OpenRaiser/Intern-Atlas huggingface dataset:
Trust-aware Peer Assessment using Multi-armed Bandit Algorithms
Authors: Hou Pong Chan, Tong Zhao, Irwin King
Venue: WWW
Year: 2016

Massive Open Online Coursers (MOOCs) offer a convenient way for people to access quality courses via the internet. However, the problem of grading open-ended assignments at such a large scale still remains challenging. Although peer assessment have been proposed to handle the large-scale grading problem in MOOCs, existing methods still suffer several limitations: (1) most current peer assessment research ignore the importance of how to allocate the assessment tasks among peers, (2) existing approaches for peer grading learn the complete ranking in an offline manner, (3) theoretical analysis for trust-aware peer grading is missing. In this work, we consider the case that we have prior knowledge about all students' reliability. We formulate the problem of peer assessment as a sequential noisy ranking aggregation problem. We derive a trust-aware allocation scheme for peer assessment to maximize the probability of constructing a correct ranking of assignments with a budget constraint.Moreover, we also derive an upper bound for the probability of prediction error on the inferred ranking of assignments. Furthermore, we propose the Trust-aware Ranking-based Multi-armed Bandit Algorithms to sequentially allocate the assessment tasks to the students based on the derived allocation scheme and learn an accurate peer grading result by taking students' reliability into consideration.
Open Data for Local Search: Challenges and Perspectives
Authors: Eric Charton, Nizar Ghoula, Marie-Jean Meurs
Venue: WWW
Year: 2016

Local search engines are specialized information retrieval systems enabling users to discover amenities and services in their neighbourhood. Developing a local search system still raises scientific questions, as well as very specific technical issues. Those issues come for example from the lack of information about local events and actors, or the specific form taken by the indexable data. Available open data can be exploited to dramatically improve the design of local search engines and their content. The purpose of this workshop is to explore new fields of investigation both in terms of algorithmic approaches as well as originality of usable data. The workshop focuses on how open data can be used to enhance the capabilities of local search engines.
Automatic Discovery of Emerging Trends using Cluster Name Synthesis on User Consumption Data: Extended Abstract
Authors: Tanushyam Chattopadhyay, Santa Maiti, Arindam Pal, Avik Ghose, Arpan Pal, Shanky Viswanathan, Narendran Sivakumar
Venue: WWW
Year: 2016

A business problem for the telecommunication companies is to provide an appropriate promotional coupon to suitable customers. This problem leads to the challenge of identifying behavioral patterns of customers and deliver the right customer engagement at the right time. So there is a need for a system that can enable the telecommunication companies to go for the best marketing strategy by leveraging customer intelligence to drive offer acceptance based on personas. Technically it is possible for the telecommunication companies to recommend suitable advertisements if they can classify the web sites browsed by their customers into classes like sports, e-commerce, social networking, streaming media etc. Another problem is to classify a new website when it doesn't belong to any of the existing clusters. In this paper, the authors are going to propose a method to automatically classify the websites and synthesize the cluster names in case it doesn't belong to any of the predefined clusters. We have experimented on a small set of data set and the classification results are quite convincing. Moreover, the phrases used to describe a website if it doesn't belong to existing classes are compliant to the phrases obtained from manual annotation. This proposed system uses the Wikipedia data to construct the document for the websites browsed by the customers.
Importance of First Steps in a Community for Growth, Diversity, and Engagement
Authors: Atef Chaudhury, Myunghwan Kim, Mitul Tiwari
Venue: WWW
Year: 2016

In both the online and offline world, networks that people form within certain communities are critical for their engagement and growth in those communities. In this work, we analyze the growth of ego-networks on LinkedIn for new employees of companies, and study how the pattern of network formation in the new company affects one's growth and engagement in that company. We observe that the initial state of ego-network growth in a newly joined company shows strong correlations with the future status in the company -- such as network size, network diversity, and retention. We also present some key patterns that demonstrate the importance of the first few connections in the new company as well as how they lead to the phenomena we observed.
Longitudinal Study of the Use of Client-side Security Mechanisms on the European Web
Authors: Ping Chen, Lieven Desmet, Christophe Huygens, Wouter Joosen
Venue: WWW
Year: 2016

As the web rapidly expands and gets integrated into all kinds of business, browsing the web has become an important part of people's daily lives. With the rising importance of various web applications sit in a browser, attackers also shifted their focus towards client-side attacks. To defend against these attacks, numerous client-side security mechanisms for the browser are proposed. The presence of these mechanisms on a website can be used as an indicator of the security awareness and practices of that website.
Enriching How-to Guides by Linking Actionable Phrases
Authors: Alexandr Chernov, Nikolaos Lagos, Matthias Gallé, Ágnes Sándor
Venue: WWW
Year: 2016

The World Wide Web contains a large number of community created knowledge of instructional nature. Similarly, in a commercial setting, databases of instructions are used by customer-care providers to guide clients in the resolution of issues. Most of these instructions are expressed in natural language. Knowledge Bases including such information are valuable through the sum of their single entries. However, as each entry is created mostly independently, users (e.g. other community members) cannot take advantage of the accumulated knowledge that can be developed via the aggregation of related entries. In this paper we consider the problem of inter-linking Knowledge Base entries, in order to get relevant information from other parts of the Knowledge Base. To achieve this, we propose to detect \textit{actionable phrases} -- text fragments that describe how to perform a certain action -- and link them to other entries. The extraction method that we implement achieves an F-score of 67.35\%. We also show that using actionable phrases results in better linking quality than using coarser-grained spans of text, as proposed in the literature. Besides the evaluation of both steps, we also include a detailed error analysis and release our annotation to the community.
On Sampling Nodes in a Network
Authors: Flavio Chierichetti, Anirban Dasgupta, Ravi Kumar, Silvio Lattanzi, Tamás Sarlós
Venue: WWW
Year: 2016

Random walk is an important tool in many graph mining applications including estimating graph parameters, sampling portions of the graph, and extracting dense communities. In this paper we consider the problem of sampling nodes from a large graph according to a prescribed distribution by using random walk as the basic primitive. Our goal is to obtain algorithms that make a small number of queries to the graph but output a node that is sampled according to the prescribed distribution. Focusing on the uniform distribution case, we study the query complexity of three algorithms and show a near-tight bound expressed in terms of the parameters of the graph such as average degree and the mixing time. Both theoretically and empirically, we show that some algorithms are preferable in practice than the others. We also extend our study to the problem of sampling nodes according to some polynomial function of their degrees; this has implications for designing efficient algorithms for applications such as triangle counting.
People and Cookies: Imperfect Treatment Assignment in Online Experiments
Authors: Dominic Coey, Michael Bailey
Venue: WWW
Year: 2016

Identifying the same internet user across devices or over time is often infeasible. This presents a problem for online experiments, as it precludes person-level randomization. Randomization must instead be done using imperfect proxies for people, like cookies, email addresses, or device identifiers. Users may be partially treated and partially untreated as some of their cookies are assigned to the test group and some to the control group, complicating statistical inference. We show that the estimated treatment effect in a cookie-level experiment converges to a weighted average of the marginal effects of treating more of a user's cookies. If the marginal effects of cookie treatment exposure are positive and constant, it underestimates the true person-level effect by a factor equal to the number of cookies per person. Using two separate datasets---cookie assignment data from Atlas and advertising exposure and purchase data from Facebook---we empirically quantify the differences between cookie and person-level advertising effectiveness experiments. The effects are substantial: cookie tests underestimate the true person-level effects by a factor of about three, and require two to three times the number of people to achieve the same power as a test with perfect treatment assignment.
Generative Mappings of New Data Publics
Authors: Beth Coleman
Venue: WWW
Year: 2016

I argue for a vital relationship between current understanding of Big Data and how we might move toward new modes of mobile application design that draw on affective and generative "data sets". Toward this end, I look at projects that use the city as a living laboratory, including mobile apps, social media mapping, and various civic uses of Internet of Things (IoT) and surveillance technology. I chose these case studies for their appropriation of psychogeographic "small data" to map a city as they engage affective and immersive aspects of interaction and spatial design.
FuhSen: A Platform for Federated, RDF-based Hybrid Search
Authors: Diego Collarana, Christoph Lange, Sören Auer
Venue: WWW
Year: 2016

The increasing amount of structured and semi-structured information available on the Web and in distributed information systems, as well as the Web's diversification into different segments such as the Social Web, the Deep Web, or the Dark Web, requires new methods for horizontal search. FuhSen is a federated, RDF-based, hybrid search platform that searches, integrates and summarizes information about entities from distributed heterogeneous information sources using Linked Data. As a use case, we present scenarios where law enforcement institutions search and integrate data spread across these different Web segments to identify cases of organized crime. We present the architecture and implementation of FuhSen and explain the queries that can be addressed with this new approach.
Turning Down the Noise in Classrooms
Authors: Mariheida Cordova-Sanchez, Pinar Yanardag
Venue: WWW
Year: 2016

The use of micro-blogging in classrooms is a recently trending concept in computer-aided education. Micro-blogs offer an effective way of communication in large classrooms, and engage students in meaningful discussions. However, existing micro-blogging systems in education setting suffer from a few drawbacks. First, relevant content might be overwhelmed by irrelevant posts to the lecture which could jeopardize effective learning. Second, students might generate redundant content by posting similar questions to each other and create substantial information overload. Third, posts covering different aspects of the class might be left undiscovered due to real-time characteristics of micro-blogs. To address these issues, we present a principled approach for picking a set of posts that promotes relevant and diverse content while effectively turning down the noise created by redundant posts. We formulate this task as a submodular optimization problem for which we provide an efficient and near-optimal solution. We evaluate our framework on real micro-blog based classroom datasets and our empirical results demonstrate that our framework is effectively able to cover the most important and diverse content that is being discussed in classrooms.
Emerging a Web Science Curriculum
Authors: Elisabeth Coskun, Su White
Venue: WWW
Year: 2016

This text describes a project which aims to explore the scope of the discipline Web Science; an emerging subject which is fundamentally inter-disciplinary. There are very few definitive subject definitions currently available for Web Science. Additionally, the nature of the subject is constantly evolving as an increasing number of different disciplines begin to practice what might identifiably be called Web Science. This potentially provides educators and students with a problem; how do you teach or learn about Web Science when there is no clear definition? This text provides a brief overview of a PhD project, the final aim of which involves the emergence of a framework for a working definition of Web Science. This will be achieved by an examination and overview of current existing Web Science curricula, as well as available Web Science literature.
Online Knowledge Triage: Searching, Detecting, Labelling and Orienting User Generated Content
Authors: Jean-Michel Dalle, Catherine Faron-Zucker, Fabien Gandon, Mathieu Lacage, Zide Meng
Venue: WWW
Year: 2016

This position paper provides an overview of the OCKTOPUS project whose goal is to increase the social and economic benefit of user-generated content, by transforming it into knowledge which can be shared and reused broadly.
CommentIQ: Enhancing Journalistic Curation of Online News Comments
Authors: Nicholas Diakopoulos
Venue: WWW
Year: 2016

National news outlets routinely publish articles that attract hundreds and even thousands of user comments. These comments often provide valuable feedback and critique, personal perspectives, new information and expertise, and opportunities for discussion (not to mention profanity and vitriol). The varying quality of comments demands a high level of moderation and curatorial attention in order to cultivate a successful online community around news. Amongst publishers there is a growing awareness that finding and publicly highlighting high quality comments can in turn promote the general quality of the discourse. Further journalistic value can be gleaned by identifying and developing new sources of information and expertise from comments. In this talk I will present an editorially-aware visual analytics system called CommentIQ that supports moderators in curating high quality news comments at scale. The possibilities and ramifications of algorithmically infused social media moderation will be discussed in terms of journalistic ideals and norms of free speech and inclusion.
Aspect-Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews
Authors: Hui Du, Xueke Xu, Xueqi Cheng, Dayong Wu, Yue Liu, Zhihua Yu
Venue: WWW
Year: 2016

Recently, Deep Convolutional Neural Networks (CNNs) have been widely applied to sentiment analysis of short texts. Naturally, word embedding techniques are used to learn continuous word representations for constructing sentence matrix as input to CNN. As for sentiment analysis of customer reviews, we argue that it is problematic to learn a single representation for a word while ignoring sentiment information and the discussed aspects. In this poster, we propose a novel word embedding model to learn sentimental word embedding given specific aspects by modeling both sentiment and syntactic context under the specific aspects. We apply our method as input to CNN for sentiment analysis in multiple domains. Experiments show that the CNN based on the proposed model can consistently achieve superior performance compared to CNN based on traditional word embedding method.
Competency Based Learning in the Web of Learning Data
Authors: Guillaume Durand, Nabil Belacel, Cyril Goutte
Venue: WWW
Year: 2016

In this paper, we present, discuss and summarize different research works we carried out toward the exploitation of the Web of data for learning and training purpose (Web of learning data). For several years now, we have conducted efforts to explore this main objective through two complementary directions. The first direction is the scalability and particularly the need to develop methods able to provide learners with adequate learning path in the world of big data. The second direction is related to the transition from Web data to Web of learning data and particularly the extraction of cognitive attributes from Web content. For this purpose, we proposed different text mining techniques as well as the development of competency framework engineering tools. Resulting evidence-based techniques allow us to properly evaluate and improve the relationships between learning materials, performance records and student competencies. Although some questions remain unanswered and challenging technology improvements are still required, promising results and developments are arising.
Travel the World: Analyzing and Predicting Booking Behavior using E-mail Travel Receipts
Authors: Nemanja Djuric, Mihajlo Grbovic, Vladan Radosavljevic, Jaikit Savla, Varun Bhagwan, Doug Sharp
Venue: WWW
Year: 2016

Tourism industry has grown tremendously in the previous several decades. Despite its global impact, there still remain a number of open questions related to better understanding of tourists and their habits. In this work we analyze the largest data set of travel receipts considered thus far, and focus on exploring and modeling booking behavior of online customers. We extract useful, actionable insights into the booking behavior, and tackle the task of predicting the booking time. The presented results can be directly used to improve booking experience of customers and optimize targeting campaigns of travel operators.
Recommending Sellers to Buyers in Virtual Marketplaces Leveraging Social Information
Authors: Lukas Eberhard, Christoph Trattner
Venue: WWW
Year: 2016

Social information such as stated interests or geographic check-ins in social networks has shown to be useful in many recommender tasks recently. Although many successful examples exist, not much attention has been put on exploring the extent to which social impact is useful for the task of recommending sellers to buyers in virtual marketplaces. To contribute to this sparse field of research we collected data of a marketplace and a social network in the virtual world of Second Life and introduced several social features and similarity metrics that we used as input for a user-based $k$-nearest neighbor collaborative filtering method. As our results reveal, most of the types of social information and features which we used are useful to tackle the problem we defined. Social information such as joined groups or stated interests are more useful, while others such as places users have been checking in, do not help much for recommending sellers to buyers. Furthermore, we find that some of the features significantly vary in their predictive power over time, while others show more stable behaviors. This research is relevant for researchers interested in recommender systems and online marketplace research as well as for engineers interested in feature engineering.
Investigations on Rating Computer Sciences Conferences: An Experiment with the Microsoft Academic Graph Dataset
Authors: Suhendry Effendy, Roland H. C. Yap
Venue: WWW
Year: 2016

The rating of Computer Science (CS) conferences are important as it influences how papers published at the conferences and may also be used to evaluate research. In this paper, we proposed a method, \rsit{}, based on a small given set of top conference ({\em pivots}) and a relatedness measure based this set as well as basic baseline methods using citation count and field rating. We experimented with a snapshot dataset from Microsoft Academic Graph together with conference data from Microsoft Academic Search. We evaluated the conference ratings from our methods with the CCF conference rating list. We showed that \rsit{} correlates well with CCF rating and correlates better than ratings from using a baseline ranking with citation count or field rating.
Distributed Estimation of Graph 4-Profiles
Authors: Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis
Venue: WWW
Year: 2016

We present a novel distributed algorithm for counting all four-node induced subgraphs in a big graph. These counts, called the 4-profile, describe a graph's connectivity properties and have found several uses ranging from bioinformatics to spam detection. We also study the more complicated problem of estimating the local 4-profiles centered at each vertex of the graph. The local 4-profile embeds every vertex in an 11-dimensional space that characterizes the local geometry of its neighborhood: vertices that connect different clusters will have different local 4-profiles compared to those that are only part of one dense cluster. Our algorithm is a local, distributed message-passing scheme on the graph and computes all the local 4-profiles in parallel. We rely on two novel theoretical contributions: we show that local 4-profiles can be calculated using compressed two-hop information and also establish novel concentration results that show that graphs can be substantially sparsified and still retain good approximation quality for the global 4-profile. We empirically evaluate our algorithm using a distributed GraphLab implementation that we scaled up to 640 cores. We show that our algorithm can compute global and local 4-profiles of graphs with millions of edges in a few minutes, significantly improving upon the previous state of the art.
Distributed Wikis and Social Networks: A Good Fit
Authors: Babak Esfandiari, Alan Davoust
Venue: WWW
Year: 2016

Social networks can play an important role in the process of decentralizing authority in distributed systems. We will focus on distributed wiki systems, and we show how, in the special case of a peer-to-peer wiki, there is a rational incentive for users to self-organize and form a meaningful social network. We discuss to that effect the basic metrics that can be derived from the topology of the social network to help assess the subjective quality of wiki entries. Demos and experimental results will illustrate and support our discussion. We finally speculate as to how these results may also translate to discussion forums or recommender systems.