelihood estimation problem, which we solve with the EM algorithm. This framework enables a variety of prediction mechanisms to set the reserve price. As examples, we study objective variable methods with regression, kernelized regression, and neural networks on simulated and real data. Our methods outperform previous approaches both in terms of scalability and profit.