OpenRaiser/Intern-Atlas huggingface dataset:
MapWatch: Detecting and Monitoring International Border Personalization on Online Maps
Authors: Gary Soeller, Karrie Karahalios, Christian Sandvig, Christo Wilson
Venue: WWW
Year: 2016

Maps have long played a crucial role in enabling people to conceptualize and navigate the world around them. However, maps also encode the world-views of their creators. Disputed international borders are one example of this: governments may mandate that cartographers produce maps that conform to their view of a territorial dispute. Today, online maps maintained by private corporations have become the norm. However, these new maps are still subject to old debates. Companies like Google and Bing resolve these disputes by localizing their maps to meet government requirements and user preferences, i.e., users in different locations are shown maps with different international boundaries. We argue that this non-transparent personalization of maps may exacerbate nationalistic disputes by promoting divergent views of geopolitical realities.
PerSentiment: A Personalized Sentiment Classification System for Microblog Users
Authors: Kaisong Song, Ling Chen, Wei Gao, Shi Feng, Daling Wang, Chengqi Zhang
Venue: WWW
Year: 2016

Microblogging services are playing increasingly important roles in our daily life today. It is useful for microblog users to instantly understand the sentiment of a large number of microblogs posted by their friends and make appropriate response. Despite considerable progress on microblog sentiment classification, most of the existing works ignore the influence of personal distinctions of different microblog users on the sentiments they convey, and none of them has provided real-world personalized sentiment classification systems. Considering personal distinctions in sentiment analysis is natural and necessary as different people have different language habits, personal characters, opinion bias and so on. In this demonstration, we present a live system based on Twitter called PerSentiment, an individuality-dependent sentiment classification system which makes the first attempt to analyze the personalized sentiment of recent tweets and retweets posted by the authenticated user and the users he/she follows. Our system consists of four steps, i.e., requesting tweets via Twitter API, preprocessing collected tweets for extracting features, building personalized sentiment classifier based on a novel and extensible Latent Factor Model (LFM) trained on emoticon-tagged tweets, and finally visualizing the sentiment of friends' tweets to provide a guide for better sentiment understanding.
Mining Large Dense Subgraphs
Authors: Ajitesh Srivastava, Charalampos Chelmis, Viktor K. Prasanna
Venue: WWW
Year: 2016

Several applications including community detection in social networks and discovering correlated genes involve finding large subgraphs of high density. We propose the problem of finding the largest subgraph of a given density. The problem is a generalization of the Max-Clique problem which seeks the largest subgraph that has an edge density of 1. We define an objective function and prove that its optimization results in the largest graph of given density. We propose an algorithm that finds the subgraph by running multiple local search heuristics with random restarts. For massive graphs, where running the algorithm directly may be intractable, we use a sampling technique that reduces the graph to a smaller one which is likely to contain large dense subgraphs. We evaluate our algorithm on multiple real life and synthetic datasets. Our experiments show that our algorithm performs as well as the state-of-the-art for finding large subgraphs of high density, while providing density guarantees.
Ubiquitous Learning and Digital Literacy Practices Connecting Teacher and Learner
Authors: Andreja Istenic Starcic, Ziga Turk
Venue: WWW
Year: 2016

In this paper, we discuss digital literacy and preservice teacher education and reflect on the current state of web-based education and development of digital literacy in teacher education in Slovenia. The literacy context is discussed in the context of the Educational Technology course which is delivered in teacher education. The aim of this course for preservice preprimary and primary classroom teachers is to develop student teachers' digital literacy and prepare them for the efficient integration of ICT into their teaching. This will in turn influence learners' digital literacy and competence for active engagement in the emerging culture of participation. The paper discusses web-based teaching methodology with a focus on instructional design, learning resources and high-order learning outcomes. The affordance of mobile technology fosters ubiquitous learning which has been integrated into the teacher education curriculum. The notion of tools in learning and literacies is discussed in the context of the transition from traditional written culture to digital culture. In integrating mobile learning, three important dimensions converge, underlining the development of digital literacy: the technology dimension of wireless mobile providing instant access, the social dimension and the learning behaviours dimension. A survey was conducted to examine undergraduate student-teachers' attitudes on the application of ubiquitous education and the development of digital literacy through the integration of mobile learning. The results indicate that student-teachers have developed competences in a variety of mobile learning and teaching activities They believe that mobile technology increases connection between learner and teacher but are neutral about the integration of children's social practices from their free time to school environment.
No Honor Among Thieves: A Large-Scale Analysis of Malicious Web Shells
Authors: Oleksii Starov, Johannes Dahse, Syed Sharique Ahmad, Thorsten Holz, Nick Nikiforakis
Venue: WWW
Year: 2016

Web shells are malicious scripts that attackers upload to a compromised web server in order to remotely execute arbitrary commands, maintain their access, and elevate their privileges. Despite their high prevalence in practice and heavy involvement in security breaches, web shells have never been the direct subject of any study. In contrast, web shells have been treated as malicious blackboxes that need to be detected and removed, rather than malicious pieces of software that need to be analyzed and, in detail, understood. In this paper, we report on the first comprehensive study of web shells. By utilizing different static and dynamic analysis methods, we discover and quantify the visible and invisible features offered by popular malicious shells, and we discuss how attackers can take advantage of these features. For visible features, we find the presence of password bruteforcers, SQL database clients, portscanners, and checks for the presence of security software installed on the compromised server. In terms of invisible features, we find that about half of the analyzed shells contain an authentication mechanism, but this mechanism can be bypassed in a third of the cases. Furthermore, we find that about a third of the analyzed shells perform homephoning, i.e., the shells, upon execution, surreptitiously communicate to various third parties with the intent of revealing the location of new shell installations. By setting up honeypots, we quantify the number of third-party attackers benefiting from shell installations and show how an attacker, by merely registering the appropriate domains, can completely take over all installations of specific vulnerable shells.
DynamoGraph: A Distributed System for Large-scale, Temporal Graph Processing, its Implementation and First Observations
Authors: Matthias Steinbauer, Gabriele Anderst-Kotsis
Venue: WWW
Year: 2016

Graph models have a long standing history as models for real world structures and processes. In recent research two important dimensions of graphs are described of particular importance. (1) Temporal aspects of graphs cannot be neglected for many current application scenarios such as social network analysis or the analysis of the global web graph. (2) The mentioned graph structures have grown to very large sizes such that traditional methodologies no longer hold. In this work a distributed computing framework designed for storing and processing of large-scale temporal graphs is presented. For this system a reference implementation in Java was created. In this paper first insight on the implementation and observations in using the system are discussed.
Wikipedia Tools for Google Spreadsheets
Authors: Thomas Steiner
Venue: WWW
Year: 2016

In this paper, we introduce the Wikipedia Tools for Google Spreadsheets. Google Spreadsheets is part of a free, Web-based software office suite offered by Google within its Google Docs service. It allows users to create and edit spreadsheets online, while collaborating with other users in realtime. Wikipedia is a free-access, free-content Internet encyclopedia, whose content and data is available, among other means, through an API. With the Wikipedia Tools for Google Spreadsheets, we have created a toolkit that facilitates working with Wikipedia data from within a spreadsheet context. We make these tools available as open-source on GitHub [https://github.com/tomayac/wikipedia-tools-for-google-spreadsheets], released under the permissive Apache 2.0 license.
The Effect of Recommendations on Network Structure
Authors: Jessica Su, Aneesh Sharma, Sharad Goel
Venue: WWW
Year: 2016

Online social networks regularly offer users personalized, algorithmic suggestions of whom to connect to. Here we examine the aggregate effects of such recommendations on network structure, focusing on whether these recommendations increase the popularity of niche users or, conversely, those who are already popular. We investigate this issue by empirically and theoretically analyzing abrupt changes in Twitter's network structure around the mid-2010 introduction of its "Who to Follow" feature. We find that users across the popularity spectrum benefitted from the recommendations; however, the most popular users profited substantially more than average. We trace this "rich get richer" phenomenon to three intertwined factors. First, as is typical of network recommenders, the system relies on a "friend-of-friend"-style algorithm, which we show generally results in users being recommended proportional to their degree. Second, we find that the baseline growth rate of users is sublinear in degree. This mismatch between the recommender and the natural network dynamics thus alters the structural evolution of the network. Finally, we find that people are much more likely to respond positively to recommendations for popular users---perhaps because of their greater name recognition---further amplifying the cumulative advantage of well-known individuals.
Collaborative Nowcasting for Contextual Recommendation
Authors: Yu Sun, Nicholas Jing Yuan, Xing Xie, Kieran McDonald, Rui Zhang
Venue: WWW
Year: 2016

Mobile digital assistants such as Microsoft Cortana and Google Now currently offer appealing proactive experiences to users, which aim to deliver the right information at the right time. To achieve this goal, it is crucial to precisely predict users' real-time intent. Intent is closely related to context, which includes not only the spatial-temporal information but also users' current activities that can be sensed by mobile devices. The relationship between intent and context is highly dynamic and exhibits chaotic sequential correlation. The context itself is often sparse and heterogeneous. The dynamics and co-movement among contextual signals are also elusive and complicated. Traditional recommendation models cannot directly apply to proactive experiences because they fail to tackle the above challenges. Inspired by the nowcasting practice in meteorology and macroeconomics, we propose an innovative collaborative nowcasting model to effectively resolve these challenges. The proposed model successfully addresses sparsity and heterogeneity of contextual signals. It also effectively models the convoluted correlation within contextual signals and between context and intent. Specifically, the model first extracts collaborative latent factors, which summarize shared temporal structural patterns in contextual signals, and then exploits the collaborative Kalman Filter to generate serially correlated personalized latent factors, which are utilized to monitor each user's real-time intent. Extensive experiments with real-world data sets from a commercial digital assistant demonstrate the effectiveness of the collaborative nowcasting model. The studied problem and model provide inspiring implications for new paradigms of recommendations on mobile intelligent devices.
Assessing the Quality of Wikipedia Editors through Crowdsourcing
Authors: Yu Suzuki, Satoshi Nakamura
Venue: WWW
Year: 2016

In this paper, we propose a method for assessing the quality of Wikipedia editors. By effectively determining whether the text meaning persists over time, we can determine the actual contribution by editors. This is used in this paper to detect vandal. However, the meaning of text does not always change if a term in the text is added or removed. Therefore, we cannot capture the changes of text meaning automatically, so we cannot detect whether the meaning of text survives or not. To solve this problem, we use crowdsourcing to manually detect changes of text meaning. In our experiment, we confirmed that our proposed method improves the accuracy of detecting vandals by about 5%.
Towards Embedded Markup of Learning Resources on the Web: An Initial Quantitative Analysis of LRMI Terms Usage
Authors: Davide Taibi, Stefan Dietze
Venue: WWW
Year: 2016

Embedded markup of Web pages have emerged as a significant source of structured data on the Web. In this context, the LRMI initiative has provided a set of vocabulary terms, now part of the schema.org vocabulary, to enable the markup of resources of educational value. In this paper we present a preliminary analysis of the use of LRMI terms on the Web by assessing LRMI-based statements extracted from the Web Data Commons dataset.
AceMap: A Novel Approach towards Displaying Relationship among Academic Literatures
Authors: Zhaowei Tan, Changfeng Liu, Yuning Mao, Yunqi Guo, Jiaming Shen, Xinbing Wang
Venue: WWW
Year: 2016

A large number of papers are being published every year, which makes it difficult for researchers to grasp the relationship among the scientific literatures and the big picture of academic fields. The new challenges have thus been raised, such as analyzing the complicated citation and author network, mining valuable scientific knowledge, and visualizing big scholarly data. The existing academic systems, such as Google Scholar and DBLP have mainly adopted text-based methods, while some other systems make attempts to better navigate the literatures, for example, AMiner and Science Navigation Map. Although these systems show improvements, they fail to present the academic data in a holistic way, and also have limited functions. Therefore, we need to develop new tools which can realize more modules and further explore the academic literatures.
AMiner: Mining Deep Knowledge from Big Scholar Data
Authors: Jie Tang
Venue: WWW
Year: 2016

AMiner is the second generation of the ArnetMiner system. We focus on developing author-centric analytic and mining tools for gaining a deep understanding of the large and heterogeneous networks formed by authors, papers, venues, and knowledge concepts. One fundamental goal is how to extract and integrate semantics from different sources. We have developed algorithms to automatically extract researchers' profiles from the Web and re- solve the name ambiguity problem, and connect different professional networks. We also developed methodologies to incorporate knowledge from the Wikipedia and other sources into the system to bridge the gap between network science and the web mining research. In this talk, I will focus on answering two fundamental questions for author-centric network analysis: who is who? and who are similar to each other? The system has been in operation since 2006 and has collected more than 100,000,000 author profiles, 100,000,000 publication papers, and 7,800,000 knowledge concepts. It has been widely used for collaboration recommendation, similarity analysis, and community evolution.
Visualizing Large-scale and High-dimensional Data
Authors: Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei
Venue: WWW
Year: 2016

We study the problem of visualizing large-scale and high-dimensional data in a low-dimensional (typically 2D or 3D) space. Much success has been reported recently by techniques that first compute a similarity structure of the data points and then project them into a low-dimensional space with the structure preserved. These two steps suffer from considerable computational costs, preventing the state-of-the-art methods such as the t-SNE from scaling to large-scale and high-dimensional data (e.g., millions of data points and hundreds of dimensions). We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space. Comparing to t-SNE, LargeVis significantly reduces the computational cost of the graph construction step and employs a principled probabilistic model for the visualization step, the objective of which can be effectively optimized through asynchronous stochastic gradient descent with a linear time complexity. The whole procedure thus easily scales to millions of high-dimensional data points. Experimental results on real-world data sets demonstrate that the LargeVis outperforms the state-of-the-art methods in both efficiency and effectiveness. The hyper-parameters of LargeVis are also much more stable over different data sets.
A Simple Tags Categorization Framework Using Spatial Coverage to Discover Geospatial Semantics
Authors: Camille Tardy, Laurent Moccozet, Gilles Falquet
Venue: WWW
Year: 2016

There exist many popular crowdsourcing and social services (Volunteered Geographic Information (VGI)) to share information and documents such as Flickr, Foursquare, Twitter , Facebook, etc. They all use metadata, folksonomy and more importantly a geographic axis with GPS coordinates and/or geographic tags. Using this available folksonomy in VGI services we propose a logical approach to highlight and possibly discover the characteristics of geographic places. The approach is based on the notion of spatial coverage and a model of tags categorization and on their semantic identification, using semantic services such as GeoNames, OpenStreetMap or WordNet. We illustrate our model with Flickr to retrieve the characteristics (function, usage?) of places even if those places have a small number of related photos. Those found characteristics allow tag disambiguation and can be use to complete the semantic gap on places and POIs such as the function of buildings, which can exist in geographic services.
HyLAR+: Improving Hybrid Location-Agnostic Reasoning with Incremental Rule-based Update
Authors: Mehdi Terdjimi, Lionel Médini, Michael Mrissa
Venue: WWW
Year: 2016

Web applications that rely on datasets of limited sizes to handle small but frequent updates and numerous queries have no simple way to define where data should be stored and processed. We propose a reasoning framework that can be integrated in Web applications and is able to perform the same reasoning tasks on both client or server sides. This framework embeds a rule-based reasoning engine that uses an algorithm relying on both incremental reasoning and named graphs. We evaluate the performance of our approach and compare the effects of incremental reasoning and named graphs in different experimental conditions. Results show that our reasoner can significantly reduce response times to INSERT and DELETE queries. During the demo we will exhibit how it can be used to perform reasoning tasks based on client-generated information and improve Web applications with location-agnostic reasoning.
Finding Structure in Wikipedia Edit Activity: An Information Cascade Approach
Authors: Ramine Tinati, Markus Luczak-Rösch, Wendy Hall
Venue: WWW
Year: 2016

This paper documents a study of the real-time Wikipedia edit stream containing over 6 million edits on 1.5 million English Wikipedia articles, during 2015. We focus on answering questions related to identification and use of information cascades between Wikipedia articles, based on author editing activity. Our findings show that by constructing information cascades between Wikipedia articles using editing activity, we are able to construct an alternative linking structure in comparison to the embedded links within a Wikipedia page. This alternative article hyperlink structure was found to be relevant in topic, and timely in relation to external global events (e.g., political activity). Based on our analysis, we contextualise the findings against areas of interest such as events detection, vandalism, edit wars, and editing behaviour.
Real-time Tweet Classification in Disaster Situation
Authors: Fujio Toriumi, Seigo Baba
Venue: WWW
Year: 2016

During a disaster, appropriate information must be collected quickly. For example, residents along the coast require information about tsunamis and those who have lost their houses need information about shelters. Twitter can attract more attention than other forms of mass media under these circumstances because it can quickly provide such information. Since Twitter has an enormous amount of tweets, they must be classified to provide users with the information they need. Previous works on extracting information from Twitter focused on the text data of tweets. However, in some cases, text mining has difficulty extracting information. For example, it might be difficult for text mining to group tweets with URLs. On the other hand, by assuming that users who retweet the same tweet are interested in the same topic, we can classify tweets that are required by users with similar interests based on retweets. Thus, we employ the tweet classification method that focuses on retweets. In this paper, we demonstrated that our method works quickly in disaster situations and that it can quickly classify the required information based on the needs in disaster situations and is helpful for collecting information under them.
A Query-oriented Approach for Relevance in Citation Networks
Authors: Luam C. Totti, Prasenjit Mitra, Mourad Ouzzani, Mohammed J. Zaki
Venue: WWW
Year: 2016

Finding a relevant set of publications for a given topic of interest is a challenging problem. We propose a two-stage query-dependent approach for retrieving relevant papers given a keyword-based query. In the first stage, we utilize content similarity to select an initial seed set of publications; we then augment them by citation links weighted with information such as citation context relevance and age-based attenuation. In the second stage, we construct a multi-layer graph that expands the publications subgraph by including links to the authors, venues, and keywords. This allows us to return recommendations that are both highly authoritative, and also textually related to the query. We show that our staged approach gives superior results on three different benchmark query sets.