OpenRaiser/Intern-Atlas huggingface dataset:
Propagation phenomena in large social networks
Authors: Meeyoung Cha
Venue: WWW
Year: 2014

Social media and blogging services have become extremely popular. Every day hundreds of millions of users share conversations on random thoughts, emotional expressions, political news, and social issues. Users interact by following each other's updates and passing along interesting pieces of information to their friends. Information therefore can diffuse widely and quickly through social links. Information propagation in networks like Twitter is unique in that traditional media sources and word-of-mouth propagation coexist. The availability of digitally-logged propagation events in social media help us better understand how user influence, tie strength, repeated exposures, conventions, and various other factors come into play in the way people generate and consume information in the modern society. In this talk, I will present several findings on how bad news [9], rumors [8], prominent events [11], conventions [6, 7], tags [1, 4], behaviors [12], and moods [10] propagate in social media based on a large amount of data collected from networks like Twitter, Flickr, Facebook, and Blogosphere. I will talk about the different roles of user types [2] and content types [5] in propagations as well as ways to measure their influence [3]. Among various findings, I will demonstrate that indegree of a user, a well-known measure of popularity, alone can reveal little about the influence.
Recommendation for advertising messages on mobile devices
Authors: Chih-Chun Chan, Yu-Chieh Lin, Ming-Syan Chen
Venue: WWW
Year: 2014

Mobile devices, especially smart phones, have been popular in recent years. With users spending much time on mobile devices, service providers deliver advertising messages to mobile device users and look forward to increasing their revenue. However, delivery of proper advertising messages is challenging since strategies of advertising in TV, SMS, or website may not be applied to the banner-based advertising on mobile devices. In this work, we study how to properly recommend advertising messages for mobile device users. We propose a novel approach which simultaneously considers several important factors: user profile, apps used, and clicking history. We apply experiments on real-world mobile log data, and the results demonstrate the effectiveness of the proposed approach.
Joint question clustering and relevance prediction for open domain non-factoid question answering
Authors: Snigdha Chaturvedi, Vittorio Castelli, Radu Florian, Ramesh Nallapati, Hema Raghavan
Venue: WWW
Year: 2014

Web searches are increasingly formulated as natural language questions, rather than keyword queries. Retrieving answers to such questions requires a degree of understanding of user expectations. An important step in this direction is to automatically infer the type of answer implied by the question, e.g., factoids, statements on a topic, instructions, reviews, etc. Answer Type taxonomies currently exist for factoid-style questions, but not for open-domain questions. Building taxonomies for non-factoid questions is a harder problem since these questions can come from a very broad semantic space. A few attempts have been made to develop taxonomies for non-factoid questions, but these tend to be too narrow or domain specific. In this paper, we address this problem by modeling the Answer Type as a latent variable that is learned in a data-driven fashion, allowing the model to be more adaptive to new domains and data sets. We propose approaches that detect the relevance of candidate answers to a user question by jointly 'clustering' questions according to the hidden variable, and modeling relevance conditioned on this hidden variable.
Big smog meets web science: smog disaster analysis based on social media and device data on the web
Authors: Jiaoyan Chen, Huajun Chen, Guozhou Zheng, Jeff Z. Pan, Honghan Wu, Ningyu Zhang
Venue: WWW
Year: 2014

Nowadays, people are increasingly concerned about smog disaster and the caused health hazard. However, the current methods for big smog analysis are usually based on the traditional lagging data sources or merely adopt physical environment observations, which limit the methods' accuracy and usability. The discipline of Web Science, the research fields of which include web of people and web of devices, provides real time web data as well as novel web data analysis approaches. In this paper, both social web data and device web data are proposed for smog disaster analysis. Firstly, we utilize social web data to define and calculate Individual Public Health Indexes (IPHIs) for smog caused health hazard quantification. Secondly, we integrate social web data and device web data to build standard health hazard rating reference and train smog-health models for health hazard prediction. Finally, we apply the rating reference and models to online and location-sensitive smog disaster monitoring, which can better guide people's behaviour and government's strategy design for disaster mitigation.
Iterative algorithm for inferring entity types from enumerative descriptions
Authors: Qian Chen, Mizuho Iwaihara
Venue: WWW
Year: 2014

Entity type matching has many real world applications, especially in entity clustering, de-duplication and efficient query processing. Current methods to extract entities from text usually disregard regularities in the order of entities appearing in the text. In this paper, we focus on enumerative descriptions which enlist entity names in a certain hidden order, often occurring in web documents as listings and tables. We propose an algorithm to discover entity types from enumerative descriptions, where a type hierarchy is known but enumerating orders are hidden and heterogeneous, and partial entity-type mappings are given as seed instances. Our algorithm is iterative: We extract skeletons from syntactic patterns, then train a hidden Markov model to find an optimum enumerating order from seed instances and skeletons, to find a best-fit entity-type assignment.
A pruning algorithm for optimal diversified search
Authors: Fei Chen, Yiqun Liu, Jian Li, Min Zhang, Shaoping Ma
Venue: WWW
Year: 2014

Given a number of possible sub-intents (also called subtopics) for a certain query and their corresponding search results, diversified search aims to return a single result list that could satisfy as many users' intents as possible. Previous studies have demonstrated that finding the optimal solution for diversified search is NP-hard. Therefore, several algorithms have been proposed to obtain a local optimal ranking with greedy approximations. In this paper, a pruned exhaustive search algorithm is proposed to decrease the complexity of the optimal search for the diversified search problem. Experimental results indicate that the proposed algorithm can decrease the computation complexity of exhaustive search without any performance loss.
Sentiment-enhanced explanation of product recommendations
Authors: Li Chen, Feng Wang
Venue: WWW
Year: 2014

Because of the important role of product reviews during users' decision process, we propose a novel explanation interface that particularly fuses the feature sentiments as extracted from reviews into explaining recommendations. Besides, it can explain multiple items altogether by revealing their similarity in respect of feature sentiments as well as static specifications, so as to support users' tradeoff making. Relative to existing works, we believe that this interface can be more effective, trustworthy, and persuasive.
Finding local experts on twitter
Authors: Zhiyuan Cheng, James Caverlee, Himanshu Barthwal, Vandana Bachani
Venue: WWW
Year: 2014

We address the problem of identifying local experts on Twitter. Specifically, we propose a local expertise framework that integrates both users' topical expertise and their local authority by leveraging over 15 million geo-tagged Twitter lists. We evaluate the proposed approach across 16 queries coupled with over 2,000 individual judgments from Amazon Mechanical Turk. Our initial experiments find significant improvement over a naive local expert finding approach, suggesting the promise of exploiting geo-tagged Twitter lists for local expert finding.
Entity-centric summarization: generating text summaries for graph snippets
Authors: Shruti Chhabra
Venue: WWW
Year: 2014

In recent times, focus of information retrieval community has shifted from traditional keyword-based retrieval to techniques utilizing the semantics in the text. Since such techniques require the understanding of relationships between entities, efforts are ongoing to organize the Web into large entity-relationship graphs. These graphs can be leveraged to answer complex relationship queries. However, most of the research has focused upon extracting structural information between entities such as a path, Steiner tree, or subgraphs. Little attention has been paid to the comprehension of these structural results, which is necessary for the user to understand relationships encapsulated in these structures. In this doctoral proposal, we pursue the idea of entity-centric summarization and propose a novel framework to produce entity-centric summaries which describe the relationships among input entities. We discuss the inherent challenges associated with each module in the framework and present an evaluation plan. Results from our preliminary experiments are encouraging and substantiate the feasibility of summarization problem.
REST to JavaScript for better client-side development
Authors: Hyunghun Cho, Sukyoung Ryu
Venue: WWW
Year: 2014

In today's Web-centric era, embedded systems become mashup various web services via RESTful web services. RESTful web services use REST APIs that describe actions as resource state transfers via standard HTTP methods such as GET, PUT, POST, and DELETE. While RESTful web services are lightweight and executable on any platforms that support HTTP methods, writing programs composed of only such primitive methods is not a familiar concept to developers. Therefore, no single design strategy for (fully) RESTful APIs works for arbitrary domains, and current REST APIs are system dependent, incomplete, and likely to change. To help sever-side development of REST APIs, several domain-specific languages such as WADL, WSDL 2.0, and RDF provide automatic tools to generate REST APIs. However, client-side developers who often do not know the web services domain and do not understand RESTful web services suffer from the lack of any development help. In this paper, we present a new approach to build JavaScript APIs that are more accessible to client-side developers than REST APIs. We show a case study of our approach that uses JavaScript APIs and their wrapper implementation instead of REST APIs, and we describe the efficiency in the client-side development.
Taming the web
Authors: Jong-Deok Choi
Venue: WWW
Year: 2014

The World Wide Web (WWW) has become an indispensable part of the modern life, providing many benefits in diverse ways. For instance, the huge amount of information from the web offers people unprecedented levels of opportunities for education, entertainments, social activities, productivity improvements, and business. The web, however, has also become perilous with many dangers, such as privacy violation and security breaches, and therein exist many villains who would like to turn into victims scrupulous as well as casual users. In this regard, WWW has become almost like Wild Wild West, where wonderful opportunities and great perils co-existed. Tizen (www.tizen.org) is a web-centric open-source, standards-based software platform for smart devices, such as smartphones, smart TVs, IVI (In-Vehicle Infotainment) and other consumer devices like cameras, printers, and more. Tizen is web-centric in that it directly supports web apps - applications (apps) written in HTML5 and Javascript - even outside the web-browsers and provides seamless supports for the web. As such, Tizen not only shares the benefits and perils of the web with other platforms, but also has the additional burden to meet the performance of non-web platforms: platforms that directly support only conventional programming languages. In this talk, we present Tizen's approaches to taming the web to maximize its benefits while minimizing the risks of its perils. We also describe various optimizations of Tizen that enable delivering web-app performance on par with that of non-web platforms.
Designing a high-performance mobile cloud web browser
Authors: Ryan H. Choi, Youngil Choi
Venue: WWW
Year: 2014

A mobile cloud web browser is a web browser that enables mobile devices with constrained resources to support complex web pages by performing most of resource demanding operations on a cloud web server. In this paper, we present a design of a mobile web cloud browser with efficient data structure.
To gather together for a better world: understanding and leveraging communities in micro-lending recommendation
Authors: Jaegul Choo, Daniel Lee, Bistra Dilkina, Hongyuan Zha, Haesun Park
Venue: WWW
Year: 2014

Micro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. In Kiva.org, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding micro-financial transactions available at Kiva. Based on this approach, we achieved a competitive performance in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan's geo-location, a borrower's gender, a field partner's reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders' background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results.
Can social media help us reason about mental health?
Authors: Munmun De Choudhury
Venue: WWW
Year: 2014

Millions of people each year suffer from depression, which makes mental illness one of the most serious and widespread health challenges in our society today. There is therefore a need for effective policies, interventions, and prevention strategies that enable early detection and diagnosis of mental health concerns in populations. This talk reports some findings on the potential of leveraging social media postings as a new type of lens in understanding mental illness in individuals and populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements of behavior over time while radically expanding population sample sizes. The talk highlights how this research direction may be useful in developing tools for identifying the onset of depressive disorders, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from mental illness to be more proactive about their mental health.
People like us: mining scholarly data for comparable researchers
Authors: Graham Cormode, S. Muthukrishnan, Jinyun Yan
Venue: WWW
Year: 2014

There are many situations that one needs to find comparable others for a given researcher. Examples include finding peer reviewers, programming committees for conferences, and comparable individual asked in recommendation letters for tenure evaluation. The task is often done on an ad hoc and informal basis. In this paper, we address an interesting problem that has not been adequately studied so far: mining cumulated large scale scholarly data to find comparable researchers. We propose a standard to quantify the quality of individual's research output, through the quality of publishing venues. We represent a researcher as a sequence of her publication records, and develop methods to compute the distance between two researchers through sequence matching. Multiple variations of distances are considered to target different scenarios. We define comparable relation by the distance, and conduct experiments on a large corpus and demonstrate the effectiveness our methods through examples. In the end of the paper, we identify several promising directions for further work.
Chaff from the wheat: characterization and modeling of deleted questions on stack overflow
Authors: Denzil Correa, Ashish Sureka
Venue: WWW
Year: 2014

Stack Overflow is the most popular Community based Question Answering (CQA) website for programmers on the web with 2.05M users, 5.1M questions and 9.4M answers. Stack Overflow has explicit, detailed guidelines on how to post questions and an ebullient moderation community. Despite these precise communications and safeguards, questions posted on Stack Overflow can be extremely off topic or very poor in quality. Such questions can be deleted from Stack Overflow at the discretion of experienced community members and moderators. We present the first study of deleted questions on Stack Overflow. We divide our study into two parts - (i) Characterization of deleted questions over ~5 years (2008-2013) of data, (ii) Prediction of deletion at the time of question creation. Our characterization study reveals multiple insights on question deletion phenomena. We find that it takes substantial time to vote a question to be deleted but once voted, the community takes swift action. We also see that question authors delete their questions to salvage reputation points. We notice some instances of accidental deletion of good quality questions but such questions are voted back to be undeleted quickly. We discover a pyramidal structure of question quality on Stack Overflow and find that deleted questions lie at the bottom (lowest quality) of the pyramid. We also build a predictive model to detect the deletion of question at the creation time. We experiment with 47 features -- based on User Profile, Community Generated, Question Content and Syntactic style -- and report an accuracy of 66%. Our findings reveal important suggestions for content quality maintenance on community based question answering websites. To the best of our knowledge, this is the first large scale study on poor quality (deleted) questions on Stack Overflow.
Adaptive presentation of linked data on mobile
Authors: Luca Costabello, Fabien Gandon
Venue: WWW
Year: 2014

We present PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations.
Recommending without short head
Authors: Paolo Cremonesi, Franca Garzotto, Roberto Pagano, Massimo Quadrana
Venue: WWW
Year: 2014

We discuss a comprehensive study exploring the impact of recommender systems when recommendations are forced to omit popular items (short head) and to use niche products only (long tail). This is an interesting issue in domains, such as e-tourism, where product availability is constrained, "best sellers" most popular items are the first ones to be consumed, and the short head may eventually become unavailable for recommendation purposes. Our work provides evidence that the effects resulting from item consumption may increase the utility of personalized recommendations.
Random walks based modularity: application to semi-supervised learning
Authors: Robin Devooght, Amin Mantrach, Ilkka Kivimäki, Hugues Bersini, Alejandro Jaimes, Marco Saerens
Venue: WWW
Year: 2014

Although criticized for some of its limitations, modularity remains a standard measure for analyzing social networks. Quantifying the statistical surprise in the arrangement of the edges of the network has led to simple and powerful algorithms. However, relying solely on the distribution of edges instead of more complex structures such as paths limits the extent of modularity. Indeed, recent studies have shown restrictions of optimizing modularity, for instance its resolution limit. We introduce here a novel, formal and well-defined modularity measure based on random walks. We show how this modularity can be computed from paths induced by the graph instead of the traditionally used edges. We argue that by computing modularity on paths instead of edges, more informative features can be extracted from the network. We verify this hypothesis on a semi-supervised classification procedure of the nodes in the network, where we show that, under the same settings, the features of the random walk modularity help to classify better than the features of the usual modularity. Additionally, the proposed approach outperforms the classical label propagation procedure on two data sets of labeled social networks.
The web observatory extension: facilitating web science collaboration through semantic markup
Authors: Dominic DiFranzo, John S. Erickson, Marie Joan Kristine Gloria, Joanne S. Luciano, Deborah L. McGuinness, James A. Hendler
Venue: WWW
Year: 2014

The multi-disciplinary nature of Web Science and the large size and diversity of data collected and studied by its practitioners has inspired a new type of Web resource known as the Web Observatory. Web observatories are platforms that enable researchers to collect, analyze and share data about the Web and to share tools for Web research. At the Boston Web Observatory Workshop 2013, a semantic model for describing Web Observatories was drafted and an extension to the schema.org microdata vocabulary collection was proposed. This paper details our implementation of the proposed extension, and how we have applied it to the Web Observatory Portal created by the Tetherless World Constellation at Rensselaer Polytechnic Institute (TWC RPI). We recognize this effort to be the "first-step" in the construction, evaluation and validation of the Web observatory model and not the final recommendation. Our hope is that this extension recommendation and our initial implementation sparks additional discussion among the Web Science community of on whether such direction enables Web Observatory curators to better expose and explain their individual Web Observatories to others, thereby enabling better collaboration between researchers across the Web Science community.
RDF mapping rules refinements according to data consumers' feedback
Authors: Anastasia Dimou, Miel Vander Sande, Tom De Nies, Erik Mannens, Rik Van de Walle
Venue: WWW
Year: 2014

The missing feedback loop is considered the reason for broken Data Cycles on current Linked Open Data ecosystems. Read-Write platforms are proposed, but they are restricted to capture modifications after the data is released as Linked Data. Triggering though a new iteration results in loosing the data consumers' modifications, as a new version of the source data is mapped, overwriting the currently published. We propose a prime solution that interprets the data consumers' feedback to update the mapping rules. This way, data publishers initiate a new iteration of the Data Cycle considering the data consumers' feedback when they map a new version of the published data.