OpenRaiser/Intern-Atlas huggingface dataset:
Using Participatory Web-based Surveillance Data to Improve Seasonal Influenza Forecasting in Italy
Authors: Daniela Perrotta, Michele Tizzoni, Daniela Paolotti
Venue: WWW
Year: 2017

Traditional surveillance of seasonal influenza is generally affected by reporting lags of at least one week and by continuous revisions of the numbers initially released. As a consequence, influenza forecasts are often limited by the time required to collect new and accurate data. On the other hand, the availability of novel data streams for disease detection can help in overcoming these issues by capturing an additional surveillance signal that can be used to complement data collected by public health agencies. In this study, we investigate how combining both traditional and participatory Web-based surveillance data can provide accurate predictions for seasonal influenza in real-time fashion. To this aim, we use two data sources available in Italy from two different monitoring systems: traditional surveillance data based on sentinel doctors reports and digital surveillance data deriving from a participatory system that monitors the influenza activity through Internet-based surveys. We integrate such digital component in a linear autoregressive exogenous (ARX) model based on traditional surveillance data and evaluate its predictive ability over the course of four influenza seasons in Italy, from 2012-2013 to 2015-2016, for each of the four weekly time horizons. Our results show that by using data extracted from a Web-based participatory surveillance system, which are usually available one week in advance with respect to traditional surveillance, it is possible to obtain accurate weekly predictions of influenza activity at national level up to four weeks in advance. Compared to a model that is only based on data from sentinel doctors, our approach significantly improves real-time forecasts of influenza activity, by increasing the Pearson's correlation up to 30% and by reducing the Mean Absolute Error up to 43% for the four weekly time horizons.
The Web-Wide World
Authors: Mark Pesce
Venue: WWW
Year: 2017

The great project of the World Wide Web has succeeded - a large portion of the world's information is now instantly accessible through open protocols and open presentation formats. The Web is as Sir Tim Berners-Lee envisioned it, a vast resource of interconnected knowledge. Yet that resource exists in a universe of its own. Meanwhile the real world has become crowded with connected devices, none more significant than the smartphone - bringing the Web to eighty percent of the planet's adult population by the end of this decade. Smartphones have become fantastically adept at navigating cyberspace, but - with the singular exception of maps - have few real connections to the world immediately at hand.
Notability Determination for Wikipedia
Authors: Yashaswi Pochampally, Kamalakar Karlapalem
Venue: WWW
Year: 2017

Being the ever-growing online encyclopedia, Wikipedia requires a keen investigation about which articles are to be included for it to maintain its indispensability. To prevent unnecessary articles from being included, official guidelines of Wikipedia demand these named entities to meet "notability" standards for their article inclusion. In this paper, we evaluate named entities for their notability by using reliability and entity salience features. Evaluations of our system provide evidence for the viability of our solution as an alternative to the manual decisions made by the reviewers for inclusion of an article using the notability rules.
Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media
Authors: Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, Gerhard Weikum
Venue: WWW
Year: 2017

The web is a huge source of valuable information. However, in recent times, there is an increasing trend towards false claims in social media, other web-sources, and even in news. Thus, factchecking websites have become increasingly popular to identify such misinformation based on manual analysis. Recent research proposed methods to assess the credibility of claims automatically. However, there are major limitations: most works assume claims to be in a structured form, and a few deal with textual claims but require that sources of evidence or counter-evidence are easily retrieved from the web. None of these works can cope with newly emerging claims, and no prior method can give user-interpretable explanations for its verdict on the claim's credibility.
Visualizing Scholarly Publications and Citations to Enhance Author Profiles
Authors: Jason Portenoy, Jevin D. West
Venue: WWW
Year: 2017

With data on scholarly publications becoming more abundant and accessible, there exist new opportunities for using this information to provide rich author profiles to display and explore scholarly work. We present a pair of linked visualizations connected to the Microsoft Academic Graph that can be used to explore the publications and citations of individual authors. We provide an online application with which a user can manage collections of papers and generate these visualizations.
Sketching Linguistic Borders: Mobility Analysis on Multilingual Microbloggers
Authors: Muhammad Syafiq Mohd Pozi, Yukiko Kawai, Adam Jatowt, Toyokazu Akiyama
Venue: WWW
Year: 2017

Twitter has been used for various kinds of sociological studies including also multi-lingual analysis. In this paper we study the phenomenon of multilingualism in Twitter from a novel viewpoint. We advance the existing studies by correlating user mobility and multilingualism. The results we show can be used for explaining the usage of languages based on user location and mobility.
Mining Unusual Search Behavior Related to Physical Events
Authors: Abhay Prakash, Parag Agrawal, Kedhar Nath Narahari, Puneet Agrawal
Venue: WWW
Year: 2017

Search logs of commercial search engines like Bing or Google reflect the topics of interest in humans at any given point of time. While many of these topics are predictable information needs, some are unusual and point to a curious human mind. In this paper, we propose a novel solution for mining unusual search behaviors triggered by some recent physical events using Bing search logs. Our algorithm successfully identified unusual search behaviors related to physical events during 2016. We define unusual-relatedness and use crowdsource judgment to evaluate quality of our results along with a qualitative analysis. Our results show a 9.5 times improvement over baseline.
Constructing Structured Information Networks from Massive Text Corpora
Authors: Xiang Ren, Meng Jiang, Jingbo Shang, Jiawei Han
Venue: WWW
Year: 2017

In today's computerized and information-based society, text data is rich but messy. People are soaked with vast amounts of natural-language text data, ranging from news articles, social media post, advertisements, to a wide range of textual information from various domains (medical records, corporate reports). To turn such massive unstructured text data into actionable knowledge, one of the grand challenges is to gain an understanding of the factual information (e.g., entities, attributes, relations, events) in the text. In this tutorial, we introduce data-driven methods to construct structured information networks (where nodes are different types of entities attached with attributes, and edges are different relations between entities) for text corpora of different kinds (especially for massive, domain-specific text corpora) to rep- resent their factual information. We focus on methods that are minimally-supervised, domain-independent, and language-independent for fast network construction across various application domains (news, web, biomedical, reviews). We demonstrate on real datasets including news articles, scientific publications, tweets and reviews how these constructed networks aid in text analytics and knowledge discovery at a large scale.
Ties That Bind: Characterizing Classes by Attributes and Social Ties
Authors: Aria Rezaei, Bryan Perozzi, Leman Akoglu
Venue: WWW
Year: 2017

Given a set of attributed subgraphs known to be from different classes, how can we discover their differences? There are many cases where collections of subgraphs may be contrasted against each other. For example, they may be as- signed ground truth labels (spam/not-spam), or it may be desired to directly compare the biological networks of different species or compound networks of different chemicals. In this work we introduce the problem of characterizing the differences between attributed subgraphs that belong to different classes. We define this characterization problem as one of partitioning the attributes into as many groups as the number of classes, while maximizing the total attributed quality score of all the given subgraphs. We show that our attribute-to-class assignment problem is NP-hard and an optimal (1 -- 1/e)-approximation algorithm exists. We also propose two different faster heuristics that are linear-time in the number of attributes and subgraphs. Unlike previous work where only attributes were taken into account for characterization, here we exploit both attributes and social ties (i.e. graph structure). Through extensive experiments, we compare our proposed algorithms, show findings that agree with human intuition on datasets from Amazon co-purchases, Congressional bill sponsorships and DBLP co-authorships. We also show that our approach of characterizing subgraphs is better suited for sense-making than discriminating classification approaches.
Beyond the Stars: Towards a Novel Sentiment Rating to Evaluate Applications in Web Stores of Mobile Apps
Authors: Phillipe Rodrigues, Ismael Santana Silva, Glívia Angélica Rodrigues Barbosa, Flávio Roberto dos Santos Coutinho, Fernando Mourão
Venue: WWW
Year: 2017

This paper proposes an approach to evaluate mobile applications which complements the information provided by the number of stars and downloads in app stores. The goal is to provide novel information to assist users in the decision-making process regarding the choice of applications. In this sense, we conducted experiments to verify the relationship between the number of stars and the content of review comments. Results indicated that there is information in reviews not properly represented by stars. Thus, we present a sentiment rating generated automatically by aggregating opinions reported in the reviews related to each application. We evaluated this new rating using 26,996 reviews related to six applications present on the Google Play Store. The obtained results allow us to demonstrate that: (1) it is possible and feasible to generate a sentiment rating automatically and (2) the rating is useful for web stores of mobile applications to improve their mechanisms of ranking and recommendation as well as to assist users and developers to evaluate the quality and/or acceptance of the offered mobile applications.
Push or Request: An Investigation of HTTP/2 Server Push for Improving Mobile Performance
Authors: Sanae Rosen, Bo Han, Shuai Hao, Zhuoqing Morley Mao, Feng Qian
Venue: WWW
Year: 2017

In HTTP/1.1, it is necessary for the client to request an object (e.g. an image in a page) in order for the server to send it, even if the server knows in advance what the client will need. Server Push is a feature introduced in HTTP/2 that promises to improve page load times (PLT) by having the server push content to the browser in advance. In this paper, we investigate the benefits and challenges of using Server Push on mobile devices. We first examine whether pushing all content or just the CSS and Javascript files performs better, and find the former leads to much better web performance. Also, we find that sites making use of domain sharding or which otherwise have content divided across many servers do not benefit much from Server Push, a major challenge for Server Push going forward. Network performance characteristics also play a major role. Server Push is especially effective at improving performance at high loss rates (16% median PLT reduction with a 2% loss rate) and high latencies (14% PLT reduction with 100 ms latency), and has little benefit for high-speed Ethernet connections. This motivates its use on mobile devices, although we also find the limited processing power of these devices limits the benefits of Server Push. Server Push also offers modest energy benefits, with energy savings of 9% on LTE for one device. Overall, Server Push is a promising approach for improving web performance in mobile networks, but there are a number of challenges in achieving the full benefits of Server Push.
Cascades: A View from Audience
Authors: Rahmtin Rotabi, Krishna Kamath, Jon M. Kleinberg, Aneesh Sharma
Venue: WWW
Year: 2017

Cascades on social and information networks have been a tremendously popular subject of study in the past decade, and there is a considerable literature on phenomena such as diffusion mechanisms, virality, cascade prediction, and peer network effects. Against the backdrop of this research, a basic question has received comparatively little attention: how desirable are cascades on a social media platform from the point of view of users' While versions of this question have been considered from the perspective of the producers of cascades, any answer to this question must also take into account the effect of cascades on their audience --- the viewers of the cascade who do not directly participate in generating the content that launched it. In this work, we seek to fill this gap by providing a consumer perspective of information cascades. Users on social and information networks play the dual role of producers and consumers, and our work focuses on how users perceive cascades as consumers. Starting from this perspective, we perform an empirical study of the interaction of Twitter users with retweet cascades. We measure how often users observe retweets in their home timeline, and observe a phenomenon that we term the Impressions Paradox: the share of impressions for cascades of size k decays much more slowly than frequency of cascades of size k. Thus, the audience for cascades can be quite large even for rare large cascades. We also measure audience engagement with retweet cascades in comparison to non-retweeted or organic content. Our results show that cascades often rival or exceed organic content in engagement received per impression. This result is perhaps surprising in that consumers didn't opt in to see tweets from these authors. Furthermore, although cascading content is widely popular, one would expect it to eventually reach parts of the audience that may not be interested in the content. Motivated by the tension in these empirical findings, we posit a simple theoretical model that focuses on the effect of cascades on the audience (rather than the cascade producers). Our results on this model highlight the balance between retweeting as a high-quality content selection mechanism and the role of network users in filtering irrelevant content. In particular, the results suggest that together these two effects enable the audience to consume a high quality stream of content in the presence of cascades.
Syntactic Representation Learning for Open Information Extraction on Web
Authors: Chengsen Ru, Jintao Tang, Shasha Li, Ting Wang
Venue: WWW
Year: 2017

This paper proposes a representation learning based method to discover new relations between entities from web, which is more general than existing Open Information Extraction(OIE) methods. Given dependency sequences on the expandPath as input, a convolutional neural network(CNN) is adopted to learn the representation layer features of the syntactic dependency patterns which indicate the relations. Experimental results show that compared with the state-of-art OIE methods, the proposed method obviously improves recall without much expense of precision.
An Adaptive Method for Clustering by Fast Search-and-Find of Density Peaks: Adaptive-DP
Authors: Shanshan Ruan, Rashid Mehmood, Ali Daud, Hussain Dawood, Jalal S. Alowibdi
Venue: WWW
Year: 2017

Clustering by fast search and find of density peaks (DP) is a method in which density peaks are used to select the number of cluster centers. The DP has two input parameters: 1) the cutoff distance and 2) cluster centers. Also in DP, different methods are used to measure the density of underlying datasets. To overcome the limitations of DP, an Adaptive-DP method is proposed. In Adaptive-DP method, heat-diffusion is used to estimate density, cutoff distance is simplified, and novel method is used to discover exact number of cluster centers, adaptively. To validate the proposed method, we tested it on synthetic and real datasets, and comparison are done with the state of the art clustering methods. The experimental results validate the robustness and effectiveness of proposed method.
meCurate: Personalized Curation Service using a Tiny Text Intelligence
Authors: Woo-Jong Ryu, Jung-Hyun Lee, Kang-Min Kim, SangKeun Lee
Venue: WWW
Year: 2017

The necessity and importance of intelligent content curation have been demanding, as exemplified by Google Photos and Inbox. In this demo, we demonstrate a personalized curation service on a smartphone, called meCurate. By understanding implicit user interests from user smartphone usage data, meCurate intelligently organizes both in-device user content (e.g., SMSs and bookmarks) and external content (e.g., news articles crawled from The New York Times website), which are likely to match the inferred user's interests. In addition, meCurate retrieves semantically relevant in-device user content and external content to an explicit text or voice query. To this end, we utilize a tiny text intelligence which is suitable for smartphones with limited resources. Notably, meCurate works in a stand-alone, privacy-protecting manner without sending out any in-device personal data or content, resulting in a unique user experience.
Particle Filter Inference based on Activities for Overlapping Community Models
Authors: Shohei Sakamoto, Koji Eguchi
Venue: WWW
Year: 2017

Various kinds of data such as social media can be represented as a network or graph. Latent variable models using Bayesian statistical inference are powerful tools to represent such networks. One such latent variable network model is a Mixed Membership Stochastic Blockmodel (MMSB), which can discover overlapping communities in a network and has high predictive power. Previous inference methods estimate the latent variables and unknown parameters of the MMSB on the basis of the whole observed network. Therefore, dynamic changes in network structure over time are hard to track. Thus, we present a particle filter based on node activities with various term lengths for online sequential estimation of the MMSB. For instance, in an e-mail communication network, each particle only considers e-mail accounts that sent or received a message within a specific term length, where the length may be different from those of other particles. We show through experiments that our proposed methods achieve both high prediction performance and computational efficiency.
The Onions Have Eyes: A Comprehensive Structure and Privacy Analysis of Tor Hidden Services
Authors: Iskander Sánchez-Rola, Davide Balzarotti, Igor Santos
Venue: WWW
Year: 2017

Tor is a well known and widely used darknet, known for its anonymity. However, while its protocol and relay security have already been extensively studied, to date there is no comprehensive analysis of the structure and privacy of its Web Hidden Service.
Nonlinear Characterization of Activity Dynamics in Online Collaboration Websites
Authors: Tiago Santos, Simon Walk, Denis Helic
Venue: WWW
Year: 2017

Modeling activity in online collaboration websites, such as StackExchange Question and Answering portals, is becoming increasingly important, as the success of these websites critically depends on the content contributed by its users. In this paper, we represent user activity as time series and perform an initial analysis of these time series to obtain a better understanding of the underlying mechanisms that govern their creation. In particular, we are interested in identifying latent nonlinear behavior in online user activity as opposed to a simpler linear operating mode. To that end, we apply a set of statistical tests for nonlinearity as a means to characterize activity time series derived from $16$ different online collaboration websites. We validate our approach by comparing activity forecast performance from linear and nonlinear models, and study the underlying dynamical systems we derive with nonlinear time series analysis. Our results show that nonlinear characterizations of activity time series help to (i) improve our understanding of activity dynamics in online collaboration websites, and (ii) increase the accuracy of forecasting experiments.
Modeling the Influence of Popular Trending Events on User Search Behavior
Authors: Shubhra Kanti Karmaker Santu, Liangda Li, Dae Hoon Park, Yi Chang, ChengXiang Zhai
Venue: WWW
Year: 2017

Understanding how users' search behavior is influenced by real world events is important both for social science research and for designing better search engines for users. In this paper, we study how to model the influence of events on user queries by framing it as a novel data mining problem. Specifically, given a text description of an event, we mine the search log data to identify queries that are triggered by it and further characterize the temporal trend of influence created by the same event on user queries. We solve this data mining problem by proposing computational measures that quantify the influence of an event on a query to identify triggered queries and then, proposing a novel extension of Hawkes process to model the evolutionary trend of the influence of an event on search queries. Evaluation results using news articles and search log data show that the proposed approach is effective for identification of queries triggered by events reported in news articles and characterization of the influence trend over time, opening up many interesting opportunities of applications such as comparative analysis of influential events and prediction of event-triggered queries by users.
Predicting the Impact of Software Engineering Topics: An Empirical Study
Authors: Santonu Sarkar, Rumana Lakdawala, Subhajit Datta
Venue: WWW
Year: 2017

Predicting the future is hard, more so in active research areas. In this paper, we customize an established model for citation prediction of research papers and apply it on research topics. We argue that research topics, rather than individual publications, have wider relevance in the research ecosystem, for individuals as well as organizations. In this study, topics are extracted from a corpus of software engineering publications covering 55,000+ papers written by more than 70,000 authors across 56 publication venues, over a span of 38 years, using natural language processing techniques. We demonstrate how critical aspects of the original paper-based prediction model are valid for a topic-based approach. Our results indicate the customized model is able to predict citations for many of the topics considered in our study with reasonably high accuracy. Insights from these results indicate the promise of citation of prediction of research topics, and its utility for individual researchers, as well as research groups.
Towards Understanding the Evolution of the WWW Conference
Authors: Pavel Savov, Adam Jatowt, Radoslaw Nielek
Venue: WWW
Year: 2017

The World Wide Web conference is a well-established and mature venue with an already long history. Over the years it has been attracting papers reporting many important research achievements centered around the Web. In this work we aim at understanding the evolution of WWW conference series by detecting crucial years and important topics. We propose a simple yet novel approach based on tracking the classification errors of the conference papers according to their predicted publication years.