OpenRaiser/Intern-Atlas huggingface dataset:
Visualizing Social Media Sentiment in Disaster Scenarios
Authors: Yafeng Lu, Xia Hu, Feng Wang, Shamanth Kumar, Huan Liu, Ross Maciejewski
Venue: WWW
Year: 2015

Recently, social media, such as Twitter, has been successfully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media during disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social media streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualization of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geographic visualization. In particular, we provide an entropy-based metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how visual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.
Ads Selection At Twitter
Authors: Yue Lu, Sandeep Pandey
Venue: WWW
Year: 2015

Online advertising is a multi-billion dollar industry and it also serves as the major revenue source for Twitter Inc. In this talk, we present the ads selection pipeline at Twitter, using Promoted Tweets in Home Timelines as an example. The pipeline starts from targeting, where we model Twitter users' attributes offline, e.g. user gender, age, interest etc, so that we can match them with advertisers' specified audience criteria. The second critical component is user engagement rate prediction, where we employ a large-scale online learning system to do real-time training and prediction with rich features. Lastly, we run a second price auction based on the predictions, advertisers' bids and some other optimization parameters. We will present a series of case studies drawn from recent experiments in the setting of the deployed system used at Twitter.
Leveraging Semantic Web technologies for more relevant E-tourism Behavioral Retargeting
Authors: Chun Lu, Milan Stankovic, Philippe Laublet
Venue: WWW
Year: 2015

The e-tourism is today an important field of the e-commerce. One specificity of this field is that consumers spend much time comparing many options on multiple websites before purchasing. It's easy for consumers to forget the viewed offers or websites. The Behavioral Retargeting (BR) is a widely used technique for online advertising. It leverages consumers' actions on advertisers' websites and displays relevant ads on publishers' websites. In this paper, we're interested in the relevance of the displayed ads in the e-tourism field. We present MERLOT 1, a Semantic-based travel destination recommender system that can be deployed to improve the relevance of BR in the e-tourism field. We conducted a preliminary experiment with the real data of a French travel agency. The results of 33 participants showed very promising results with regards to the baseline according to all used metrics. By this paper, we wish to provide a novel viewpoint to address the BR relevance problem, different from the dominating machine learning approaches.
Grading the Graders: Motivating Peer Graders in a MOOC
Authors: Yanxin Lu, Joe Warren, Christopher M. Jermaine, Swarat Chaudhuri, Scott Rixner
Venue: WWW
Year: 2015

In this paper, we detail our efforts at creating and running a controlled study designed to examine how students in a MOOC might be motivated to do a better job during peer grading. This study involves more than one thousand students of a popular MOOC. We ask two specific questions: (1) When a student knows that his or her own peer grading efforts are being examined by peers, does this knowledge alone tend to motivate the student to do a better job when grading assignments? And (2) when a student not only knows that his or her own peer grading efforts are being examined by peers, but he or she is also given a number of other peer grading efforts to evaluate (so the peer graders see how other peer graders evaluate assignments), do both of these together tend to motivate the student to do a better job when grading assignments? We find strong statistical evidence that ``grading the graders'' does in fact tend to increase the quality of peer grading.
When Resources Collide: Towards a Theory of Coincidence in Information Spaces
Authors: Markus Luczak-Rösch, Ramine Tinati, Nigel Shadbolt
Venue: WWW
Year: 2015

This paper is an attempt to lay out foundations for a general theory of coincidence in information spaces such as the World Wide Web, expanding on existing work on bursty structures in document streams and information cascades. We elaborate on the hypothesis that every resource that is published in an information space, enters a temporary interaction with another resource once a unique explicit or implicit reference between the two is found. This thought is motivated by Erwin Shroedingers notion of entanglement between quantum systems. We present a generic information cascade model that exploits only the temporal order of information sharing activities, combined with inherent properties of the shared information resources. The approach was applied to data from the world's largest online citizen science platform Zooniverse and we report about findings of this case study.
An Introduction to Entity Recommendation and Understanding
Authors: Hao Ma, Yan Ke
Venue: WWW
Year: 2015

Entities and the Knowledge about the entities have become indispensable building blocks in modern search engines. This tutorial aims to present the current state of research in the emerging semantic search and recommendation field by studying how to help users effectively explore the knowledge base as well as answer their information needs. Many live applications and systems will be demonstrated throughout this tutorial. After the completion of the tutorial, the audience will have an introduction and overview of what is the emerging topics of entity recommendation and understanding. The audience will learn and be able to understand some current research work as well as industry practices using computational intelligence techniques in entity recommendation and understanding. One of the major goals of this tutorial is to help audience identify a few research directions that could have big impact in the near future.
Measurement and Analysis of Mobile Web Cache Performance
Authors: Yun Ma, Xuanzhe Liu, Shuhui Zhang, Ruirui Xiang, Yunxin Liu, Tao Xie
Venue: WWW
Year: 2015

The Web browser is a killer app on mobile devices such as smartphones. However, the user experience of mobile Web browsing is undesirable because of the slow resource loading. To improve the performance of Web resource loading, caching has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the performance of mobile Web caching. For example, most of these studies mainly focus on client-side implementations but not server-side configurations, suffer from biased user behaviors, and fail to study "miscached" resources. To address these issues, in this paper, we present a proactive approach for a comprehensive measurement study on mobile Web cache performance. The key idea of our approach is to proactively crawl resources from hundreds of websites periodically with a fine-grained time interval. Thus, we are able to uncover the resource update history and cache configurations at the server side, and analyze the cache performance in various time granularities. Based on our collected data, we build a new cache analysis model and study the upper bound of how high percentage of resources could potentially be cached and how effective the caching works in practice. We report detailed analysis results of different websites and various types of Web resources, and identify the problems caused by unsatisfactory cache performance. In particular, we identify two major problems -- Redundant Transfer and Miscached Resource, which lead to unsatisfactory cache performance. We investigate three main root causes: Same Content, Heuristic Expiration, and Conservative Expiration Time, and discuss what mobile Web developers can do to mitigate those problems.
Topic and Sentiment Unification Maximum Entropy Model for Online Review Analysis
Authors: Changlin Ma, Meng Wang, Xue-wen Chen
Venue: WWW
Year: 2015

Opinion mining is an important research topic in data mining. Many current methods are coarse-grained, which are practically problemic due to insufficient feedback information and limited reference values. To address these problems, a novel topic and sentiment unification maximum entropy LDA model is proposed in this paper for fine-grained opinion mining of online reviews. In this model, a maximum entropy component is first added to the traditional LDA model to distinguish background words, aspect words and opinion words and further realize both the local and global extraction of these words. A sentiment layer is then inserted between a topic layer and a word layer to extend the proposed model to four layers. Sentiment polarity analysis is done based on the extraction of aspect words and opinion words to simultaneously acquire the sentiment polarity of the whole review and each topic, which leads to, fine-grained topic-sentiment abstract. Experimental results demonstrate the validity of the proposed model and theory.
Tree Kernel-based Protein-Protein Interaction Extraction Considering both Modal Verb Phrases and Appositive Dependency Features
Authors: Changlin Ma, Yong Zhang, Maoyuan Zhang
Venue: WWW
Year: 2015

Protein-protein interaction plays an important role in understanding biological processes. In order to resolve the parsing error resulted from modal verb phrases and the noise interference brought by appositive dependency, an improved tree kernel-based PPI extraction method is proposed in this paper. Both modal verbs and appositive dependency features are considered to define some relevant processing rules which can effectively optimize and expand the shortest dependency path between two proteins in the new method. On the basis of these rules, the effective optimization and expanding path is used to direct the cutting of constituent parse tree, which makes the constituent parse tree for protein-protein interaction extraction more precise and concise. The experimental results show that the new method achieves better results on five commonly used corpora.
Web Intelligence and Communities
Authors: Pierre Maret, Rajendra Akerkar, Laurent Vercouter
Venue: WWW
Year: 2015

The World Wide Web (WWW) provides precious means for communication, which goes far beyond the traditional communication media. Web-based communities have become imperative spaces for individuals to seek and share expertise. Networks in these communities usually differ in their topology from other networks such as the World Wide Web. In this paper, we explore some research issues of web intelligence and communities. We will also introduce the WI&C'15 workshop's goal and structure.
SCULPT: A Schema Language for Tabular Data on the Web
Authors: Wim Martens, Frank Neven, Stijn Vansummeren
Venue: WWW
Year: 2015

Inspired by the recent working effort towards a recommendation by the World Wide Web Consortium (W3C) for tabular data and metadata on the Web, we present in this paper a concept for a schema language for tabular web data called SCULPT. The language consists of rules constraining and defining the structure of regions in the table. These regions are defined through the novel formalism of region selection expressions. We present a formal model for SCULPT and obtain a linear time combined complexity evaluation algorithm. In addition, we consider weak and strong streaming evaluation for SCULPT and present a SCULPT fragment for each of these streaming variants. Finally, we discuss several extensions of SCULPT including alternative semantics, types, complex content, and explore region selection expressions as a basis for a transformation language.
CubeViz: Exploration and Visualization of Statistical Linked Data
Authors: Michael Martin, Konrad Abicht, Claus Stadler, Axel-Cyrille Ngonga Ngomo, Tommaso Soru, Sören Auer
Venue: WWW
Year: 2015

CubeViz is a flexible exploration and visualization platform for statistical data represented adhering to the RDF Data Cube vocabulary. If statistical data is provided adhering to the Data Cube vocabulary, CubeViz exhibits a faceted browsing widget allowing to interactively filter observations to be visualized in charts. Based on the selected structural part, CubeViz offers suitable chart types and options for configuring the visualization by users. In this demo we present the CubeViz visualization architecture and components, sketch its underlying API and the libraries used to generate the desired output. By employing advanced introspection, analysis and visualization bootstrapping techniques CubeViz hides the schema complexity of the encoded data in order to support a user-friendly exploration experience.
Spanning Edge Centrality: Large-scale Computation and Applications
Authors: Charalampos Mavroforakis, Richard Garcia-Lebron, Ioannis Koutis, Evimaria Terzi
Venue: WWW
Year: 2015

The spanning centrality of an edge e in an undirected graph G is the fraction of the spanning trees of G that contain e. Despite its appealing definition and apparent value in certain applications in computational biology, spanning centrality hasn't so far received a wider attention as a measure of edge centrality. We may partially attribute this to the perceived complexity of computing it, which appears to be prohibitive for very large networks. Contrary to this intuition, spanning centrality can in fact be approximated arbitrary well by very efficient near-linear time algorithms due to Spielman and Srivastava, combined with progress in linear system solvers. In this article we bring theory into practice, with careful and optimized implementations that allow the fast computation of spanning centrality in very large graphs with millions of nodes. With this computational tool in our disposition, we demonstrate experimentally that spanning centrality is in fact a useful tool for the analysis of large networks. Specifically, we show that, relative to common centrality measures, spanning centrality is more effective in identifying edges whose removal causes a higher disruption in an information propagation procedure, while being very resilient to noise, in terms of both the edges scores and the resulting edge ranking.
Visualization of Trustworthiness Graphs
Authors: Stephen Mayhew, Dan Roth
Venue: WWW
Year: 2015

Trustworthiness is a field of research that seeks to estimate the credibility of information by using knowledge of the source of the information. The most interesting form of this problem is when different pieces of information share sources, and when there is conflicting information from different sources. This model can be naturally represented as a bipartite graph. In order to understand this data well, it is important to have several methods of exploring it. A good visualization can help to understand the problem in a way that no simple statistics can. This paper defines several desiderata for a "good" visualization and presents three different visualization methods for trustworthiness graphs.
No Escape From Reality: Security and Privacy of Augmented Reality Browsers
Authors: Richard McPherson, Suman Jana, Vitaly Shmatikov
Venue: WWW
Year: 2015

Augmented reality (AR) browsers are an emerging category of mobile applications that add interactive virtual objects to the user's view of the physical world. This paper gives the first system-level evaluation of their security and privacy properties.
Reconnecting Digital Publications to the Web using their Spatial Information
Authors: Ben De Meester, Tom De Nies, Ruben Verborgh, Erik Mannens, Rik Van de Walle
Venue: WWW
Year: 2015

Digital publications can be packaged and viewed via the Open Web Platform using the EPUB 3 format. Meanwhile, the increased amount of mobile clients and the advent of HTML5's Geolocation have opened a whole range of possibilities for digital publications to interact with their readers. However, EPUB 3 files often remain closed silos of information, no longer linked with the rest of the Web. In this paper, we propose a solution to reconnect digital publication with the (Semantic) Web. We will also show how we can use that connection to improve contextualization for a user, specifically via spatial information. We enrich digital publications by connecting the detected concepts to their URIs on, e.g., DBpedia, and by devising an algorithm to approximate the location of any detected concept, we can provide a user with the spatial center of gravity of his reading position. The evaluation of the location approximation algorithm showed a high recall, and the high correlation between estimation error and standard deviation can provide the user with a sense of correctness (or spread) of an approximation. This means relevant locations (and their possible radius) can be shown for a user, based on the content he or she is reading, and based on his or her location. This methodology can be used to reconnect digital publications with the online world, to entice readers, and ultimately, as a novel location-based recommendation technique.
Modeling the Evolution of User-generated Content on a Large Video Sharing Platform
Authors: Rishabh Mehrotra, Prasanta Bhattacharya
Venue: WWW
Year: 2015

Video sharing and entertainment websites have rapidly grown in popularity and now constitute some of the most visited websites on the Internet. Despite the high usage and user engagement, most of recent research on online media platforms have restricted themselves to networking based social media sites like Facebook or Twitter. The current study is among the first to perform a large-scale empirical study using longitudinal video upload data from one of the largest online video sites. Unlike previous studies in the online media space that have focused exclusively on demand-side research questions, we model the supply-side of the crowd contributed video ecosystem on this platform. The modeling and subsequent prediction of video uploads is made complicated by the heterogeneity of video types (e.g. popular vs. niche video genres), and the inherent time trend effects. We identify distinct genre-clusters from our dataset and employ a self-exciting Hawkes point-process model on each of these clusters to fully specify and estimate the video upload process. Our findings show that using a relatively parsimonious point-process model, we are able to achieve higher model fit, and predict video uploads to the platform with a higher accuracy than competing models.
Towards Hierarchies of Search Tasks & Subtasks
Authors: Rishabh Mehrotra, Emine Yilmaz
Venue: WWW
Year: 2015

Current search systems do not provide adequate support for users tackling complex tasks due to which the cognitive burden of keeping track of such tasks is placed on the searcher. As opposed to recent approaches to search task extraction, a more naturalistic viewpoint would involve viewing query logs as hierarchies of tasks with each search task being decomposed into more focussed sub-tasks. In this work, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks & subtasks. The proposed approach makes use of the multi-relational aspect of query associations which are important in identifying query-task associations. We describe a greedy agglomerative model selection algorithm based on the Gamma-Poisson conjugate mixture that take just one pass through the data to learn a fully probabilistic, hierarchical model of trees that is capable of learning trees with arbitrary branching structures as opposed to the more common binary structured trees. We evaluate our method based on real world query log data based on query term prediction. To the best of our knowledge, this work is the first to consider hierarchies of search tasks and subtasks.
On Topology of Baidu's Association Graph Based on General Recommendation Engine and Users' Behavior
Authors: Cong Men, Wanwan Tang, Po Zhang, Junqi Hou
Venue: WWW
Year: 2015

To better meet users' underlying navigational requirement, search engines like Baidu has developed general recommendation engine and provided related entities on the right side of the search engine results page(SERP). However, users' behavior have not been well investigated after the association of individual queries in search engine. To better understand users' navigational activities, we propose a new method to map users' behavior to an association graph and make graph analysis. Interesting properties like clustering and assortativity are found in this association graph. This study provides a new perspective on research of semantic network and users' navigational behavior on SERP.
Discovering Meta-Paths in Large Heterogeneous Information Networks
Authors: Changping Meng, Reynold Cheng, Silviu Maniu, Pierre Senellart, Wangda Zhang
Venue: WWW
Year: 2015

The Heterogeneous Information Network (HIN) is a graph data model in which nodes and edges are annotated with class and relationship labels. Large and complex datasets, such as Yago or DBLP, can be modeled as HINs. Recent work has studied how to make use of these rich information sources. In particular, meta-paths, which represent sequences of node classes and edge types between two nodes in a HIN, have been proposed for such tasks as information retrieval, decision making, and product recommendation. Current methods assume meta-paths are found by domain experts. However, in a large and complex HIN, retrieving meta-paths manually can be tedious and difficult. We thus study how to discover meta-paths automatically. Specifically, users are asked to provide example pairs of nodes that exhibit high proximity. We then investigate how to generate meta-paths that can best explain the relationship between these node pairs. Since this problem is computationally intractable, we propose a greedy algorithm to select the most relevant meta-paths. We also present a data structure to enable efficient execution of this algorithm. We further incorporate hierarchical relationships among node classes in our solutions. Extensive experiments on real-world HIN show that our approach captures important meta-paths in an efficient and scalable manner.
Locally Adaptive Density Ratio for Detecting Novelty in Twitter Streams
Authors: Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel
Venue: WWW
Year: 2015

With the massive growth of social data, a huge attention has been given to the task of detecting key topics in the Twitter stream. In this paper, we propose the use of novelty detection techniques for identifying both emerging and evolving topics in new tweets. In specific, we propose a locally adaptive approach for density-ratio estimation in which the density ratio between new and reference data is used to capture evolving novelties, and at the same time a locally adaptive kernel is employed into the density-ratio objective function to capture emerging novelties based on the local neighborhood structure. In order to address the challenges associated with short text, we adopt an efficient approach for calculating semantic kernels with the proposed density-ratio method. A comparison to different methods shows the superiority of the proposed algorithm.