OpenRaiser/Intern-Atlas huggingface dataset:
Web as Corpus Supporting Natural Language Generation for Online River Information Communication
Authors: Xiwu Han, Antonio A. R. Ioris, Chenghua Lin
Venue: WWW
Year: 2015

Web as corpus for NLP has been popular, and we now employed web as corpus for NLG, and made the online communication of tailored river information more effective and efficient. Evaluation and analysis shows that our generated texts were comparable to those written by domain experts and experienced users.
Towards Serving "Delicious" Information within Its Freshness Date
Authors: Hao Han, Takashi Nakayama, Junxia Guo, Keizo Oyama
Venue: WWW
Year: 2015

Like freshness date of food, Web information also has its "shelf life". In this paper, we exploratively study the reflection of shelf life of information in browsing behaviors. Our analysis shows that the satisfaction of browsing behavior is modified if the shelf life of information could be considered by search engines.
FluTCHA: Using Fluency to Distinguish Humans from Computers
Authors: Kotaro Hara, Mohammad Taghi Hajiaghayi, Benjamin B. Bederson
Venue: WWW
Year: 2015

Improvements in image understanding technologies are making it possible for computers to pass traditional CAPTCHA tests with high probability. This suggests the need for new kinds of tasks that are easy to accomplish for humans but remain difficult for computers. In this paper, we introduce Fluency CAPTCHA (FluTCHA), a novel method to distinguish humans from computers using the fact that humans are better than machines at improving the fluency of sentences. We propose a way to let users work on FluTCHA tests and simultaneously complete useful linguistic tasks. Evaluation demonstrates the feasibility of using FluTCHA to distinguish humans from computers.
Disentangling the Lexicons of Disaster Response in Twitter
Authors: Nathan O. Hodas, Greg Ver Steeg, Joshua J. Harrison, Satish Chikkagoudar, Eric Bell, Courtney D. Corley
Venue: WWW
Year: 2015

People around the world use social media platforms such as Twitter to express their opinion and share activities about various aspects of daily life. In the same way social media changes communication in daily life, it also is transforming the way individuals communicate during disasters and emergencies. Because emergency officials have come to rely on social media to communicate alerts and updates, they must learn how users communicate disaster related content on social media. We used a novel information-theoretic unsupervised learning tool, CorEx, to extract and characterize highly relevant content used by the public on Twitter during known emergencies, such as fires, explosions, and hurricanes. Using the resulting analysis, authorities may be able to score social media content and prioritize their attention toward those messages most likely to be related to the disaster.
Targeted Content for a Real-Time Activity Feed: For First Time Visitors to Power Users
Authors: Diane Hu, Tristan Schneiter
Venue: WWW
Year: 2015

The Activity Feed is Etsy's take on the ubiquitous "web feed" - a continuous stream of aggregated content, personalized for each user. These streams have become the de facto means of serving advertisements in the context of social media. Any visitor to Facebook or Twitter has seen advertisements placed on their web feed. For Etsy, an online marketplace for handmade and vintage goods with over 29 million unique items, the AF makes the marketplace feel a bit smaller for users. It enables discovery of relevant content, including activities from their social graph, recommended shops and items, and new listings from favorite shops. At the same time, Etsy's AF provides a platform for presenting users with targeted content, as well as advertisements, served alongside relevant and timely content.
When Does Improved Targeting Increase Revenue?
Authors: Patrick Hummel, R. Preston McAfee
Venue: WWW
Year: 2015

In second price auctions with symmetric bidders, we find that improved targeting via enhanced information disclosure decreases revenue when there are two bidders and increases revenue if there are at least four bidders. With asymmetries, improved targeting increases revenue if the most frequent winner wins less than 30.4% of the time, but can decrease revenue otherwise. We derive analogous results for position auctions. Finally, we show that revenue can vary non-monotonically with the number of bidders who are able to take advantage of improved targeting.
Towards a Data-driven Approach to Identify Crisis-Related Topics in Social Media Streams
Authors: Muhammad Imran, Carlos Castillo
Venue: WWW
Year: 2015

While categorizing any type of user-generated content online is a challenging problem, categorizing social media messages during a crisis situation adds an additional layer of complexity, due to the volume and variability of information, and to the fact that these messages must be classified as soon as they arrive. Current approaches involve the use of automatic classification, human classification, or a mixture of both. In these types of approaches, there are several reasons to keep the number of information categories small and updated, which we examine in this article. This means at the onset of a crisis an expert must select a handful of information categories into which information will be categorized. The next step, as the crisis unfolds, is to dynamically change the initial set as new information is posted online. In this paper, we propose an effective way to dynamically extract emerging, potentially interesting, new categories from social media data.
Exploring Bibliographies for Research-related Tasks
Authors: Angelo Di Iorio, Raffaele Giannella, Francesco Poggi, Fabio Vitali
Venue: WWW
Year: 2015

Bibliographies are fundamental tools for research communities. Besides the obvious uses as connection to previous research, citations are also widely used for evaluation purposes: the productivity of researchers, departments and universities is increasingly measured by counting their citations. Unfortunately, citations counters are just rough indicators: a deeper knowledge of individual citations -- where, when, by whom and why -- improves research evaluation tasks and supports researchers in their daily activity. Yet, such information is mostly hidden within repositories of scholarly papers and is still difficult to find, navigate and make use of.
Path Sampling: A Fast and Provable Method for Estimating 4-Vertex Subgraph Counts
Authors: Madhav Jha, C. Seshadhri, Ali Pinar
Venue: WWW
Year: 2015

Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting results for 4-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes. Indeed, even a highly tuned enumeration code takes more than a day on a graph with millions of edges. Most previous work that runs for truly massive graphs employ clusters and massive parallelization. We provide a sampling algorithm that provably and accurately approximates the frequencies of all 4-vertex pattern subgraphs. Our algorithm is based on a novel technique of 3-path sampling and a special pruning scheme to decrease the variance in estimates. We provide theoretical proofs for the accuracy of our algorithm, and give formal bounds for the error and confidence of our estimates. We perform a detailed empirical study and show that our algorithm provides estimates within 1% relative error for all subpatterns (over a large class of test graphs), while being orders of magnitude faster than enumeration and other sampling based algorithms. Our algorithm takes less than a minute (on a single commodity machine) to process an Orkut social network with 300 million edges.
Online Event Recommendation for Event-based Social Networks
Authors: Xiancai Ji, Zhi Qiao, Mingze Xu, Peng Zhang, Chuan Zhou, Li Guo
Venue: WWW
Year: 2015

With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. While, the existing event recommendation approaches are batch learning fashion. Such approaches are impractical for real-world recommender systems where training data often arrive sequentially. Hence, we present an online event recommendation method. Experimental results on several real-world datasets demonstrate the utility of our method.
Measuring Gender Bias in News Images
Authors: Sen Jia, Thomas Lansdall-Welfare, Nello Cristianini
Venue: WWW
Year: 2015

Analysing the representation of gender in news media has a long history within the fields of journalism, media and communication. Typically this can be performed by measuring how often people of each gender are mentioned within the textual content of news articles. In this paper, we adopt a different approach, classifying the faces in images of news articles into their respective gender. We present a study on $885{,}573$ news articles gathered from the web, covering a period of four months between 19th October 2014 and 19th January 2015 from $882$ news outlets. Findings show that gender bias differs by topic, with Fashion and the Arts showing the least bias. Comparisons of gender bias by outlet suggest that tabloid-style news outlets may be less gender-biased than broadsheet-style ones, supporting previous results from textual content analysis of news articles.
Querying Web-Scale Information Networks Through Bounding Matching Scores
Authors: Jiahui Jin, Samamon Khemmarat, Lixin Gao, Junzhou Luo
Venue: WWW
Year: 2015

Web-scale information networks containing billions of entities are common nowadays. Querying these networks can be modeled as a subgraph matching problem. Since information networks are incomplete and noisy in nature, it is important to discover answers that match exactly as well as answers that are similar to queries. Existing graph matching algorithms usually use graph indices to improve the efficiency of query processing. For web-scale information networks, it may not be feasible to build the graph indices due to the amount of work and the memory/storage required. In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices. The quality of an answer is measured by a matching score that is computed online. To speed up query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, we can efficiently prune the answers that have low qualities without having to evaluate all possible answers. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently answer the queries on web-scale information networks. We demonstrate the effectiveness and the efficiency of our approach through a series of experiments on real-world information networks. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach that does not use bounds.
LN-Annote: An Alternative Approach to Information Extraction from Emails using Locally-Customized Named-Entity Recognition
Authors: YoungHoon Jung, Karl Stratos, Luca P. Carloni
Venue: WWW
Year: 2015

Personal mobile devices offer a growing variety of personalized services that enrich considerably the user experience. This is made possible by increased access to personal information, which to a large extent is extracted from user email messages and archives. There are, however, two main issues. First, currently these services can be offered only by large web-service companies that can also deploy email services. Second, keeping a large amount of structured personal information on the cloud raises privacy concerns. To address these problems, we propose LN-Annote, a new method to extract personal information from the email that is locally available on mobile devices (without remote access to the cloud). LN-Annote enables third-party service providers to build a question-answering system on top of the local personal information without having to own the user data. In addition, LN-Annote mitigates the privacy concerns by keeping the structured personal information directly on the personal device. Our method is based on a named-entity recognizer trained in two separate steps: first using a common dataset on the cloud and then using a personal dataset in the mobile device at hand. Our contributions include also the optimization of the implementation of LN-Annote: in particular, we implemented an OpenCL version of the custom-training algorithm to leverage the Graphic Processing Unit (GPU) available on the mobile device. We present an extensive set of experiment results: beside proving the feasibility of our approach, they demonstrate its efficiency in terms of the named-entity extraction performance as well as the execution speed and the energy consumption spent in mobile devices.
Active Learning for Multi-relational Data Construction
Authors: Hiroshi Kajino, Akihiro Kishimoto, Adi Botea, Elizabeth M. Daly, Spyros Kotoulas
Venue: WWW
Year: 2015

Knowledge on the Web relies heavily on multi-relational representations, such as RDF and Schema.org. Automatically extracting knowledge from documents and linking existing databases are common approaches to construct multi-relational data. Complementary to such approaches, there is still a strong demand for manually encoding human expert knowledge. For example, human annotation is necessary for constructing a common-sense knowledge base, which stores facts implicitly shared in a community, because such knowledge rarely appears in documents. As human annotation is both tedious and costly, an important research challenge is how to best use limited human resources, whiles maximizing the quality of the resulting dataset. In this paper, we formalize the problem of dataset construction as active learning problems and present the Active Multi-relational Data Construction (AMDC) method. AMDC repeatedly interleaves multi-relational learning and expert input acquisition, allowing us to acquire helpful labels for data construction. Experiments on real datasets demonstrate that our solution increases the number of positive triples by a factor of 2.28 to 17.0, and that the predictive performance of the multi-relational model in AMDC achieves the highest or comparable to the best performance throughout the data construction process.
Learning to Detect Event-Related Queries for Web Search
Authors: Nattiya Kanhabua, Tu Ngoc Nguyen, Wolfgang Nejdl
Venue: WWW
Year: 2015

In many cases, a user turns to search engines to find information about real-world situations, namely, political elections, sport competitions, or natural disasters. Such temporal querying behavior can be observed through a significant number of event-related queries generated in web search. In this paper, we study the task of detecting event-related queries, which is the first step for understanding temporal query intent and enabling different temporal search applications, e.g., time-aware query auto-completion, temporal ranking, and result diversification. We propose a two-step approach to detecting events from query logs. We first identify a set of event candidates by considering both implicit and explicit temporal information needs. The next step further classifies the candidates into two main categories, namely, event or non-event. In more detail, we leverage different machine learning techniques for query classification, which are trained using the feature set composed of time series features from signal processing, along with features derived from click-through information, and standard statistical features. In order to evaluate our proposed approach, we conduct an experiment using two real-world query logs with manually annotated relevance assessments for 837 events. To this end, we provide a large set of event-related queries made available for fostering research on this challenging task.
Towards Optimising the Data Flow in Distributed Applications
Authors: Felix Leif Keppmann, Maria Maleshkova, Andreas Harth
Venue: WWW
Year: 2015

Networked applications continuously move towards service-based and modular solutions. At the same time, web technologies, proven to be modular and distributed, are applied to these application areas. However, web technologies have to be adapted to the new characteristics of the involved systems -- no explicit client and server roles, use of heterogeneous devices, or high frequency and low latency data communication. To this end, we present an approach for describing distributed applications in terms of graphs of communicating nodes. In particular, we develop a formal model for capturing the communication between nodes, by including dynamic and static data producing devices, data consuming client applications, as well as devices that can serve as data produces and consumers at the same time. In our model, we characterise nodes by their frequencies of data exchange. We complement our model with a decision algorithm for determining the pull/push communication direction to optimise the amount of redundantly transferred data (i.e., data that is pushed but cannot be processed or data that is pulled but is not yet updated). The presented work lays the foundation for creating distributed applications which can automatically optimise data exchange.
Self Curation, Social Partitioning, Escaping from Prejudice and Harassment: The Many Dimensions of Lying Online
Authors: Max Van Kleek, Daniel A. Smith, Nigel R. Shadbolt, Dave Murray-Rust, Amy Guy
Venue: WWW
Year: 2015

Portraying matters as other than they truly are is an important part of everyday human communication. In this paper, we use a survey to examine ways in which people fabricate, omit or alter the truth online. Many reasons are found, including creative expression, hiding sensitive information, role-playing, and avoiding harassment or discrimination. The results suggest lying is often used for benign purposes, and we conclude that its use may be essential to maintaining a humane online society.
Multi-Aspect Collaborative Filtering based on Linked Data for Personalized Recommendation
Authors: Han-Gyu Ko, Joo-Sik Son, In-Young Ko
Venue: WWW
Year: 2015

Since users often consider more than one aspect when they choose an item, relevant researches introduced multi-criteria recommender systems and showed that multi-criteria ratings add values to the existing CF-based recommender systems to provide more accurate recommendation results to users. However, all the previous works require multi-criteria ratings given by users explicitly while most of the existing datasets such as Netflix and MovieLens are a single criterion. Therefore, to take advantage of multi-criteria recommendation, there must be a way to extract necessary aspects and analyze users' preferences on those aspects from the given single-criterion type of dataset. In this paper, we propose an approach of utilizing semantic information of items to extract essential aspects to perform multi-aspect collaborative filtering to recommend users with items in a personalized manner.
Subjective Similarity: Personalizing Alternative Item Recommendations
Authors: Tolga Könik, Rajyashree Mukherjee, Jayasimha Katukuri
Venue: WWW
Year: 2015

We present a new algorithm for recommending alternatives to a given item in an e-commerce setting. Our algorithm is an incremental improvement over an earlier system, which recommends similar items by first assigning the input item to clusters and then selecting best quality items within those clusters. The original algorithm does not consider the recent context and our new algorithm improves the earlier system by personalizing the recommendations to user intentions. The system measures user intention using the recent queries, which are used to determine the level of abstraction in similarity and relative importance of similarity dimensions. We show that user engagement increases when recommended item titles share more terms with most recent queries. Moreover, the new algorithm increases query coverage without sacrificing input item similarity and item quality.
Towards a Complete Event Type Taxonomy
Authors: Aljaz Kosmerlj, Evgenia Belyaeva, Gregor Leban, Marko Grobelnik, Blaz Fortuna
Venue: WWW
Year: 2015

We present initial results of our effort to build an extensive and complete taxonomy of events described in news articles. By crawling Wikipedia's current events portal we identified nine top-level event types. Using articles referenced by the portal we built a event type classification model for news articles using lexical and semantic features and present a small-scale manual evaluation of its results. Results show that our model can accurately distinguish between event types but its coverage could still be significantly improved.
Extracting Taxonomies from Bipartite Graphs
Authors: Tobias Kötter, Stephan Günnemann, Christos Faloutsos, Michael R. Berthold
Venue: WWW
Year: 2015

Given a large bipartite graph that represents objects and their properties, how can we automatically extract semantic information that provides an overview of the data and -- at the same time -- enables us to drill down to specific parts for an in-depth analysis? In this work in-progress paper, we propose extracting a taxonomy that models the relation between the properties via an is-a hierarchy. The extracted taxonomy arranges the properties from general to specific providing different levels of abstraction.
Community Change Detection in Dynamic Networks in Noisy Environment
Authors: Sadamori Koujaku, Mineichi Kudo, Ichigaku Takigawa, Hideyuki Imai
Venue: WWW
Year: 2015

Detection of anomalous changes in social networks has been studied in various applications such as change detection of social interests and virus infections. Among several kinds of network changes, we concentrate on the structural changes of relatively small stationary communities. Such a change is important because it implies that some crucial changes have happened in a special group, such as dismiss of a board of directors. One difficulty is that we have to do this in a noisy environment. This paper, therefore, proposes an algorithm that finds stationary communities in a noisy environment. Experiments on two real networks showed the advantages of our proposed algorithm.
Events and Controversies: Influences of a Shocking News Event on Information Seeking
Authors: Danai Koutra, Paul N. Bennett, Eric Horvitz
Venue: WWW
Year: 2015

It has been suggested that online search and retrieval contributes to the intellectual isolation of users within their preexisting ideologies, where people's prior views are strengthened and alternative viewpoints are infrequently encountered. This so-called "filter bubble" phenomenon has been called out as especially detrimental when it comes to dialog among people on controversial, emotionally charged topics, such as the labeling of genetically modified food, the right to bear arms, the death penalty, and online privacy. We seek to identify and study information-seeking behavior and access to alternative versus reinforcing viewpoints following shocking, emotional, and large-scale news events. We choose for a case study to analyze search and browsing on gun control/rights, a strongly polarizing topic for both citizens and leaders of the United States. We study the period of time preceding and following a mass shooting to understand how its occurrence, follow-on discussions, and debate may have been linked to changes in the patterns of searching and browsing. We employ information-theoretic measures to quantify the diversity of Web domains of interest to users and understand the browsing patterns of users. We use these measures to characterize the influence of news events on these web search and browsing patterns.