OpenRaiser/Intern-Atlas huggingface dataset:
Linking Entities for Enriching and Structuring Social Media Content
Authors: Raphaël Troncy
Venue: WWW
Year: 2016

Social media platforms such as Twitter, Facebook or LinkedIn become a reliable source of news and play a key role for being aware of events around the world. Using social media to recognize, enrich or summarize events is however very challenging. In the first part of this talk, we will present ADEL, a novel hybrid architecture for an adaptive entity linking system, that combines methods from the natural language processing, information retrieval and semantic fields. The framework enables to link all the mentions occurring in a text to their entity counterparts in a knowledge base. It is modular and adaptive since it enables to process text written in different languages and of different kind (newswire, tweets, blog posts, etc.) while entities can be of common types (PERSON, LOCATION and ORGANIZATION) or specific ones (dates, numbers) and be disambiguated in generic or specialized knowledge bases. We will show how ADEL can outperform the state-of-the-art systems on the reference NEEL challenges that happens in the yearly #Micropost workshop (2014-2016).
Temporal Analytics for Predictive Cyber Threat Intelligence
Authors: Staffan Truvé
Venue: WWW
Year: 2016

Recorded Future has developed its Temporal Analytics Engine as a general purpose platform for harvesting and analyzing unstructured text from the open, deep, and dark web, and for transforming that content into a structured representation suitable for different analyses.
Tracing and Predicting Collaboration for Junior Scholars
Authors: Chun-Hua Tsai, Yu-Ru Lin
Venue: WWW
Year: 2016

Academic publication is a key indicator for measuring scholars' scientific productivity and has a crucial impact on their future career. Previous work has identified the positive association between the number of collaborators and academic productivity, which motivates the problem of tracing and predicting potential collaborators for junior scholars. Nevertheless, the insufficient publication record makes current approaches less effective for junior scholars. In this paper, we present an exploratory study of predicting junior scholars' future co-authorship in three different network density. By combining features based on affiliation, geographic and content information, the proposed model significantly outperforms the baseline methods by 12% in terms of sensitivity. Furthermore, the experiment result shows the association between network density and feature selection strategy. Our study sheds light on the re-evaluation of existing approaches to connect scholars in the emerging worldwide Web of Scholars.
Identifying Web Queries with Question Intent
Authors: Gilad Tsur, Yuval Pinter, Idan Szpektor, David Carmel
Venue: WWW
Year: 2016

Vertical selection is the task of predicting relevant verticals for a Web query so as to enrich the Web search results with complementary vertical results. We investigate a novel variant of this task, where the goal is to detect queries with a question intent. Specifically, we address queries for which the user would like an answer with a human touch. We call these CQA-intent queries, since answers to them are typically found in community question answering (CQA) sites. A typical approach in vertical selection is using a vertical's specific language model of relevant queries and computing the query-likelihood for each vertical as a selective criterion. This works quite well for many domains like Shopping, Local and Travel. Yet, we claim that queries with CQA intent are harder to distinguish by modeling content alone, since they cover many different topics. We propose to also take the structure of queries into consideration, reasoning that queries with question intent have quite a different structure than other queries. We present a supervised classification scheme, random forest over word-clusters for variable length texts, which can model the query structure. Our experiments show that it substantially improves classification performance in the CQA-intent selection task compared to content-oriented based classification, especially as query length grows.
Monitoring Parkinson's Disease Progression Using Behavioural Inferences, Mobile Devices and Web Technologies
Authors: Julio Vega
Venue: WWW
Year: 2016

Parkinson's Disease (PD) affects patients' motor and non-motor functionality. Traditional assessment techniques are inaccurate because PD symptoms vary throughout the day and are evaluated in sporadic and subjective sessions. Although recent works have utilised wearable devices to try to overcome these issues, most are unsuitable for following patients regularly for a long time. In contrast, my approach aims to monitor PD continuously in a longitudinal, naturalistic, non-disruptive and non-intrusive way. It uses smartphones to log and transmit over the Internet social, environmental, and interaction data about patients and their surroundings. This data is complemented with other web data sources (i.e., geographical and weather data) and then processed to infer a set of metrics (a latent behavioural variable or LBV) of people's activities and habits. Then, the LBV's trends are measured and mapped to the progression of the disease. As a part of the pilot study to test the proposed methodology, I have collected ~290 million records from 2 patients, making this dataset 34.5x bigger and 4x richer than state-of-the-art sets. I used the collected data to identify six possible PD-related LBVs. This project aims to get a more accurate disease picture and to reduce the physical and psychological burden of traditional assessment methods. Ultimately, the work has the potential to save patients' time and improve the efficiency and effectiveness of health services.
Strengthening Weak Identities Through Inter-Domain Trust Transfer
Authors: Giridhari Venkatadri, Oana Goga, Changtao Zhong, Bimal Viswanath, Krishna P. Gummadi, Nishanth Sastry
Venue: WWW
Year: 2016

On most current websites untrustworthy or spammy identities are easily created. Existing proposals to detect untrustworthy identities rely on reputation signals obtained by observing the activities of identities over time within a single site or domain; thus, there is a time lag before which websites cannot easily distinguish attackers and legitimate users. In this paper, we investigate the feasibility of leveraging information about identities that is aggregated across multiple domains to reason about their trustworthiness. Our key insight is that while honest users naturally maintain identities across multiple domains (where they have proven their trustworthiness and have acquired reputation over time), attackers are discouraged by the additional effort and costs to do the same. We propose a flexible framework to transfer trust between domains that can be implemented in today's systems without significant loss of privacy or significant implementation overheads.
Analyzing Global and Pairwise Collective Spatial Attention for Geo-social Event Detection in Microblogs
Authors: Shoko Wakamiya, Adam Jatowt, Yukiko Kawai, Toyokazu Akiyama
Venue: WWW
Year: 2016

Microblogging has been recently used for detecting common opinions of users at different geographic places. In this paper we propose a novel spatial visualization system for uncovering collective spatial attention and interest of users not at but rather towards different locations. In other words, we aim to answer questions of the type: what do users collectively talk about when they refer to certain geographical places? In addition, we analyze relations between geographical locations from where Twitter users issue messages and the locations they tweet about. This allows answering questions such as: what do users at a certain place commonly talk about when they refer to another geographical place? We demonstrate an online visualization system that supports the interactive analysis of collective spatial attention over time using 4 months' long collection of tweets in USA.
Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model
Authors: Shuai Wang, Zhiyuan Chen, Bing Liu
Venue: WWW
Year: 2016

Aspect-level sentiment analysis or opinion mining consists of several core sub-tasks: aspect extraction, opinion identification, polarity classification, and separation of general and aspect-specific opinions. Various topic models have been proposed by researchers to address some of these sub-tasks. However, there is little work on modeling all of them together. In this paper, we first propose a holistic fine-grained topic model, called the JAST (Joint Aspect-based Sentiment Topic) model, that can simultaneously model all of above problems under a unified framework. To further improve it, we incorporate the idea of lifelong machine learning and propose a more advanced model, called the LAST (Lifelong Aspect-based Sentiment Topic) model. LAST automatically mines the prior knowledge of aspect, opinion, and their correspondence from other products or domains. Such knowledge is automatically extracted and incorporated into the proposed LAST model without any human involvement. Our experiments using reviews of a large number of product domains show major improvements of the proposed models over state-of-the-art baselines.
Improving Music Recommendation Using Distributed Representation
Authors: Dongjing Wang, ShuiGuang Deng, Songguo Liu, Guandong Xu
Venue: WWW
Year: 2016

In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods.
KCF.js: A Javascript Library for Knowledge Cards Fusion
Authors: Haofen Wang, Zhijia Fang, Tong Ruan
Venue: WWW
Year: 2016

Recently Web search engines have built knowledge graphs to support entity search and to provide structural summaries called \emph{knowledge cards} for entities mentioned in queries. Different knowledge cards might be complementary or even have conflicts on values of the equivalent property. Thus, it is essential to achieve a more comprehensive fused card from those individual cards representing the same entity. In this paper, we present a system with technical details of card disambiguation, property alignment, value deduplication and card ranking to fuse knowledge cards from various search engines. We further develop a Javascript library called KCF.js based on the card fusion engine and demonstrates its usability via three possible applications.
The Practice of Web Product Design and Development Course Design
Authors: Xiaoxuan Wang, Jiale Gao
Venue: WWW
Year: 2016

People who design and develop web product need interdisciplinary knowledge and skills from various fields. Thus in most Chinese vocational colleges, students are lack of a completed experience to fulfill employers' needs before they leave colleges. In this paper, we propose a project-based approach to design the course, in which students unify business, product design and development theories into one project practice to build the web product. Over different stages and increasingly complexity, students learn how to interact with customers, build project management skills, design product with business mind instead of focusing on design aesthetics and developing programming skills only.
NERank: Ranking Named Entities in Document Collections
Authors: Chengyu Wang, Rong Zhang, Xiaofeng He, Aoying Zhou
Venue: WWW
Year: 2016

While most of the entity ranking research focuses on Web corpora with user queries as input, little has been done to rank entities directly from documents. We propose a ranking algorithm NERank to address this issue. NERank employs a random walk process on a weighted tripartite graph mined from the document collection. We evaluate NERank over real-life document datasets and compare it with baselines. Experimental results show the effectiveness of our method.
Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks
Authors: Samuel F. Way, Daniel B. Larremore, Aaron Clauset
Venue: WWW
Year: 2016

Women are dramatically underrepresented in computer science at all levels in academia and account for just 15% of tenure-track faculty. Understanding the causes of this gender imbalance would inform both policies intended to rectify it and employment decisions by departments and individuals. Progress in this direction, however, is complicated by the complexity and decentralized nature of faculty hiring and the non-independence of hires. Using comprehensive data on both hiring outcomes and scholarly productivity for 2659 tenure-track faculty across 205 Ph.D.-granting departments in North America, we investigate the multi-dimensional nature of gender inequality in computer science faculty hiring through a network model of the hiring process. Overall, we find that hiring outcomes are most directly affected by (i) the relative prestige between hiring and placing institutions and (ii) the scholarly productivity of the candidates. After including these, and other features, the addition of gender did not significantly reduce modeling error. However, gender differences do exist, e.g., in scholarly productivity, postdoctoral training rates, and in career movements up the rankings of universities, suggesting that the effects of gender are indirectly incorporated into hiring decisions through gender's covariates. Furthermore, we find evidence that more highly ranked departments recruit female faculty at higher than expected rates, which appears to inhibit similar efforts by lower ranked departments. These findings illustrate the subtle nature of gender inequality in faculty hiring networks and provide new insights to the underrepresentation of women in computer science.
Computational Social Science for the World Wide Web (CSSW3)
Authors: Ingmar Weber, Claudia Wagner, Markus Strohmaier, Luca Maria Aiello
Venue: WWW
Year: 2016

This tutorial aims at outlining fundamental methods for studying typical social science research questions with organic data (i.e., data that has not been designed for a specific research purpose but can be found on the Web). Further, social theories, statistical methods and models that help to understand the processes that generated the data will be discussed. Participants will learn (1) how to turn theoretical assumptions into models and test them, (2) how to validate measurements and (3) how to approximate causality when working with organic data.
Wikipedia and Stock Return: Wikipedia Usage Pattern Helps to Predict the Individual Stock Movement
Authors: Pengyu Wei, Ning Wang
Venue: WWW
Year: 2016

Vast volumes of online information related to news stories, blogs and online social media have an observable effect on investor's opinions towards financial markets. But do these particular information reflect or impact people's decision-making in investment? This paper investigates whether data generated from Internet usage can be used to predict the movements in the financial market. We provide evidence that data on how often a company's Wikipedia page is being viewed is linked to its subsequent performance in the stock market. We then develop a portfolio in line with the Wikipedia usages and demonstrate that our investment strategy based on Wikipedia views is profitable both financially and statistically. Our finding implies that online web data such as Wikipedia presents an alternative insights on collecting and quantifying investor's sentiments towards financial markets, which can be further employed as a timely approximation of investor's behaviours in decision-making.
Capturing and Annotating Processes using a Collaborative Platform
Authors: Tobias Weller, Maria Maleshkova
Venue: WWW
Year: 2016

Existing standards in capturing processes concentrate on client tools. Furthermore, semantic information are often available that cannot be captured in a structured way with the proposed standard formats. In addition, processes are usually used and maintained by multiple persons. Therefore, a collaborative platform to discuss and share information about processes is valuable.
Citation Distance: Measuring Changes in Scientific Search Strategies
Authors: Ryan Whalen, Yun Huang, Craig Tanis, Anup Sawant, Brian Uzzi, Noshir S. Contractor
Venue: WWW
Year: 2016

Using latent semantic analysis on the full text of scientific articles, we measure the distance between 36 million citing/cited article pairs and chart changes in citation proximity over time. The analysis shows that the mean distance between citing and cited articles has steadily increased since 1990. This demonstrates that current scholars are more likely to cite distantly related research than their peers of 20 years ago who tended to cite more proximate work. These changes coincide with the introduction of new information technologies like the Internet, and the increasing popularity of interdisciplinary and multidisciplinary research. The "citation distance" measure shows promise in improving our understanding of the evolution of knowledge. It also offers a method to add nuance to scholarly impact measures by assessing the extent to which an article influences proximate or distant future work.
Veracity and Velocity of Social Media Content during Breaking News: Analysis of November 2015 Paris Shootings
Authors: Stefanie Wiegand, Stuart E. Middleton
Venue: WWW
Year: 2016

Social media sources are becoming increasingly important in journalism. Under breaking news deadlines semi-automated support for identification and verification of content is critical. We describe a large scale content-level analysis of over 6 million Twitter, You Tube and Instagram records covering the first 6 hours of the November 2015 Paris shootings. We ground our analysis by tracing how 5 ground truth images used in actual news reports went viral. We look at velocity of newsworthy content and its veracity with regards trusted source attribution. We also examine temporal segmentation combined with statistical frequency counters to identify likely eyewitness content for input to real-time breaking content feeds. Our results suggest attribution to trusted sources might be a good indicator of content veracity, and that temporal segmentation coupled with frequency statistical metrics could be used to highlight in real-time eyewitness content if applied with some additional text filters.
Crowdsourcing Annotations for Websites' Privacy Policies: Can It Really Work?
Authors: Shomir Wilson, Florian Schaub, Rohan Ramanath, Norman M. Sadeh, Fei Liu, Noah A. Smith, Frederick Liu
Venue: WWW
Year: 2016

Website privacy policies are often long and difficult to understand. While research shows that Internet users care about their privacy, they do not have time to understand the policies of every website they visit, and most users hardly ever read privacy policies. Several recent efforts aim to crowdsource the interpretation of privacy policies and use the resulting annotations to build more effective user interfaces that provide users with salient policy summaries. However, very little attention has been devoted to studying the accuracy and scalability of crowdsourced privacy policy annotations, the types of questions crowdworkers can effectively answer, and the ways in which their productivity can be enhanced. Prior research indicates that most Internet users often have great difficulty understanding privacy policies, suggesting limits to the effectiveness of crowdsourcing approaches. In this paper, we assess the viability of crowdsourcing privacy policy annotations. Our results suggest that, if carefully deployed, crowdsourcing can indeed result in the generation of non-trivial annotations and can also help identify elements of ambiguity in policies. We further introduce and evaluate a method to improve the annotation process by predicting and highlighting paragraphs relevant to specific data practices.
Get To the Top and Stay There: A Study of Citation Rank Dynamics in Academia
Authors: Yan Wu, Srini Venkat, Dah-Ming Chiu
Venue: WWW
Year: 2016

Citations serve as an important metric for identifying experts and opinion leaders in academic communities. In this paper, we analyze the evolution of yearly rankings of top-cited (C-list) authors in the domain of Computer Science. In searching for factors that help authors become top-cited, we also gather authors in the top-collaboration list (A-list) and top-publication list (P-list) for each year, and analyze cross-correlation of the C-list with the corresponding A-list and P-list for each year. Results show that the A-list and P-list serve as (unreliable) indicators for appearance on the C-list, but their effect is quick and short-lived. Through further case studies we find other key factors, such as the seminal importance of an author's publication and the association on an author's work with hot topic trends, may significantly affect rank dynamics. Based on the study of citation rank dynamics in academia, we then discuss the modeling of rank dynamics, specifically a model based on item visibility and item strength, and the general applicability of such a model.
Learning Web Queries for Retrieval of Relevant Information about an Entity in a Wikipedia Category
Authors: Vikrant Yadav, Sandeep Kumar
Venue: WWW
Year: 2016

In this paper, we present a novel method to obtain a set of most appropriate queries for retrieval of relevant information about an entity from the Web. Using the body text of existing articles in a Wikipedia category, we generate a set of queries capable of fetching the most relevant content for any entity belonging to that category. We find the common topics discussed in the articles of a category using Latent Semantic Analysis (LSA) and use them to formulate the queries. Using Long Short-Term Memory (LSTM) neural network, we reduce the number of queries by removing the less sensible ones and then select the best ones out of them. The experimental results show that the proposed method outperforms the baselines. Existing approaches are performing better in generation of the relevant section title queries by extraction from the headings of the Wikipedia articles as compared to the generation of queries by extraction from the body text of the articles. Whereas, the experimental results show that the proposed approach can perform equally well and even better in extraction of the relevant queries from the body text of the Wikipedia articles.
Detection of Multiple Identity Manipulation in Collaborative Projects
Authors: Zaher Yamak, Julien Saunier, Laurent Vercouter
Venue: WWW
Year: 2016

Various techniques are used to manipulate users in OSN environments such as social spam, identity theft, spear phishing and Sybil attacks... In this article, we are interested in analyzing the behavior of multiple fake accounts that try to bypass the OSN regulation. In the context of social media manipulation detection, we focus on the special case of multiple Identity accounts (Sockpuppet) created on English Wikipedia (EnWiki). We set up a complete methodology spanning from the data extraction from EnWiki to the training and testing of our selected data using several machine learning algorithms.
Multimodal Content-Aware Image Thumbnailing
Authors: Kohei Yamamoto, Hayato Kobayashi, Yukihiro Tagami, Hideki Nakayama
Venue: WWW
Year: 2016

News article recommendation has the key problem of needing to eliminate the redundant information in a ranked list in order to provide more relevant information within a limited time and space. In this study, we tackle this problem by using image thumbnailing, which can be regarded as the summarization of news images. We propose a multimodal image thumbnailing method considering news text as well as images themselves. We evaluate this approach on a real data set based on news articles that appeared on Yahoo! JAPAN. Experimental results demonstrate the effectiveness of our proposed method.