OpenRaiser/Intern-Atlas huggingface dataset:
On Automating Basic Data Curation Tasks
Authors: Amin Beheshti, Alireza Tabebordbar, Boualem Benatallah, Reza Nouri
Venue: WWW
Year: 2017

Big data analytics is firmly recognized as a strategic priority for modern enterprises. At the heart of big data analytics lies the data curation process, consists of tasks that transform raw data (unstructured, semi-structured and structured data sources) into curated data, i.e. contextualized data and knowledge that is maintained and made available for use by end-users and applications. To achieve this, the data curation process may involve techniques and algorithms for extracting, classifying, linking, merging, enriching, sampling, and the summarization of data and knowledge. To facilitate the data curation process and enhance the productivity of researchers and developers, we identify and implement a set of basic data curation APIs and make them available as services to researchers and developers to assist them in transforming their raw data into curated data. The curation APIs enable developers to easily add features - such as extracting keyword, part of speech, and named entities such as Persons, Locations, Organizations, Companies, Products, Diseases, Drugs, etc.; providing synonyms and stems for extracted information items leveraging lexical knowledge bases for the English language such as WordNet; linking extracted entities to external knowledge bases such as Google Knowledge Graph and Wikidata; discovering similarity among the extracted information items, such as calculating similarity between string and numbers; classifying, sorting and categorizing data into various types, forms or any other distinct class; and indexing structured and unstructured data - into their data applications. These services can be accessed via a REST API, and the data is returned as a JSON file that can be integrated into data applications. The curation APIs are available as an open source project on GitHub.
SAVE-SD 2017: Third Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data
Authors: Alejandra N. González-Beltrán, Francesco Osborne, Silvio Peroni, Sahar Vahdati
Venue: WWW
Year: 2017

The third edition of the Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data (SAVE-SD 2017) is taking place in Perth, Australia on the 3rd of April 2017, co-located with the 26th International World Wide Web Conference. The main goal of the workshop is to provide a venue for researchers, publishers and other companies to engage in discussions about semantics, analytics and visualisations on scholarly data.
Integrating Modeling Languages and Web Logs for Enhanced User Behavior Analytics
Authors: Carlo Bernaschina, Marco Brambilla, Thanas Koka, Andrea Mauri, Eric Umuhoza
Venue: WWW
Year: 2017

While basic Web analytics tools are widespread and provide statistics about Web site navigation, no approaches exist for merging such statistics with information about the Web application structure, content and semantics. We demonstrate the advantages of combining Web application models with runtime navigation logs, at the purpose of deepening the understanding of users behaviour. We propose a model-driven approach that combines user interaction modeling (based on the IFML standard), full code generation of the designed application, user tracking at runtime through logging of runtime component execution and user activities, integration with page content details, generation of integrated schema-less data streams, and application of large-scale analytics and visualization tools for big data, by applying both traditional dataviz techniques and direct representation of statistics on visual models of the Web application.
Global Entity Ranking Across Multiple Languages
Authors: Prantik Bhattacharyya, Nemanja Spasojevic
Venue: WWW
Year: 2017

We present work on building a global long-tailed ranking of entities across multiple languages using Wikipedia and Freebase knowledge bases. We identify multiple features and build a model to rank entities using a ground-truth dataset of more than 10 thousand labels. The final system ranks 27 million entities with 75% precision and 48% F1 score. We provide performance evaluation and empirical evidence of the quality of ranking across languages, and open the final ranked lists for future research.
SESAME: European Statistics Explored via Semantic Alignment onto Wikipedia
Authors: Natalia Boldyrev, Marc Spaniol, Jannik Strötgen, Gerhard Weikum
Venue: WWW
Year: 2017

Authorities such as the European Commission have recognized the need to offer a unified access to the data gathered by a wide variety of providers, such as the European Statistical Organization (Eurostat) or the European Environment Agency. Its EU Open Data Portal serves as a gateway to numerical data, statistical reports, and visualization tools. While making the data available to the users from all member states and concentrating efforts on bridging the language gap, the portal still focuses on a primarily statistical perspective. That is, numerical data are explained with general terms, only. However, the related events, people, or organizations ``causing'' or being ``affected'' by the statistical observation remain concealed to the user.
Top-k Query Processing with Conditional Skips
Authors: Edward Bortnikov, David Carmel, Guy Golan-Gueta
Venue: WWW
Year: 2017

This work improves the efficiency of dynamic pruning algorithms by introducing a new posting iterator that can skip large parts of the matching documents during top-k query processing. Namely, the conditional-skip iterator jumps to a target document while skipping all matching documents preceding the target that cannot belong to the final result list. We experiment with two implementations of the new iterator, and show that integrating it into representative dynamic pruning algorithms such as MaxScore, WAND, and Block Max WAND (BMW), reduces the document scoring overhead, and eventually the query latency.
Friendly, Appealing or Both?: Characterising User Experience in Sponsored Search Landing Pages
Authors: Marc Bron, Miriam Redi, Mounia Lalmas, Fabrizio Silvestri, Huw Evans, Mahlon Chute
Venue: WWW
Year: 2017

Many of today's websites have recognised the importance of mobile friendly pages to keep users engaged and to provide a satisfying user experience. However, next to the experience provided by the sites themselves, advertisements, when clicked, present users with landing pages that are not necessarily mobile friendly. We explore what type of features are able to characterise the mobile friendliness of sponsored search ad landing pages. To have a complete understanding of the mobile ad experience in terms of layout and visual appearance, we also explore the notion of the ad page aesthetic appeal. We design and collect annotations for both dimensions on a large set of ads, and find that mobile friendliness and aesthetics represent different notions. We perform a comprehensive study of the effectiveness of over 120 features on the tasks of friendliness and aesthetics prediction. We find that next to general page size, HTML, and resource usage based features, several features based on the visual composition of landing pages are important to determine mobile friendliness and aesthetics. We demonstrate the additional benefit of these various types of features by comparing against the mobile friendliness guidelines provided by W3C. Finally, we use our models to determine the state of landing page mobile friendliness and aesthetics on a large sample of advertisements of a major internet company.
Coarse to Fine: Diffusing Categories in Wikipedia
Authors: Pengshan Cai, Yansong Feng, Yantao Jia, Yuanzhuo Wang, Xiaolong Jin, Xueqi Cheng
Venue: WWW
Year: 2017

Automatic taxonomy construction aims to build a categorization system without human efforts. Traditional textual pattern based methods extract hyponymy relation in raw texts. However, these methods usually yield low precision and recall. In this paper, we propose a method to automatically find diffusing attributes to a category from Wikipedia infoboxes. We use the diffusing attribute to diffuse a coarse-grained category into several fine-grained subcategories and generate a finer-grained taxonomy. Experiments show our method can find proper diffusing attributes to categories across various domains.
Describing Patterns and Disruptions in Large Scale Mobile App Usage Data
Authors: Steven Van Canneyt, Marc Bron, Andy Haines, Mounia Lalmas
Venue: WWW
Year: 2017

The advertising industry is seeking to use the unique data provided by the increasing usage of mobile devices and mobile applications (apps) to improve targeting and the experience with apps. As a consequence, understanding user behaviours with apps has gained increased interests from both academia and industry. In this paper we study user app engagement patterns and disruptions of those patterns in a data set unique in its scale and coverage of user activity. First, we provide a detailed account of temporal user activity patterns with apps and compare these to previous studies on app usage behavior. Then, in the second part, and the main contribution of this work, we take advantage of the scale and coverage of our sample and show how app usage behavior is disrupted through major political, social, and sports events.
Predicting the Popularity of Online Content with Group-specific Models
Authors: Qi Cao, Huawei Shen, Hao Gao, Jinhua Gao, Xueqi Cheng
Venue: WWW
Year: 2017

Predicting the popularity of online content is highly valuable in many applications and has been studied for several years. However, existing models either work in population level---all messages are assumed to follow similar popularity dynamics, lacking flexibility to capture the intrinsic complexity of popularity dynamics, or work in individual level---the popularity dynamics of messages are independent of each other, failing to leverage other messages to improve prediction accuracy. In this paper, we propose a divide and conquer framework for popularity prediction. We first divide messages into groups, anticipating each group of messages follow similar popularity dynamics, and then, we train a group-specific model for the messages of each group. Experiments demonstrate that group-specific models improve the population-level models by about 30% and outperform state-of-the-art individual-level model.
The Demographics of Mail Search and their Application to Query Suggestion
Authors: David Carmel, Liane Lewin-Eytan, Alex Libov, Yoelle Maarek, Ariel Raviv
Venue: WWW
Year: 2017

Web mail search is an emerging topic, which has not been the object of as many studies as traditional Web search. In particular, little is known about the characteristics of mail searchers and of the queries they issue. We study here the characteristics of Web mail searchers, and explore how demographic signals such as location, age, gender, and inferred income, influence their search behavior. We try to understand for instance, whether women exhibit different mail search patterns than men, or whether senior people formulate more precise queries than younger people. We compare our results, obtained from the analysis of a Yahoo Web mail search query log, to similar work conducted in Web and Twitter search. In addition, we demonstrate the value of the user's personal query log, as well as of the global query log and of the demographic signals, in a key search task: dynamic query auto-completion. We discuss how going beyond users' personal query logs (their search history) significantly improves the quality of suggestions, in spite of the fact that a user's mailbox is perceived as being highly personal. In particular, we note the striking value of demographic features for queries relating to companies/organizations, thus verifying our assumption that query completion benefits from leveraging queries issued by ``people like me". We believe that demographics and other such global features can be leveraged in other mail applications, and hope that this work is a first step in this direction.
Optimizing the Recency-Relevancy Trade-off in Online News Recommendations
Authors: Abhijnan Chakraborty, Saptarshi Ghosh, Niloy Ganguly, Krishna P. Gummadi
Venue: WWW
Year: 2017

Online news media sites are emerging as the primary source of news for a large number of users. The selection of 'front-page' stories on these media sites usually takes into consideration several crowdsourced popularity metrics, such as number of views or shares by the readers. In this work, we focus on automatically recommending front-page stories in such media websites. When recommending news stories, there are two basic metrics of interest - recency and relevancy. Ideally, recommender systems should recommend the most relevant stories soon after they are published. However, the relevancy of a story only becomes evident as the story ages, thereby creating a tension between recency and relevancy. A systematic analysis of popular recommendation strategies in use today reveals that they lead to poor trade-offs between recency and relevancy in practice. So, in this paper, we propose a new recommendation strategy (called Highest Future-Impact) which attempts to optimize on both the axes. To implement our proposed strategy in practice, we develop an optimization framework combining the predicted future-impact of the stories with the uncertainties in the predictions. Evaluations over three real-world news datasets show that our implementation achieves good performance trade-offs between recency and relevancy.
Leveraging Social Connections to Improve Peer Assessment in MOOCs
Authors: Hou Pong Chan, Irwin King
Venue: WWW
Year: 2017

With the advent of Massive Open Online Courses (MOOCs), students from all over the world can access to quality courses via a web browser. Due to their great convenience, a popular MOOC can easily attract tens of thousands of students to enroll. Hence, a challenging problem in MOOCs is to find an efficient way to grade a large scale of assignments. To address this problem, peer assessment was proposed to grade the assignments in a scalable way. In peer assessment, each student is asked to access a subset of his/her peers' assignments via a web interface, then all these peer grades are aggregated to predict a final grade for each submitted assignment. These peer grades are very noisy due to the fact that different students have different bias and reliability. Several probabilistic models were proposed to improve the accuracy of the predicted grades by explicitly modeling the bias and reliability of each student. However, existing methods assumed that all students are independent of each other while ignoring the social interactions among the students. In real life, students' grading bias are easily affected by their friends. For example, a student tends to have a tough grading standard if his/her friends are harsh graders. Following this intuition, we propose three probabilistic models for peer assessment by incorporating social connections to model the dependencies of bias among the students. Moreover, we evaluate our models in a new peer grading dataset, which is enhanced with the social information of users in the discussion forums of the MOOC platform. Experimental results show that our models improve the accuracy of the predicted grades by leveraging social connections of students.
Security Implications of Redirection Trail in Popular Websites Worldwide
Authors: Li Chang, Hsu-Chun Hsiao, Wei Jeng, Tiffany Hyun-Jin Kim, Wei-Hsi Lin
Venue: WWW
Year: 2017

URL redirection is a popular technique that automatically navigates users to an intended destination webpage with- out user awareness. However, such a seemingly advantageous feature may offer inadequate protection from security vulnerabilities unless every redirection is performed over HTTPS. Even worse, as long as the final redirection to a website is performed over HTTPS, the browser's URL bar indicates that the website is secure regardless of the security of prior redirections, which may provide users with a false sense of security. This paper reports a well-rounded investigation to analyze the wellness of URL redirection security. As an initial large-scale investigation, we screened the integrity and consistency of URL redirections for the Alexa top one million (1M) websites, and further examined 10,000 (10K) websites with their login features. Our results suggest that 1) the majority (83.3% in the 1M dataset and 78.6% in the 10K dataset) of redirection trails among web- sites that support only HTTPS are vulnerable to attacks, and 2) current incoherent practices (e.g., naked domains and www subdomains being redirected to different destinations with varying security levels) undermine the security guarantees provided by HTTPS and HSTS.
On-Demand Bot Detection and Archival System
Authors: Nikan Chavoshi, Hossein Hamooni, Abdullah Mueen
Venue: WWW
Year: 2017

Unusually high correlation in activities among users in social media is an indicator of bot behavior. We have developed a system, called DeBot, that identifies such bots in Twitter network. Our system reports and archives thousands of bot accounts every day. DeBot is an unsupervised method capable of detecting bots in a parameter-free fashion. In February 2017, DeBot has collected over 710K unique bots since August 2015. Since we are detecting and archiving Twitter bots on a daily basis, we have the ability to offer two different services based on our bot detection system. The first one is a bot archive API that makes it easy for researchers to query the DeBot's archive. This API can be used to answer various queries: Is a given Twitter account a bot? When was this bot active in the past? Which twitter accounts were detected as bots on a specific date? The second service that we offer is an on-demand bot detection platform which can detect bots that are related to a given topic or geographical location, and report them to the user in few hours. This paper explains all the details of the services we offer on top of the DeBot's bot detection engine.
Temporal Patterns in Bot Activities
Authors: Nikan Chavoshi, Hossein Hamooni, Abdullah Mueen
Venue: WWW
Year: 2017

Correlated or synchronized bots commonly exist in social media sites such as Twitter. Bots work towards gaining human followers, participating in campaigns, and engaging in unethical activities such as spamming and false click generation. In this paper, we perform temporal pattern mining on bot activities in Twitter. We discover motifs (repeating behavior), discords (anomalous behavior), joins, bursts and dynamic clusters in activities of Twitter bots, and explain the significance of these temporal patterns in gaining competitive advantage over humans. Our analysis identifies a small set of indicators that separates bots from humans with high precision.
A Selfie is Worth a Thousand Words: Mining Personal Patterns behind User Selfie-posting Behaviours
Authors: Tianlang Chen, Yuxiao Chen, Jiebo Luo
Venue: WWW
Year: 2017

Selfies have become increasingly fashionable in the social media era. People are willing to share their selfies in various social media platforms such as Facebook, Instagram and Flicker. The popularity of selfie have caught researchers' attention, especially psychologists. In computer vision and machine learning areas, little attention has been paid to this phenomenon as a valuable data source. In this paper, we focus on exploring the deeper personal patterns behind people's different kinds of selfie-posting behaviours. We develop this work based on a dataset of WeChat, one of the most extensively used instant messaging platform in China. In particular, we first propose an unsupervised approach to classify the images posted by users. Based on the classification result, we construct three types of user-level features that reflect user preference, activity and posting habit. Based on these features, for a series of selfie related tasks, we build classifiers that can accurately predict two sets of users with opposite selfie-posting behaviours. We have found that people's interest, activity and posting habit have a great influence on their selfie-posting behaviours. Taking selfie frequency as an example, the classification accuracy between selfie-posting addict and nonaddict can reach 89.36%. We also prove that using user's image information to predict these behaviours achieve better performance than using text information. More importantly, for each set of users with a specific selfie-posting behaviour, we extract and visualize significant personal patterns about them. In addition, to concisely construct the relation between personal pattern and selfie-posting behaviour, we cluster users and extract their high-level attributes, revealing the correlation between these attributes and users' selfie-posting behaviours. In the end, we demonstrate that users' selfie-posting behaviour, as a good predictor, could predict their different preferences toward these high-level attributes accurately.
Building and Analyzing a Global Co-Authorship Network Using Google Scholar Data
Authors: Yang Chen, Cong Ding, Jiyao Hu, Ruichuan Chen, Pan Hui, Xiaoming Fu
Venue: WWW
Year: 2017

By publishing papers together, academic authors can form a co-authorship network, modeling the collaboration among them. This paper presents a data-driven study by crawling and analyzing the vast majority of author profiles of Google Scholar. We make the following major contributions: (1) We present a demographic analysis and get an informative overview of the authors from different aspects, such as the distribution of countries, scientific labels, and academic titles. (2) Based on the publication lists of crawled authors, we build a global co-authorship network with 402.39K authors to study the collaboration among authors. With the aid of social network analysis (SNA), we observe several unique features of this network. (3) We explore the relationship between the co-authorship network and citation metrics. We find a strong correlation between PageRank and h-index.
Data + Intuition: A Hybrid Approach to Developing Product North Star Metrics
Authors: Albert C. Chen, Xin Fu
Venue: WWW
Year: 2017

"You make what you measure" is a familiar mantra at data-driven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product. Metrics fall into two general categories: direct count metrics such as total revenue and monthly active users, and nuanced quality metrics regarding value or other aspects of the user experience. Count metrics, when used exclusively as the North Star, might inform product decisions that harm user experience. Therefore, quality metrics play an important role in product development. We present a five-step framework for developing quality metrics using a combination of machine learning and product intuition. Machine learning ensures that the metric accurately captures user experience. Product intuition makes the metric interpretable and actionable. Through a case study of the Endorsements product at LinkedIn, we illustrate the danger of optimizing exclusively for count metrics, and showcase the successful application of our framework toward developing a quality metric. We show how the new quality metric has driven significant improvements toward creating a valuable, user-first product.