OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Careful Microblog Users for Spammer Detection
Authors: Hao Fu, Xing Xie, Yong Rui
Venue: WWW
Year: 2015

Microblogging websites, e.g. Twitter and Sina Weibo, have become a popular platform for socializing and sharing information in recent years. Spammers have also discovered this new opportunity to unfairly overpower normal users with unsolicited content, namely social spams. While it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users seeking for spammers on purpose is also observed. We regard this behavior as a useful information for spammer detection. In this paper, we approach the problem of spammer detection by leveraging the "carefulness" of users, which indicates how careful a user is when she is about to follow a potential spammer. We propose a framework to measure the carefulness, and develop a supervised learning algorithm to estimate it based on known spammers and legitimate users. We then illustrate how spammer detection can be improved in the aid of the proposed measure. Evaluation on a real dataset with millions of users and an online testing are performed on Sina Weibo. The results show that our approach indeed capture the carefulness, and it is effective to detect spammers. In addition, we find that the proposed measure is also beneficial for other applications, e.g. link prediction.
MobInsight: On Improving The Performance of Mobile Apps in Cellular Networks
Authors: Vijay Gabale, Dilip Krishnaswamy
Venue: WWW
Year: 2015

It is well-known that the performance of Web-browsing as well as mobile applications (or apps) suffers on today's cellular networks. In this work, we perform a systematic measurement study of more than 50 popular apps and 2 cellular networks, and discover that while cellular networks have predictable latency, it is the path between exit points of cellular networks (e.g., GGSN) and cloud-servers that degrades apps performance. High latency and unpredictability over this path affects browsing and activity completion times of apps, worsening the performance by several magnitudes. Furthermore, we find that as the number of apps on mobile devices increases, cellular networks in turn suffer due to large number of active connections, primarily used for push notifications, experiencing heavy signaling overhead in the network. Towards accelerating the performance of apps and improving their operational efficiency, we envision an easy to deploy operator-managed platform, and study two architectural optimizations that sit at vantage points inside cellular networks: virtual app-server (vApp) and network-assisted, virtual push-notification server (vPNS). vApps improve apps' browsing experience while vPNSs take the burden of carrying periodic message off cellular networks. Using trace-driven simulations, we find that vApps can improve activity completion times by more than 3-fold, whereas vPNS can reduce the signaling load by a factor of 6 in cellular networks and reduce energy consumption by a factor of 2 on mobile devices.
Make Hay While the Crowd Shines: Towards Efficient Crowdsourcing on the Web
Authors: Ujwal Gadiraju
Venue: WWW
Year: 2015

Within the scope of this PhD proposal, we set out to investigate two pivotal aspects that influence the effectiveness of crowdsourcing: (i) microtask design, and (ii) workers behavior. Leveraging the dynamics of tasks that are crowdsourced on the one hand, and accounting for the behavior of workers on the other hand, can help in designing tasks efficiently. To help understand the intricacies of microtasks, we identify the need for a taxonomy of typically crowdsourced tasks. Based on an extensive study of 1000 workers on CrowdFlower, we propose a two-level categorization scheme for tasks. We present insights into the task affinity of workers, effort exerted by workers to complete tasks of various types, and their satisfaction with the monetary incentives. We also analyze the prevalent behavior of trustworthy and untrustworthy workers. Next, we propose behavioral metrics that can be used to measure and counter malicious activity in crowdsourced tasks. Finally, we present guidelines for the effective design of crowdsourced surveys and set important precedents for future work.
You Will Get Mail!Predicting the Arrival of Future Email
Authors: Iftah Gamzu, Zohar Shay Karnin, Yoelle Maarek, David Wajc
Venue: WWW
Year: 2015

The majority of Web email is known to be generated by machines even when one excludes spam. Many machine-generated email messages such as invoices or travel itineraries are critical to users. Recent research studies establish that causality relations between certain types of machine-generated email messages exist and can be mined. These relations exhibit a link between a given message to a past message that gave rise to its creation. For example, a shipment notification message can often be linked to a past online purchase message. Instead of studying how an incoming message can be linked to the past, we propose here to focus on predicting future email arrival as implied by causality relations. Such a prediction method has several potential applications, ranging from improved ad targeting in up sell scenarios to reducing false positives in spam detection.
LDP4ROs: Managing Research Objects with the W3C Linked Data Platform
Authors: Daniel Garijo, Nandana Mihindukulasooriya, Óscar Corcho
Venue: WWW
Year: 2015

In this demo we present LDP4ROs, a prototype implementation that allows creating, browsing and updating Research Objects (ROs) and their contents using typical HTTP operations. This is achieved by aligning the RO model with the W3C Linked Data Platform (LDP).
Knowledge Obtention Combining Information Extraction Techniques with Linked Data
Authors: Ángel Luís Garrido, Pilar Blazquez, Maria G. Buey, Sergio Ilarri
Venue: WWW
Year: 2015

Today, we can find a vast amount of textual information stored in proprietary data stores. The experience of searching information in these systems could be improved in a remarkable manner if we combine these private data stores with the information supplied by the Internet, merging both data sources to get new knowledge. In this paper, we propose an architecture with the goal of automatically obtaining knowledge about entities (e.g., persons, places, organizations, etc.) from a set of natural text documents, building smart data from raw data. We have tested the system in the context of the news archive of a real Media Group.
Conference Live: Accessible and Sociable Conference Semantic Data
Authors: Anna Lisa Gentile, Maribel Acosta, Luca Costabello, Andrea Giovanni Nuzzolese, Valentina Presutti, Diego Reforgiato Recupero
Venue: WWW
Year: 2015

In this paper we describe Conference Live, a semantic Web application to browse conference data. Conference Live is a Web and mobile application based on conference data from the Semantic Web Dog Food server, which provides facilities to browse papers and authors at a specific conference. Available data for the specific conference is enriched with social features (e.g. integrated Twitter accounts of paper authors), scheduling features (calendar information are attached for paper presentations and social events), the possibility to check and add feedback to each paper and to vote for papers, if the conference includes sessions where participants can vote, as it is popular e.g. for poster sessions. As use case we report on the usage of the application at the Extended Semantic Web Conference (ESWC) in May 2014.
Rethinking Security of Web-Based System Applications
Authors: Martin Georgiev, Suman Jana, Vitaly Shmatikov
Venue: WWW
Year: 2015

Many modern desktop and mobile platforms, including Ubuntu, Google Chrome, Windows, and Firefox OS, support so called Web-based system applications that run outside the Web browser and enjoy direct access to native objects such as files, camera, and geolocation. We show that the access-control models of these platforms are (a) incompatible and (b) prone to unintended delegation of native-access rights: when applications request native access for their own code, they unintentionally enable it for untrusted third-party code, too. This enables malicious ads and other third-party content to steal users' OAuth authentication credentials, access camera on their devices, etc.
Identification of Web Spam through Clustering of Website Structures
Authors: Filippo Geraci
Venue: WWW
Year: 2015

Spam websites are domains whose owners are not interested in using them as gates for their activities but they are parked to be sold in the secondary market of web domains. To transform the costs of the annual registration fees in an opportunity of revenues, spam websites most often host a large amount of ads in the hope that someone who lands on the site by chance clicks on some ads. Since parking has become a widespread activity, a large number of specialized companies have come out and made parking a straightforward task that simply requires to set the domain's name servers appropriately.
Cardinal Contests
Authors: Arpita Ghosh, Patrick Hummel
Venue: WWW
Year: 2015

Contests are widely used as a means for effort elicitation in settings ranging from government R&D contests to online crowdsourcing contests on platforms such as Kaggle, Innocentive, or TopCoder. Such rank-order mechanisms---where agents' rewards depend only on the relative ranking of their submissions' qualities---are natural mechanisms for incentivizing effort when it is easier to obtain ordinal, rather than cardinal, information about agents' outputs, or where absolute measures of quality are unverifiable. An increasing number of online contests, however, rank entries according to some numerical evaluation of their absolute quality---for instance, the performance of an algorithm on a test dataset, or the performance of an intervention in a randomized trial. Can the contest designer incentivize higher effort by making the rewards in an ordinal rank-order mechanism contingent on such cardinal information? We model and analyze cardinal contests, where a principal running a rank-order tournament has access to an absolute measure of the qualities of agents' submissions in addition to their relative rankings, and ask how modifying the rank-order tournament to incorporate cardinal information can improve incentives for effort. Our main result is that a simple threshold mechanism---a mechanism that awards the prize for a rank if and only if the absolute quality of the agent at that rank exceeds a certain threshold---is optimal amongst all mixed cardinal-ordinal mechanisms where the fraction of the jth prize awarded to the jth-ranked agent is any arbitrary non-decreasing function of her submission's quality. Further, the optimal threshold mechanism uses exactly the same threshold for each rank. We study what contest parameters determine the extent of the benefit from incorporating such cardinal information into an ordinal rank-order contest, and investigate the extent of improvement in equilibrium effort via numerical simulations.
Visual-Based Classification of Figures from Scientific Literature
Authors: Theodoros Giannakopoulos, Ioannis Foufoulas, Elefterios Stamatogiannakis, Harry Dimitropoulos, Natalia Manola, Yannis E. Ioannidis
Venue: WWW
Year: 2015

Authors of scientific publications and books use images to present a wide spectrum of information. Despite the richness of the visual content of scientific publications the figures are usually not taken into consideration in the context of text mining methodologies towards the automatic indexing and retrieval of scientific corpora. In this work, we present a system for automatic categorization of figures from scientific literature to a set of predefined classes. We have employed a wide range of visual features that achieve high discrimination ability between the adopted classes. A real-world dataset has been compiled and annotated in order to train and evaluate the proposed method using three different classification schemata.
Period Assertion as Nanopublication: The PeriodO Period Gazetteer
Authors: Patrick Golden, Ryan Shaw
Venue: WWW
Year: 2015

The PeriodO period gazetteer collects definitions of time periods made by archaeologists and other historical scholars. In constructing the gazetteer, we sought to make period definitions parsable and comparable by computers while also retaining the broader scholarly context in which they were conceived. Our approach resulted in a dataset of period definitions and their provenances that resemble what data scientists working in the e-science domain have dubbed "nanopublications." In this paper we describe the origin and goals of nanopublications, provide an overview of the design and implementation of a database of period definitions, and highlight the similarities and differences between the two.
Pivotality of Nodes in Reachability Problems Using Avoidance and Transit Hitting Time Metrics
Authors: Golshan Golnari, Yanhua Li, Zhi-Li Zhang
Venue: WWW
Year: 2015

Reachability is crucial to many network operations in various complex networks. More often than not, however, it is not sufficient simply to know whether a source node s can reach a target node t in the network. Additional information associated with reachability such as how long or how many possible ways node s may take to reach node t. In this paper we analyze another piece of important information associated with reachability -- which we call pivotality. Pivotality captures how pivotal a role that a node k or a subset of nodes S may play in the reachability from node s to node t in a given network. We propose two important metrics, the avoidance and transit hitting times, which extend and generalize the classical notion of hitting times. We show these metrics can be computed from the fundamental matrices associated with the appropriately defined random walk transition probability matrices and prove that the classical hitting time from a source to a target can be decomposed into the avoidance and transit hitting times with respect to any third node. Through simulated and real-world network examples, we demonstrate that these metrics provide a powerful ranking tool for the nodes based on their pivotality in the reachability.
Supporting the Development of Smart Cities using a Use Case Methodology
Authors: Marion Gottschalk, Mathias Uslar
Venue: WWW
Year: 2015

Urbanization grows steadily, i.e. more humans live at one place and rural areas are more unpopular. Urbanization faces challenges for city planning and development. Cities have to deal with large crowds, high energy consumption, large quantities of garbage etc. Thus, smart cities have to meet many requirements of different areas. Hence, realizing smart cities can be supported by linking different smart areas, such as smart girds and smart homes, to one large area. The linking is done by information and communication technologies, which are supported through a clear definition of functionalities and interfaces. Smart cities and further smart areas are under development, so, it is difficult to depict an overview on their functionalities, yet. Therefore, the two approaches, use case methodology and integration profiles, are introduced in this work, which are also realized by a web-based application.
Microscopic Description and Prediction of Information Diffusion in Social Media: Quantifying the Impact of Topical Interests
Authors: Przemyslaw A. Grabowicz, Niloy Ganguly, Krishna P. Gummadi
Venue: WWW
Year: 2015

A number of recent studies of information diffusion in social media, both empirical and theoretical, have been inspired by viral propagation models derived from epidemiology. These studies model propagation of memes, i.e., pieces of information, between users in a social network similarly to the way diseases spread in human society. Naturally, many of these studies emphasize social exposure, i.e., the number of friends or acquaintances of a user that have exposed a meme to her, as the primary metric for understanding, predicting, and controlling information diffusion. Intuitively, one would expect a meme to spread in a social network selectively, i.e., amongst the people who are interested in the meme. However, the importance of the alignment between the topicality of a meme and the topical interests of the potential adopters and influencers in the network has been less explored in the literature. In this paper, we quantify the impact of the topical alignment between memes and users on their adoption. Our analysis, using empirical data about two different types of memes, i.e., hashtags and URLs spreading through the Twitter social media platform, finds that topical alignment between memes and users is as crucial as the social exposure in understanding and predicting meme adoptions. Our results emphasize the need to look beyond social network-based viral propagation models and develop microscopic models of information diffusion that account for interests of users and topicality of information.
queryCategorizr: A Large-Scale Semi-Supervised System for Categorization of Web Search Queries
Authors: Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Narayan Bhamidipati, Jordan Hawker, Caleb Johnson
Venue: WWW
Year: 2015

Understanding interests expressed through user's search query is a task of critical importance for many internet applications. To help identify user interests, web engines commonly utilize classification of queries into one or more pre-defined interest categories. However, majority of the queries are noisy short texts, making accurate classification a challenging task. In this demonstration, we present queryCategorizr, a novel semi-supervised learning system that embeds queries into low-dimensional vector space using a neural language model applied on search log sessions, and classifies them into general interest categories while relying on a small set of labeled queries. Empirical results on large-scale data show that queryCategorizr outperforms the current state-of-the-art approaches. In addition, we describe a Graphical User Interface (GUI) that allows users to query the system and explore classification results in an interactive manner.
Increasing the Productivity of Scholarship: The Case for Knowledge Graphs
Authors: Paul Groth
Venue: WWW
Year: 2015

Over the past several years, we have seen an explosion in the number of tools and services that enable scholars to improve their personal productivity whether it is socially enabled reference managers or cloud-hosted experimental environments. However, we have yet to see a step-change in the productivity of the system of scholarship as a whole. While there are certainly broader social reasons for this, in this talk I argue that we are just now at a technical position to create radical change in how scholarship is performed. Specifically, I will discuss how recent advances in machine reading, developments in open data and explicit social networks, can be used to create scholarly knowledge graphs. These graphs can connect the underlying intellectual corpus with ongoing discourse allowing the development of algorithms that hypothesize, filter and reflect alongside humans.
Learning Temporal Tagging Behaviour
Authors: Toni Grütze, Gary Yao, Ralf Krestel
Venue: WWW
Year: 2015

Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today's social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags.
Network A/B Testing: From Sampling to Estimation
Authors: Huan Gui, Ya Xu, Anmol Bhasin, Jiawei Han
Venue: WWW
Year: 2015

A/B testing, also known as bucket testing, split testing, or controlled experiment, is a standard way to evaluate user engagement or satisfaction from a new service, feature, or product. It is widely used in online websites, including social network sites such as Facebook, LinkedIn, and Twitter to make data-driven decisions. The goal of A/B testing is to estimate the treatment effect of a new change, which becomes intricate when users are interacting, i.e., the treatment effect of a user may spill over to other users via underlying social connections.When conducting these online controlled experiments, it is a common practice to make the Stable Unit Treatment Value Assumption (SUTVA) that each individual's response is affected by their own treatment only. Though this assumption simplifies the estimation of treatment effect, it does not hold when network interference is present, and may even lead to wrong conclusion.
Mining Scholarly Communication and Interaction on the Social Web
Authors: Asmelash Teka Hadgu
Venue: WWW
Year: 2015

The explosion of Web 2.0 platforms including social networking sites such as Twitter, blogs and wikis affects all web users: scholars included. As a result, there is a need for a comprehensive approach to gain a broader understanding and timely signals of scientific communication as well as how researchers interact on the social web. Most current work in this area deals with either a low number of researchers and heavily relies on manual annotation or large-scale analysis without deep understanding of the underlying researcher population. In this proposal, we present a holistic approach to solve these problems. This research proposes novel methods to collect, filter, analyze and make sense of scholars and scholarly communication by integrating heterogeneous data sources from fast social media streams as well as the academic web. Applying reproducible research, contributing applications and data sets, the thesis proposal strives to add value by mining the social web for social good.
Fast and Accurate Maximum Inner Product Recommendations on Map-Reduce
Authors: Rob Hall, Josh Attenberg
Venue: WWW
Year: 2015

Personalization has become a predominant theme in online advertising; the internet allows advertisers to target only those users with the greatest chances of engagement, maximizing the probability of success and user happiness. However, a na{\"i}ve approach to matching users with their most suitable content scales proportionally to the product of the cardinalities of the user and content sets. For advertisers with large portfolios, this quickly becomes intractable. In this work, we address this more general {\em top-$k$ personalization} problem, giving a scalable method to produce recommendations based on personalization models where the affinity between a user and an item is captured by an inner product (i.e., most matrix factorization models). We first transform the problem into finding the $k$-nearest neighbors among the items for each user, then approximate the solution via a method which is particularly suited for use on a map-reduce cluster. We empirically show that our method is between 1 and 2 orders of magnitude faster than previous work, while maintaining excellent approximation quality. Additionally, we provide an open-source implementation of our proposed method, this implementation is used in production at Etsy for a number of large scale personalization systems, and is the same code as used in the experiments below.