OpenRaiser/Intern-Atlas huggingface dataset:
Empirical Malware Research through Observation of System Behaviour
Authors: Stefan Marschalek, Manfred Kaiser, Robert Luh, Sebastian Schrittwieser
Venue: WWW
Year: 2016

Behavioural analysis has become an important method of today's malware research. Malicious software is executed inside a controlled environment where its runtime behaviour can be studied. Recently, we proposed the concept of not only observing individual executables but a computer system as a whole. The basic idea is to identify malware by detecting anomalies in the way a system behaves. In this paper we discuss our methodology for empirical malware research and highlight its strengths and limitations. Furthermore, we explain the challenges we faced during our research and describe our lessons learned.
Going Meta: Norm Formation and Enactment on the Stack Exchange Network
Authors: Paul Matthews
Venue: WWW
Year: 2016

This paper presents research into the way norms are developed, expressed and enforced on sites that are part of the Stack Exchange (SE) social question-answering network. This network has a number of topical knowledge exchange communities using similar underlying software, enabling a focus on variation in social design. SE also separates community-related discussion from topic-specific content through the use of its "Meta" sub-sites. These were analysed together with their main sites for variation in the development and enforcement of norms. Norms expressed through explicit community policies seem rather less important than those embodied in busy discussion threads on the Meta sites. While Meta participation was fairly uniform across communities, different emphasis on scope and quality led to variation in Meta discussion and the way that norms were enacted through question closures. The social distribution of moderation work was also uneven between sites, with some sites having a few highly active moderators involved in question closure. The level of closures across the sites studied did not seem to significantly discourage participation. Indeed, modelling the effect of closures on quality and engagement indicated that low levels of closure enable "legitimate peripheral participation", the process by which newcomers can become inducted and make contributions of increasing quality over time.
TLScompare: Crowdsourcing Rules for HTTPS Everywhere
Authors: Wilfried Mayer, Martin Schmiedecker
Venue: WWW
Year: 2016

For billions of users, today's Internet has become a critical infrastructure for information retrieval, social interaction and online commerce. However, in recent years research has shown that mechanisms to increase security and privacy like HTTPS are seldomly employed by default. With the exception of some notable key players like Google or Facebook, the transition to protecting not only sensitive information flows but all communication content using TLS is still in the early stages. While non-significant portion of the web can be reached securely using an open-source browser extension called HTTPS Everywhere by the EFF, the rules fueling it are so far manually created and maintained by a small set of people.
The Spread of Misinformation in Social Media
Authors: Filippo Menczer
Venue: WWW
Year: 2016

As social media become major channels for the diffusion of news and information, they are also increasingly attractive and targeted for abuse and manipulation. This talk overviews ongoing network analytics, data mining, and modeling efforts to understand the spread of misinformation online and offline. I present machine learning methods to detect astroturf and social bots, and outline initial steps toward computational fact checking, as well as theoretical models to study how truthful and truthy facts compete for our collective attention. These efforts will be framed by a case study in which, ironically, our own research became the target of a coordinated disinformation campaign.
TrackMeOrNot: Enabling Flexible Control on Web Tracking
Authors: Wei Meng, Byoungyoung Lee, Xinyu Xing, Wenke Lee
Venue: WWW
Year: 2016

Recent advance in web tracking technologies has raised many privacy concerns. To combat users' fear of privacy invasion, online vendors have taken measures such as being more transparent with users about their data use and providing options for users to manage their online activities. Such efforts gain users' trust in online vendors and improve their willingness to share their digital footprints. However, there are still a significant amount of users who actively limit involuntarily sharing of data because vendor provided management tools only restrict the use of collected data and users worry vendors do not have enough measures in place to protect their privacy sensitive information.
The Use of Trust in Social Machines
Authors: Arpit Merchant, Tushant Jha, Navjyoti Singh
Venue: WWW
Year: 2016

Trust plays an important role in the effective working of Social Machines by allowing for cooperative behaviour amongst human and digital components of the system. A detailed study of trust helps in gaining insights into the working of social machines, and allows designers to create better systems which are able to engage more people and allow for efficient operations. In this paper, we undertake a discussion on the variety of ways in which trust can be observed in Social Machines by outlining a three class taxonomy (personal, social and functional). We build upon earlier observations in past literature while seeking a broader definition. We discuss the problem of trust, that of promoting trust amongst the trustworthy in social machines, and present the various insights, challenges and frontiers that arise in response. This includes the role of institutions, communication processes and value aligned technologies in social, personal and functional trust respectively.
For Your Eyes Only: Consuming vs. Sharing Content
Authors: Ram Meshulam, Roy Sasson
Venue: WWW
Year: 2016

This paper analyzes two types of user interactions with online content: (1) private engagement with content, measured by page-views and click-through rate; and (2) social engagement, measured by the number of shares on Facebook as well as share-rate. Based on more than a billion data points across hundreds of publishers worldwide and two time periods, it is shown that the correlation between these signals is generally low. Potential reasons for the low correlation are discussed, and the notion of private-social dissonance is defined. A more in-depth analysis shows that the dissonance between private engagement and social engagement consistently depends on content category. Categories such as Sex, Crime and Celebrities have higher private engagement than social engagement. On the other hand, categories such as Books, Careers and Music have higher social engagement than private engagement. In addition to the offline analysis, a model which utilizes the different signals was trained and deployed on a live recommendation system. The resulting weights ranked the social signal lower than click-through rate. The results are relevant for publishers, content marketers, architects of recommendation systems and researchers who wish to use social signals in order to measure and predict user engagement.
Lookupia: An Intelligent Real Estate Search Engine for Finding Houses Optimally Geolocated to Reach Points of Interest
Authors: Jonathan Milot, Patrick Munroe, Eric Beaudry, François Grondin, Guillaume Bourdeau
Venue: WWW
Year: 2016

Lookupia is an intelligent real estate search engine for finding houses optimally geolocated to reach points of interest. It uses data from OpenStreetMap and most of public transit corporations that publish transit schedules in the General Transit Feed Specification (GTFS) format.
Towards a Distributed Infrastructure for Evolving Graph Analytics
Authors: Vera Zaychik Moffitt, Julia Stoyanovich
Venue: WWW
Year: 2016

Graphs are used to represent a plethora of phenomena, from the Web and social networks, to biological pathways, to semantic knowledge bases. Arguably the most interesting and important questions one can ask about graphs have to do with their evolution. Which Web pages are showing an increasing popularity trend? How does influence propagate in social networks? How does knowledge evolve?
Evaluating the use of a MOOC within Higher Education Professional Development Training
Authors: Kate Mori, Lucy Ractliffe
Venue: WWW
Year: 2016

This paper evaluates the effectiveness of a massive open online course (MOOC) as a professional development tool in higher education. The transition from the MOOC's initial intended use as a low cost way for students to access education and aid their studies has evolved to facilitate continuing professional development (CPD), particularly within the commercial sector [1]. Findings from this study indicate there is an increase in participation and satisfaction amongst higher education staff who undertook a MOOC compared to attending traditional staff development days. Recommendations from this study?s findings highlight that staff were keen to engage with the MOOC format, but felt they needed face-to-face meetings as well to reinforce, contextualize and discuss the key messages of the MOOC. In addition to this, time allocation within workloads should be considered for any future inclusion of MOOCs for staff development.
Can One Tamper with the Sample API?: Toward Neutralizing Bias from Spam and Bot Content
Authors: Fred Morstatter, Harsh Dani, Justin Sampson, Huan Liu
Venue: WWW
Year: 2016

While social media mining continues to be an active area of research, obtaining data for research is a perennial problem. Even more, obtaining unbiased data is a challenge for researchers who wish to study human behavior, and not technical artifacts induced by the sampling algorithm of a social media site. In this work, we evaluate one social media data outlet that gives data to its users in the form of a stream: Twitter's Sample API. We show that in its current form, this API can be poisoned by bots or spammers who wish to promote their content, jeopardizing the credibility of the data collected through this API. We design a proof-of-concept algorithm that shows how malicious users could increase the probability of their content appearing in the Sample API, thus biasing the content towards spam and bot content and harming the representativity of this data outlet.
Metadata Extraction from Open edX Online Courses Using Dynamic Mapping of NoSQL Queries
Authors: Dmitry Mouromtsev, Aleksei Romanov, Dmitry Volchek, Fedor Kozlov
Venue: WWW
Year: 2016

The paper describes use cases and architecture of the course extraction plugin for the Open edX platform build upon Linked Open Data. The issue of frequent repetitions of educational materials within the MOOC and relativity of recommendation tools for course developers is considered. Comprehensive review of the designed ontology and mapping, as well as evaluation using test courses are given. The last part of the paper discusses new possibilities and opportunities for the future work.
GraPhys: Understanding Health Care Insurance Data through Graph Analytics
Authors: Luis Gregorio Moyano, Ana Paula Appel, Vagner Figuerêdo de Santana, Márcia Ito, Thiago Donizetti dos Santos
Venue: WWW
Year: 2016

Healthcare insurance data represent a rich source of information and has the potential to contribute significantly in guiding business decision making. In this work we present GraPhys, a Graph Analysis platform designed for exploration, visualization and analysis of healthcare insurance data and its corresponding metadata. By taking advantage of relationships contained in healthcare claims data, we are able to apply Graph Analytics methods and algorithms in order to devise useful business metrics to guide data analysis and exploration. Our tool focuses in better understanding physicians, patients and their practices. We illustrate our approach by demonstrating two use cases where we show how graph analytics metrics, combined with other data, may lead to useful insights not directly available to traditional Business Analytics.
Ontological Networks: Mapping Ontological Knowledge Bases into Graphs
Authors: Lucas Fonseca Navarro, Estevam Rafael Hruschka Junior, Ana Paula Appel
Venue: WWW
Year: 2016

With the exponentially growing amount of available data on the Web over the last years, several projects have been created to automatically extract knowledge from this information set. As the data domains on the Web are too wide, most of these projects store the acquired knowledge in ontological knowledge bases (OKBs). Mapping it into graph-based representation makes possible to apply graph-mining techniques to extract implicit information. However most of these projects treat the mapping process using different adjustments in several ways, thus, there is not a standard mapping process or a formal way defifined to do this task. In this paper we formally describe a graph structure called Ontological Network and how it can be used to map an Ontological Knowledge Base. We also show some graph-mining based algorithms to add new facts and to extend the ontology of an OKB while mapped into an Ontological Network as example.
CRATE: Writing Stories Together with our Browsers
Authors: Brice Nédelec, Pascal Molli, Achour Mostéfaoui
Venue: WWW
Year: 2016

Real-time collaborative editors are common tools for distributing work across space, time, and organizations. Unfortunately, mainstream editors such as Google Docs rely on central servers and raise privacy and scalability issues. CRATE is a real-time decentralized collaborative editor that runs directly in web browsers thanks to WebRTC. Compared to state-of-the-art, CRATE is the first real-time editor that only requires browsers in order to support collaborative editing and to transparently handle from small to large groups of users. Consequently, CRATE can also be used in massive online lectures, TV shows or large conferences to allow users to share their notes. CRATE's properties rely on two scientific results: (i) a replicated sequence structure with sub-linear upper bound on space complexity; this prevents the editor from running costly distributed garbage collectors, (ii) an adaptive peer sampling protocol; this prevent the editor from oversizing routing tables, hence from letting small networks pay the price of large networks. This paper describes CRATE, its properties and its usage.
LODVader: An Interface to LOD Visualization, Analyticsand DiscovERy in Real-time
Authors: Ciro Baron Neto, Kay Müller, Martin Brümmer, Dimitris Kontokostas, Sebastian Hellmann
Venue: WWW
Year: 2016

The Linked Open Data (LOD) cloud is in danger of becoming a black box. Simple questions such as "What kind of datasets are in the LOD cloud?", "In what way(s) are these datasets connected?" -- albeit frequently asked -- are at the moment still difficult to answer due to the lack of proper tooling support. The infrequent update of the static LOD cloud diagram adds to the current dilemma, since there is neither reliable nor timely-updated information to perform an interactive search, analysis or in particular visualization in order to gain insight into the current state of Linked Open Data. In this paper, we propose a new hybrid system which combines LOD Visualisation, Analytics and DiscovERy (LODVader) to aid in answering the above questions. LODVader is equipped with (1) a multi-layer LOD cloud visualization component comprising datasets, subsets and vocabularies, (2) dataset analysis components that extend the state of the art with new similarity measures and efficient link extracting techniques and (3) a fast search index that is an entry point for dataset discovery. At its core, LODVader employs a timely-updated index using a complex cluster of Bloom filters as a fast search index with low memory footprint. This BF cluster is able to efficiently perform analysis on link and dataset similarities based on stored predicate and object information, which -- once inverted -- can be employed to discover invalid links by displaying the Dark LOD Cloud. By combining all these features, we allow for an up-to-date, multi-dimensional LOD cloud analysis, which -- to the best of our knowledge -- was not possible before.
skos-history: Exploiting Web Standards for Change Tracking in Knowledge Organization Systems
Authors: Joachim Neubert
Venue: WWW
Year: 2016

"What's new?" and "What has changed?" are questions users of knowledge organizations systems, such as thesauri, classifications or taxonomies, ask, when new versions of such vocabularies are published. Until recently, it had been difficult for the publishers to provide this information (normally resorting to custom change logging within maintenance applications), and almost impossible for anybody else. With the widespread acceptance of SKOS as the standard publication and exchange format, the situation has changed fundamentally: Exact deltas between two sets of RDF triples can be computed, and the differences can be organized in a meaningful way through SPARQL queries, taking advantage of regular SKOS structures. skos-history combines a script for creating a "version store" with queries accessing this store to generate standard reports such as "added concepts" or "changed notations", or more subtle changes like concept splits, as well as aggregated statistics on certain change types, or a complete change history for a single concept across multiple versions. To allow for interactive sorting, filtering and downloading these reports, and for editing the queries in an IDE-like environment to adapt them to different vocabularies or user needs, other open source libraries are integrated.
Context-Aware Text Representation for Social Relation Aided Sentiment Analysis
Authors: Minh Luan Nguyen
Venue: WWW
Year: 2016

In this paper, we propose CaTER, which learns a novel context-aware joint representation of text and user by incorporating semantic text embedding of unlabeled tweets as well as social relation information. CaTER leverages the wealth of user contextual information available apart from user's utterances for sentiment analysis. Our approach is inspired by social science about emotional behaviors of connected users, who perhaps more likely to consensus on similar opinions. Our method outperforms numerous baselines on two real-world Twitter datasets.
Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data
Authors: Lisette Espin Noboa, Florian Lemmerich, Philipp Singer, Markus Strohmaier
Venue: WWW
Year: 2016

Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for characterizing these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses, we utilize publicly available Web data (i.e., Foursquare and census data). By studying taxi data in Manhattan, we can discover and characterize human mobility patterns that cannot be identified in a collective analysis. As one example, we find a group of taxi rides that end at locations with a high number of party venues on weekend nights. Our findings argue for a more fine-grained analysis of human mobility in order to make informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.
Context-based A/B Test Validation
Authors: Michael Nolting, Jan Eike von Seggern
Venue: WWW
Year: 2016

Data-driven and continuous development and deployment of modern web applications depend critically on registering changes as fast aspossible, paving the way for short innovation cycles. A/B testing is a popular tool for comparing the performance of different variants. Despite the widespread use of A/B tests, there is little research on how to assert the validity of such tests. Even small changes in the application's user base, hard- or software stack not related to the variants under test can transform on possibly hidden paths into significant disturbances of the overall evaluation criterion (OEC) of an A/B test and, hence, invalidate such a test. Therefore, the highly dynamic server and client run-time environments of modern web applications make it difficult to assert correctly the validity of an A/B test.
The Semantic Web and the Semantics of the Web: Where Does Meaning Come From?
Authors: Peter Norvig
Venue: WWW
Year: 2016

We would like to understand the meaning of content on the web. Bit where should that meaning come from? From markup language annotations created by the authors of the content? Crowdsourced from readers of the content? Automatically extracted by machine learning algorithms? This talk investigates the possibilities.
Article De-duplication Using Distributed Representations
Authors: Shumpei Okura, Yukihiro Tagami, Akira Tajima
Venue: WWW
Year: 2016

In news recommendation systems, eliminating redundant information is important as well as providing interesting articles for users. We propose a method that quantifies the similarity of articles based on their distributed representation, learned with the category information as weak supervision. This method is useful for evaluation under tight time constraints, since it only requires low-dimensional inner product calculation for estimating similarities. The experimental results from human evaluation and online performance in A/B testing suggest the effectiveness of our proposed method, especially for quantifying middle-level similarities. Currently, this method is used on Yahoo!\ JAPAN's front page, which has millions of users per day and billions of page views per month.
H-index Sequences across Fields: A Comparative Analysis
Authors: Marlies Olensky, Tsung-Han Tsai, Kuan-Ta Chen
Venue: WWW
Year: 2016

This study presents the first analysis of h-index sequences on a larger scale. Exemplarily, we investigated researchers from three different fields within Computer Science. We use Google Scholar citation profiles as data source to construct the h-index sequences of individual researchers. Our ultimate goal is to develop a self-evaluation tool, to assess one's own development of the h-index in comparison to other researchers in the same field, maybe identify career role models in the field and assess career development with future chances of success. The results of this study show that the average h-index sequences behave differently for the datasets, which is partly due to the different sample sizes. Hence, further research will be needed to confirm if every research field behaves differently. In addition, we applied the algorithm developed by Wu et al. to our data to classify the h-index sequences of individual authors according to five different shape categories. The majority of researchers has an S-shaped h-index sequence, followed by IS-shaped and linear sequences. Purely concave or convex sequences hardly ever occur. The researchers with the highest h-indices after 10 career years respectively belong to the S-shaped and IS-shaped categories with a few linear category occurrences. Hence, having a linear h-index is not only very hard to achieve, it is also not a guaranty to be the researcher with the highest h-index in a field.