OpenRaiser/Intern-Atlas huggingface dataset:
A Semantic Graph-Based Approach for Mining Common Topics from Multiple Asynchronous Text Streams
Authors: Long Chen, Joemon M. Jose, Haitao Yu, Fajie Yuan
Venue: WWW
Year: 2017

In the age of Web 2.0, a substantial amount of unstructured content are distributed through multiple text streams in an asynchronous fashion, which makes it increasingly difficult to glean and distill useful information. An effective way to explore the information in text streams is topic modelling, which can further facilitate other applications such as search, information browsing, and pattern mining. In this paper, we propose a semantic graph based topic modelling approach for structuring asynchronous text streams. Our model integrates topic mining and time synchronization, two core modules for addressing the problem, into a unified model. Specifically, for handling the lexical gap issues, we use global semantic graphs of each timestamp for capturing the hidden interaction among entities from all the text streams. For dealing with the sources asynchronism problem, local semantic graphs are employed to discover similar topics of different entities that can be potentially separated by time gaps. Our experiment on two real-world datasets shows that the proposed model significantly outperforms the existing ones.
When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features
Authors: Kuan-Ting Chen, Jiebo Luo
Venue: WWW
Year: 2017

With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the most essential activities in our society and studying consumer's shopping behavior is important for the industry as well as sociology and psychology. Indisputable, one of the most popular e-commerce categories is clothing business. There arises the needs for analysis of popular and attractive clothing features which could further boost many emerging applications, such as clothing recommendation and advertising. In this work, we design a novel system that consists of three major components: 1) exploring and organizing a large-scale clothing dataset from a online shopping website, 2) pruning and extracting images of best-selling products in clothing item data and user transaction history, and 3) utilizing a machine learning based approach to discovering fine-grained clothing attributes as the representative and discriminative characteristics of popular clothing style elements. Through the experiments over a large-scale online clothing shopping dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on clothing consumption trends and profitable clothing features.
Now You See It, Now You Don't! A Study of Content Modification Behavior in Facebook
Authors: Fuxiang Chen, Ee-Peng Lim
Venue: WWW
Year: 2017

Social media, as a major platform to disseminate information, has changed the way users and communities contribute content. In this paper, we aim to study content modifications on public Facebook pages operated by news media, community groups, and bloggers. We also study the possible reasons behind them, and their effects on user interaction. We conducted a detailed study of Content Censorship (CC) and Content Edit (CE) in Facebook using a detailed longitudinal dataset consisting of 57 public Facebook pages over 3 weeks covering 145,955 posts and 9,379,200 comments. We detected many CC and CE activities between 28% and 56% of these pages (in both Facebook Posts and Comments). Manual judgements on these post/comment removals and edits show that majority of the content censorship is related to negative reports on events and personal grouses, and content edit is mainly performed to improve content quality and correctness. Furthermore, recency effect is also observed as part of Facebook content modification behavior.
IExM: Information Extraction System for Movies
Authors: Peng-Yu Chen, Yi-Hui Lee, Yueh-Han Wu, Wei-Yun Ma
Venue: WWW
Year: 2017

In this demonstration, we present Information Extraction System for Movies(IExM), which helps extract relation instances from un- labeled movie articles. We have designed a new distant-supervised learning algorithm: Improved Pattern Ranking Algorithm(IPRA) to extract relation instances from unlabeled articles, which iteratively generates new patterns starting from a limited set of seed instances, and extracts new instances using high-ranking pattern in a precise and effective way. IPRA also has a special estimation for the newly generated patterns based on the quality estimation of the instances that generate the patterns and ranks patterns' quality based on var- ious factors.
People Opinion Topic Model: Opinion based User Clustering in Social Networks
Authors: Hongxu Chen, Hongzhi Yin, Xue Li, Meng Wang, Weitong Chen, Tong Chen
Venue: WWW
Year: 2017

Mining various hot discussed topics and corresponding opinions from different groups of people in social media (e.g., Twitter) is very useful. For example, a decision maker in a company wants to know how different groups of people (customers, staff, competitors, etc.) think about their services, facilities, and things happened around. In this paper, we are focusing on the problem of finding opinion variations based on different groups of people and introducing the concept of opinion based community detection. Further, we also introduce a generative graphic model, namely People Opinion Topic (POT) model, which detects social communities, associated hot discussed topics, and perform sentiment analysis simultaneously by modelling user's social connections, common interests, and opinions in a unified way. This paper is the first attempt to study community and opinion mining together. Compared with traditional social communities detection, the detected communities by POT model are more interpretable and meaningful. In addition, we further analyse how diverse opinions distributed and propagated among various social communities. Experiments on real twitter dataset indicate our model is effective.
DeepVGI: Deep Learning with Volunteered Geographic Information
Authors: Jiaoyan Chen, Alexander Zipf
Venue: WWW
Year: 2017

Recently, deep learning has been widely studied to recognize ground objects with satellite imageries. However, finding ground truths especially for developing and rural areas is quite hard and manually labeling a large set of training data is costly. In this work, we propose an ongoing research named DeepVGI which aims at deeply learning from satellite imageries with the supervision of Volunteered Geographic Information (VGI). VGI data from OpenStreetMap (OSM) and a crowdsourcing mobile application named MapSwipe which allows volunteers to label images with buildings or roads for humanitarian aids are utilized. Meanwhile, an active learning framework with deep neural networks is developed by incorporating both VGI data with more complete supervision knowledge. Our experiments show that DeepVGI can achieve high building detection performance for humanitarian mapping in rural African areas.
Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems
Authors: Cen Chen, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li, Minghui Qiu
Venue: WWW
Year: 2017

In this work, we propose a locally connected deep learning framework for recommender systems, which reduces the complexity of deep neural network (DNN) by two to three orders of magnitude. We further extend the framework using the idea of the recently proposed Wide&Deep model. Experiments on industrial-scale datasets show that our methods could achieve good results with much shorter runtime.
Predicting Intent Using Activity Logs: How Goal Specificity and Temporal Range Affect User Behavior
Authors: Justin Cheng, Caroline Lo, Jure Leskovec
Venue: WWW
Year: 2017

People have different intents in using online platforms. They may be trying to accomplish specific, short-term goals, or less well-defined, longer-term goals. While understanding user intent is fundamental to the design and personalization of online platforms, little is known about how intent varies across individuals, or how it relates to their behavior. Here, we develop a framework for understanding intent in terms of goal specificity and temporal range. Our methodology combines survey-based methodology with an observational analysis of user activity. Applying this framework to Pinterest, we surveyed nearly 6000 users to quantify their intent, and then studied their subsequent behavior on the web site. We find that goal specificity is bimodal - users tend to be either strongly goal-specific or goal-nonspecific. Goal-specific users search more and consume less content in greater detail than goal-nonspecific users: they spend more time using Pinterest, but are less likely to return in the near future. Users with short-term goals are also more focused and more likely to refer to past saved content than users with long-term goals, but less likely to save content for the future. Further, intent can vary by demographic, and with the topic of interest. Last, we show that user's intent and activity are intimately related by building a model that can predict a user's intent for using Pinterest after observing their activity for only two minutes. Altogether, this work shows how intent can be predicted from user behavior.
Ten Blue Links on Mars
Authors: Charles L. A. Clarke, Gordon V. Cormack, Jimmy Lin, Adam Roegiest
Venue: WWW
Year: 2017

This paper explores a simple question: How would we provide a high-quality search experience on Mars, where the fundamental physical limit is speed-of-light propagation delays on the order of tens of minutes? On Earth, users are accustomed to nearly instantaneous responses from web services. Is it possible to overcome orders-of-magnitude longer latency to provide a tolerable user experience on Mars? In this paper, we formulate the searching from Mars problem as a tradeoff between "effort" (waiting for responses from Earth) and "data transfer" (pre-fetching or caching data on Mars). The contribution of our work is articulating this design space and presenting two case studies that explore the effectiveness of baseline techniques, using publicly available data from the TREC Total Recall and Sessions Tracks. We intend for this research problem to be aspirational as well as inspirational---even if one is not convinced by the premise of Mars colonization, there are Earth-based scenarios such as searching from rural villages in India that share similar constraints, thus making the problem worthy of exploration and attention from researchers.
How Public Is My Private Life?: Privacy in Online Dating
Authors: Camille Cobb, Tadayoshi Kohno
Venue: WWW
Year: 2017

Online dating services let users expand their dating pool beyond their social network and specify important characteristics of potential partners. To assess compatibility, users share personal information -- e.g., identifying details or sensitive opinions about sexual preferences or worldviews -- in profiles or in one-on-one communication. Thus, participating in online dating poses inherent privacy risks. How people reason about these privacy risks in modern online dating ecosystems has not been extensively studied. We present the results of a survey we designed to examine privacy-related risks, practices, and expectations of people who use or have used online dating, then delve deeper using semi-structured interviews. We additionally analyzed 400 Tinder profiles to explore how these issues manifest in practice. Our results reveal tensions between privacy and competing user values and goals, and we demonstrate how these results can inform future designs.
Establishing Smart City Technical Standards and Guidance: A Way Forward
Authors: Stephen J. Cohen, William H. Money
Venue: WWW
Year: 2017

Solving technical problems with complex systems and integrating the many technologies employed in these multifaceted structures has been a recurring theme in Smart Cities research. This paper presents an analysis of the reason this problem has been so well explored but persists with no solution widely available. The problem is viewed as a combination of Smart City needs, governance, and increasingly technically difficult decisions. The paper describes the requirements that must be met to develop a framework that can address this seeming intractable and expanding integration concern, identifies the governance processes that can be used to address this problem, and to manage integration in Smart Cities, The solution proposed is a formalized accepted and managed technology regulated environment introduced by governance groups composed of city planners/managers, citizen, stake holders, and technology delivery organizations. The solution requirements dictate the establishment of a standard that would guide the development and usage of automated, autonomous components, integrating dynamically with software agents. All of this working to rapidly optimize shared resources through error handling processes executing largely at no cost except those of processing time, meeting safety guidelines, satisfying operational monitoring needs, and meeting post issue liability guidelines. This technical standard would obligate developers and vendors to meet safety standards and accept liability for malfeasance. As initiating steps, Smart City managers must come together and establish a basic understanding of the goals and regulations, and the methodologies for implementing them.
A Vision for Performing Social and Economic Data Analysis using Wikipedia's Edit History
Authors: Erik Dahm, Moritz Schubotz, Norman Meuschke, Bela Gipp
Venue: WWW
Year: 2017

In this vision paper, we suggest combining two lines of research to study the collective behavior of Wikipedia contributors. The first line of research analyzes Wikipedia's edit history to quantify the quality of individual contributions and the resulting reputation of the contributor. The second line of research surveys Wikipedia contributors to gain insights, e.g., on their personal and professional background, socioeconomic status, or motives to contribute toWikipedia. While both lines of research are valuable on their own, we argue that the combination of both approaches could yield insights that exceed the sum of the individual parts. Linking survey data to contributor reputation and content-based quality metrics could provide a large-scale, public domain data set to perform user modeling, i.e. deducing interest profiles of user groups. User profiles can, among other applications, help to improve recommender systems. The resulting dataset can also enable a better understanding and improved prediction of high quality Wikipedia content and successfulWikipedia contributors. Furthermore, the dataset can enable novel research approaches to investigate team composition and collective behavior as well as help to identify domain experts and young talents. We report on the status of implementing our large-scale, content-based analysis of the Wikipedia edit history using the big data processing framework Apache Flink. Additionally, we describe our plans to conduct a survey among Wikipedia contributors to enhance the content-based quality metrics.
Large Scale Density-friendly Graph Decomposition via Convex Programming
Authors: Maximilien Danisch, T.-H. Hubert Chan, Mauro Sozio
Venue: WWW
Year: 2017

Algorithms for finding dense regions in an input graph have proved to be effective tools in graph mining and data analysis. Recently, Tatti and Gionis [WWW 2015] presented a novel graph decomposition (known as the locally-dense decomposition) that is similar to the well-known k-core decomposition, with the additional property that its components are arranged in order of their densities. Such a decomposition provides a valuable tool in graph mining. Unfortunately, their algorithm for computing the exact decomposition is based on a maximum-flow algorithm which cannot scale to massive graphs, while the approximate decomposition defined by the same authors misses several interesting properties. This calls for scalable algorithms for computing such a decomposition. In our work, we devise an efficient algorithm which is able to compute exact locally-dense decompositions in real-world graphs containing up to billions of edges. Moreover, we provide a new definition of approximate locally-dense decomposition which retains most of the properties of an exact decomposition, for which we devise an algorithm that can scale to real-world graphs containing up to tens of billions of edges. Our algorithm is based on the classic Frank-Wolfe algorithm which is similar to gradient descent and can be efficiently implemented in most of the modern architectures dealing with massive graphs. We provide a rigorous study of our algorithms and their convergence rates. We conduct an extensive experimental evaluation on multi-core architectures showing that our algorithms converge much faster in practice than their worst-case analysis. Our algorithm is even more efficient for the more specialized problem of computing a densest subgraph.
Finding Rising Stars in Co-Author Networks via Weighted Mutual Influence
Authors: Ali Daud, Naif Radi Aljohani, Rabeeh Ayaz Abbasi, Zahid Rafique, Tehmina Amjad, Hussain Dawood, Khaled Hamed Alyoubi
Venue: WWW
Year: 2017

Finding rising stars is a challenging and interesting task which is being investigated recently in co-author networks. Rising stars are authors who have a low research profile in the start of their career but may become experts in the future. This paper introduces a new method Weighted Mutual Influence Rank (WMIRank) for finding rising stars. WMIRank exploits influence of co-authors' citations, order of appearance and publication venues. Comprehensive experiments are performed to analyze the performance of WMIRank in comparison to baseline methods, which have ignored weighted mutual influence. AMiner data for years 1995-2000 is used for experiments. List of top 30 authors as per proposed and baseline methods are compared for their average number of papers, average number of citations and achievements. Experimental results provide convincing evidence of the effectiveness of the investigated weighted mutual influence.
Analysing and Improving Embedded Markup of Learning Resources on the Web
Authors: Stefan Dietze, Davide Taibi, Ran Yu, Phil Barker, Mathieu d&apos, Aquin
Venue: WWW
Year: 2017

Web-scale reuse and interoperability of learning resources have been major concerns for the technology-enhanced learning community. While work in this area traditionally focused on learning resource metadata, provided through learning resource repositories, the recent emergence of structured entity markup on the Web through standards such as RDFa and Microdata and initiatives such as schema.org, has provided new forms of entity-centric knowledge, which is so far under-investigated and hardly exploited. The Learning Resource Metadata Initiative (LRMI) provides a vocabulary for annotating learning resources through schema.org terms. Although recent studies have shown markup adoption by approximately 30% of all Web pages, understanding of the scope, distribution and quality of learning resources markup is limited. We provide the first public corpus of LRMI extracted from a representative Web crawl together with an analysis of LRMI adoption on the Web, with the goal to inform data consumers as well as future vocabulary refinements through a thorough understanding of the use as well as misuse of LRMI vocabulary terms. While errors and schema misuse are frequent, we also discuss a set of simple heuristics which significantly improve the accuracy of markup, a prerequisite for reusing learning resource metadata sourced from markup.
TeamGen: An Interactive Team Formation System Based on Professional Social Network
Authors: Cheng Ding, Fan Xia, Gopakumar Gopalakrishnan, Weining Qian, Aoying Zhou
Venue: WWW
Year: 2017

We introduce TeamGen, an interactive team formation system, to form project teams interactively by leveraging professional social network information of potential members. Unlike earlier approaches that focused on creating flat teams, i.e., teams without communities and central authorities, we model teams as hierarchical structures to reflect the ubiquitous nature of teams in real commercial and open source projects. Correspondingly, our team formation algorithms emphasize local density of sub teams to assess communication costs of newly formed teams. During the demonstration, audience can (a) explore professional social network of potential members, (b) learn the effectiveness and efficiency of our proposed team formation algorithms by comparing them with existing ones, (c) inspect and understand the process of team formation, and (d) interactively refine a project team by revoking computed position assignments.
Horizon-Independent Optimal Pricing in Repeated Auctions with Truthful and Strategic Buyers
Authors: Alexey Drutsa
Venue: WWW
Year: 2017

We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a (truthful or strategic) buyer that holds a fixed valuation. We focus on a practical situation in which the seller does not know in advance the number of played rounds (the time horizon) and has thus to use a horizon-independent pricing. First, we consider straightforward modifications of previously best known algorithms and show that these horizon-independent modifications have worser or even linear regret bounds. Second, we provide a thorough theoretical analysis of some broad families of consistent algorithms and show that there does not exist a no-regret horizon-independent algorithm in those families. Finally, we introduce a novel deterministic pricing algorithm that, on the one hand, is independent of the time horizon T and, on the other hand, has an optimal strategic regret upper bound in O(log log T). This result closes the logarithmic gap between the previously best known upper and lower bounds on strategic regret.
Using the Delay in a Treatment Effect to Improve Sensitivity and Preserve Directionality of Engagement Metrics in A/B Experiments
Authors: Alexey Drutsa, Gleb Gusev, Pavel Serdyukov
Venue: WWW
Year: 2017

State-of-the-art user engagement metrics (such as session-per-user) are widely used by modern Internet companies to evaluate ongoing updates of their web services via A/B testing. These metrics are predictive of companies' long-term goals, but suffer from this property due to slow user learning of an evaluated treatment, which causes a delay in the treatment effect. That, in turn, causes low sensitivity of the metrics and requires to conduct A/B experiments with longer duration or larger set of users from a limited traffic. In this paper, we study how the delay property of user learning can be used to improve sensitivity of several popular metrics of user loyalty and activity. We consider both novel and previously known modifications of these metrics, including different methods of quantifying a trend in a metric's time series and delaying its calculation. These modifications are analyzed with respect to their sensitivity and directionality on a large set of A/B tests run on real users of Yandex. We discover that mostly loyalty metrics gain profit from the considered modifications. We find such modifications that both increase sensitivity of the source metric and are consistent with the sign of its average treatment effect as well.