OpenRaiser/Intern-Atlas huggingface dataset:
Understanding the use of Temporal Expressions on Persian Web Search
Authors: Behrooz Mansouri, Mohammad Sadegh Zahedi, Ricardo Campos, Mojgan Farhoodi, Alireza Yari
Venue: WWW
Year: 2018

The development of information retrieval algorithms and temporal information retrieval ones has been extensively carried out over the last few years. While several studies have been conducted, most of these researches relate to English, leading to a lack of knowledge in several other important languages. This includes the Persian one. In this work, we aim to shorten this gap by contributing, disseminating and enlarging the knowledge we have on temporal information retrieval aspects in Persian, which is one of the dominant languages in the Middle East, widely spoken in several countries. To achieve this objective, we propose to understand the use of temporal expressions on a large-scale Persian search engine query log consisting of 27M queries. In particular, we focus on explicit (e.g., June 2017) and relative temporal expressions (e.g., tomorrow) and try to understand (1) how often temporal expressions are used in web queries; (2) which type of temporal expressions (Date, Time, Duration and Set) are commonly used; (3) to which time (past, current or future) do temporal expressions mostly refer to; (4) to which category they often belong; (5) how often do user's reformulate their queries by adding temporal expressions; and (6) how using temporal expressions affects user's satisfaction. We believe that answering these questions may be beneficial for a large number of tasks including, user's behavior understanding and search engines' improvement effectiveness.
BarcelonaNow: Empowering Citizens with Interactive Dashboards for Urban Data Exploration
Authors: Mirko Marras, Matteo Manca, Ludovico Boratto, Gianni Fenu, David Laniado
Venue: WWW
Year: 2018

The massive amount and variety of city-related data raise equally big challenges to enable citizens to make sense of such data for improving their daily life and fostering collective decision making. The existing dashboards include limited pre-defined use cases which can only address the most common needs of citizens, but do not allow for personalization. In this work, we propose an open source environment that enables citizens to easily explore city-related data. A backend continuously collects heterogeneous data, turns them into a unified format and exposes them through an API; a front-end allows users to create interactive visualizations combining different data sources and visual models, and to share them to engage others. In this way, citizens can build up a data-driven public awareness supporting an open, transparent, and collaborative city.
Web Performance Automation for the People
Authors: Robin Marx
Venue: WWW
Year: 2018

Web performance is important for the user experience and can heavily influence web page revenues. While there are many established Web Performance Optimization (WPO) methods, our work so far has clearly shown that new network protocols, optimized browsers and cutting-edge web standards can have a significant impact on known best practices. Additionally, there is still low-hanging fruit to be exploited, in the form of personalizing performance based on user context (i.e., current device, network, browser) and user preferences (e.g., text reading vs multimedia experience). In our PhD project, we strive to integrate this user-specific metadata into dynamic configurations for both existing and new automated WPO techniques. An intermediate server can (pre)generate optimized versions of a web page, which are then selected based on user context and preferences. Additional metadata is also passed along to the browser, enabling improvements on that side, and used to steer new network protocols to speed up the incremental delivery of page resources. We use the Speeder platform to perform and evaluate full-factorial objective measurements and use subjective user studies across a range of groups to assess the applicability of our methods to end users. Our aim is to provide insights in how WPO can be tweaked for specific users, in the hopes of leading to new web standards that enable this behavior.
Incognito: A Method for Obfuscating Web Data
Authors: Rahat Masood, Dinusha Vatsalan, Muhammad Ikram, Mohamed Ali Kâafar
Venue: WWW
Year: 2018

Users leave a trail of their personal data, interests, and intents while surfing or sharing information on the Web. Web data could therefore reveal some private/sensitive information about users based on inference analysis. The possible identification of information corresponding to a single individual by an inference attack holds true even if the user identifiers are encoded or removed in the Web data. Several works have been done on improving privacy of Web data through obfuscation methods~\citeHow09,Dom09,Sha05,Che14. However, these methods are neither comprehensive, generic to be applicable to any Web data, nor effective against adversarial attacks. To this end, we propose a privacy-aware obfuscation method for Web data addressing these identified drawbacks of existing methods. We use probabilistic methods to predict privacy risk of Web data that incorporates all key privacy aspects, which are uniqueness, uniformity, and linkability of Web data. The Web data with high predicted risk are then obfuscated by our method to minimize the privacy risk using semantically similar data. Our method is resistant against adversary who has knowledge about the datasets and model learned risk probabilities using differential privacy-based noise addition. Experimental study conducted on two real Web datasets validates the significance and efficacy of our method. Our results indicate that the average privacy risk reaches to 100% with a minimum of 10 sensitive Web entries, while at most 0% privacy risk could be attained with our obfuscation method at the cost of average utility loss of 64.3%.
A Preliminary Open Data Publishing Strategy for Live Data in Flanders
Authors: Julián Andrés Rojas Meléndez, Brecht Van de Vyvere, Arne Gevaert, Ruben Taelman, Pieter Colpaert, Ruben Verborgh
Venue: WWW
Year: 2018

For smart decision making, user agents need live and historic access to open data from sensors installed in the public domain. In contrast to a closed environment, for Open Data and federated query processing algorithms, the data publisher cannot anticipate in advance on specific questions, nor can it deal with a bad cost-efficiency of the server interface when data consumers increase. When publishing observations from sensors, different fragmentation strategies can be thought of depending on how the historic data needs to be queried. Furthermore, both publish/subscribe and polling strategies exist to publish live updates. Each of these strategies come with their own trade-offs regarding cost-efficiency of the server-interface, user-perceived performance and cpu use. A polling strategy where multiple observations are published in a paged collection was tested in a proof of concept for parking spaces availability. In order to understand the different resource trade-offs presented by publish/subscribe and polling publication strategies, we devised an experiment on two machines, for a scalability test. The preliminary results were inconclusive and suggest more large scale tests are needed in order to see a trend. While the large-scale tests will be performed in future work, the proof of concept helped to identify the technical Open Data principles for the 13 biggest cities in Flanders.
Modeling Success and Engagement for the App Economy
Authors: Haim Mendelson, Ken Moon
Venue: WWW
Year: 2018

While apps on the whole increasingly drive and shape present-day Web usage, they individually thrive and fall based on rich patterns of user adoption and long-term engagement. In studying these patterns at the population level, researchers remain curtailed by practical difficulties in accessing consistently detailed data covering large, representative cross-sections of apps. We address this challenge by proposing an empirical framework for analyzing an app»s patterns of adoption and engagement. Importantly, our approach requires obtaining only an app»s daily, weekly, and monthly usership time-series data, which are popular metrics tracked and made available for many apps. Modeling how the nuanced co-dynamics of an app»s daily, weekly, and monthly usership measures (DAU, WAU, and MAU) reliably reveal user adoption and repeat engagement, we extend and demonstrably improve on the predictive performance of prior install- or DAU-based methods. To further study the mechanisms by which successful apps cultivate their userbases, we position apps along the dual axes of viral adoption and retentive engagement as mechanisms to potentially explain success. Applying our approach to data on Facebook apps, we show that these apps over time became less viral and more engaging. Interestingly, despite their diminished virality, their improved user retention subtly and counter-intuitively boosted these apps' rates of new user adoptions through user-activity-based word of mouth, because an engaged user contributes to word of mouth over a longer period of time. In a final case application, we introduce evidence that developers of apps that learn to successfully retain their users carry this valuable experience over into their new apps.
Uncovering HTTP Header Inconsistencies and the Impact on Desktop/Mobile Websites
Authors: Abner Mendoza, Phakpoom Chinprutthiwong, Guofei Gu
Venue: WWW
Year: 2018

The paradigm shift to a mobile-first economy has seen a drastic increase in mobile-optimized websites that in many cases are derived from their desktop counterparts. Mobile website design is often focused on performance optimization rather than security, and possibly developed by different teams of developers. This has resulted in a number of subtle but critical inconsistencies in terms of security guarantees provided on the web platform, such as protection mechanisms against common web attacks. In this work, we have conducted the first systematic measurement study of inconsistencies between mobile and desktop HTTP security response configuration in the top 70,000 websites. We show that HTTP security configuration inconsistencies between mobile and desktop versions of the same website can lead to vulnerabilities. Our study compares data snapshots collected one year apart to garner insights into the longitudinal trends of mobile versus desktop inconsistencies in websites.
Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-oriented Systems
Authors: Martino Mensio, Giuseppe Rizzo, Maurizio Morisio
Venue: WWW
Year: 2018

QA systems offer a human friendly interface to navigate through knowledge, which can range from encyclopedic to domain-specific. Generally, a QA system is designed to provide an answer to a specific question once (so-called single turn) and state-of-the-art systems reach nowadays robust performance in such a scenario. However, most of the interactions with QA systems are based on multiple handshakes of question/answer pairs, where the human being refines the questions further, while the system can collect the necessary information and generate a compelling final answer through multiple turns. In this paper, we investigate and experiment a multi-turn QA system that is suited to work given a particular domain of knowledge and configurable goals. Our approach models the entire dialogue as a sequence of turns, i.e. questions and answers, using a Recurrent Neural Network which is firstly trained to understand natural language, classifying entities and intents using prior knowledge of domain-specific interactions, and provide answers according to the domain used as background knowledge. We have compared our approach with state-of-the-art sequence-based intent classification using a well-known and standardized gold standard observing an increase of 17.16% of F1. Results show the robustness of the approach and the competitive results motivate the adoption in multi-turn QA scenarios.
The Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions
Authors: Martino Mensio, Giuseppe Rizzo, Maurizio Morisio
Venue: WWW
Year: 2018

A future where the conversation with machines can potentially involve mutual emotions between the parties may be not so far in time. Inspired by the episode of Black Mirror "Be Right Back'' and Replika, a futuristic app that promises to be "your best friend'', in this work we are considering the positive and negative points of including an automated learning conversational agent inside the personal world of feelings and emotions. These systems can impact both single individuals and society, worsening an already critical situation. Our conclusion is that a regulation on the artificial emotional content should be considered before actually going beyond some one-way-only limits.
SmartPub: A Platform for Long-Tail Entity Extraction from Scientific Publications
Authors: Sepideh Mesbah, Alessandro Bozzon, Christoph Lofi, Geert-Jan Houben
Venue: WWW
Year: 2018

This demo presents SmartPub, a novel web-based platform that supports the exploration and visualization of shallow meta-data (e.g., author list, keywords) and deep meta-data--long tail named entities which are rare, and often relevant only in specific knowledge domain--from scientific publications. The platform collects documents from different sources (e.g. DBLP and Arxiv), and extracts the domain-specific named entities from the text of the publications using Named Entity Recognizers (NERs) which we can train with minimal human supervision even for rare entity types. The platform further enables the interaction with the Crowd for filtering purposes or training data generation, and provides extended visualization and exploration capabilities. SmartPub will be demonstrated using sample collection of scientific publications focusing on the computer science domain and will address the entity types Dataset (i.e. dataset presented or used in a publication), and Methods (i.e. algorithms used to create/enrich/analyse a data set)
Panning for gold.com: Understanding the Dynamics of Domain Dropcatching
Authors: Najmeh Miramirkhani, Timothy Barron, Michael Ferdman, Nick Nikiforakis
Venue: WWW
Year: 2018

An event that is rarely considered by technical users and laymen alike is that of a domain name expiration. The massive growth in the registration of domain names is matched by massive numbers of domain expirations, after which domains are made available for registration again. While the vast majority of expiring domains are of no value, among the hundreds of thousands of daily expirations, there exist domains that are clearly valuable, either because of their lexical composition, or because of their residual trust. In this paper, we investigate the dynamics of domain dropcatching where companies, on behalf of users, compete to register the most desirable domains as soon as they are made available and then auction them off to the highest bidder. Using a data-driven approach, we monitor the expiration of 28 million domains over the period of nine months, collecting domain features, WHOIS records, and crawling the registered domains on a regular basis to uncover the purpose for which they were re-registered (caught). Among others, we find that on average, only 10% of the expired (dropped) domains are caught with the vast majority of the re-registrations happening on the day they are released. We investigate the features that make some domains more likely to be caught than others and discover that a domain that was malicious at the time of its expiration is twice as likely to be caught than the average domain. Moreover, previously-malicious domains are significantly more likely to be reused for malicious purposes than previously benign domains. We identify three types of users who are interested in purchasing dropped domains, ranging from freelancers who purchase one or two domains to professionals who invest more than $115K purchasing dropped domains in only three months. Finally, we observe that less than 11% were used to host web content with the remaining domains used either by speculators, or by malicious actors.
The Effect of Ad Blocking on User Engagement with the Web
Authors: Ben Miroglio, David Zeber, Jofish Kaye, Rebecca Weiss
Venue: WWW
Year: 2018

Web users are increasingly turning to ad blockers to avoid ads, which are often perceived as annoying or an invasion of privacy. While there has been significant research into the factors driving ad blocker adoption and the detrimental effect to ad publishers on the Web, the resulting effects of ad blocker usage on Web users' browsing experience is not well understood. To approach this problem, we conduct a retrospective natural field experiment using Firefox browser usage data, with the goal of estimating the effect of adblocking on user engagement with the Web. We focus on new users who installed an ad blocker after a baseline observation period, to avoid comparing different populations. Their subsequent browser activity is compared against that of a control group, whose members do not use ad blockers, over a corresponding observation period, controlling for prior baseline usage. In order to estimate causal effects, we employ propensity score matching on a number of other features recorded during the baseline period. In the group that installed an ad blocker, we find significant increases in both active time spent in the browser (+28% over control) and the number of pages viewed (+15% over control), while seeing no change in the number of searches. Additionally, by reapplying the same methodology to other popular Firefox browser extensions, we show that these effects are specific to ad blockers. We conclude that ad blocking has a positive impact on user engagement with the Web, suggesting that any costs of using ad blockers to users' browsing experience are largely drowned out by the utility that they offer.
Dynamic Mechanism Design in the Field
Authors: Vahab S. Mirrokni, Renato Paes Leme, Rita Ren, Song Zuo
Venue: WWW
Year: 2018

Dynamic mechanisms are a powerful technique in designing revenue-maximizing repeated auctions. Despite their strength, these types of mechanisms have not been widely adopted in practice for several reasons, e.g., for their complexity, and for their sensitivity to the accuracy of predicting buyers» value distributions. In this paper, we aim to address these shortcomings and develop simple dynamic mechanisms that can be implemented efficiently, and provide theoretical guidelines for decreasing the sensitivity of dynamic mechanisms on prediction accuracy of buyers» value distributions. We prove that the dynamic mechanism we propose is provably dynamic incentive compatible, and introduce a notion of buyers» regret in dynamic mechanisms, and show that our mechanism achieves bounded regret while improving revenue and social welfare compared to a static reserve pricing policy. Finally, we confirm our theoretical analysis via an extensive empirical study of our dynamic auction on real data sets from online adverting. For example, we show our dynamic mechanisms can provide a +17% revenue lift with relative regret less than 0.2%.
Evolutionary Learning Model of Social Networking Services with Diminishing Marginal Utility
Authors: Yutaro Miura, Fujio Toriumi, Toshiharu Sugawara
Venue: WWW
Year: 2018

We propose a model of a social networking service (SNS) with diminishing marginal utility in the framework of evolutionary computing and present our investigation on the effect of diminishing marginal utility on the dominant structure of strategies in all agents. SNSs such as Twitter and Facebook have been growing rapidly, but why they are prospering is unknown. SNSs have the characteristics of a public goods game because they are maintained by users posting many articles that incur some cost and because users can also be free riders, who just read articles. Thus, a number of studies aimed at understanding the conditions or mechanisms that keep social media thriving theoretically by introducing the meta-rewards game, which is a variation of a public goods game. The meta-rewards games assume constant marginal utility, meaning that the rewards by receiving comments increase linearly according to the number of comments, but describing the psychological rewards of humans is often inappropriate. In this paper, we present our modification of the model using the diminishing marginal utility and our comparison of the experimental results with those of the original meta-rewards game. We demonstrate that the structure of dominant strategies of all agents in our game is quite different from that in the original meta-rewards game and is more reasonable to explain the users' behavior in SNSs because their efforts in SNSs are limited even if they have many friends.
WWW'18 Workshop on Exploitation of Social Media for Emergency Relief and Preparedness: Chairs' Welcome & Organization
Authors: Marie-Francine Moens, Gareth J. F. Jones, Saptarshi Ghosh, Debasis Ganguly, Tanmoy Chakraborty, Kripabandhu Ghosh
Venue: WWW
Year: 2018

User-generated content on online social media (OSM) platforms has become an important source of real-time information during emergency events. The SMERP workshop series aims to provide a forum for researchers working on utilizing OSM for emergency preparedness and aiding post-emergency relief operations. The workshop aims to bring together researchers from diverse fields - Information Retrieval, Data Mining and Machine Learning, Natural Language Processing, Social Network Analysis, Computational Social Science, Human Computer Interaction - who can potentially contribute to utilizing social media for emergency relief and preparedness. The first SMERP workshop was held in April 2017 in conjunction with the ECIR 2017 conference. This 2nd SMERP Workshop with The Web Conference 2018 includes two keynote talks, a peer-reviewed research paper track, and a panel discussion.
Discovering Connotations as Labels for Weakly Supervised Image-Sentence Data
Authors: Aditya Mogadala, Bhargav Kanuparthi, Achim Rettinger, York Sure-Vetter
Venue: WWW
Year: 2018

Growth of multimodal content on the web and social media has generated abundant weakly aligned image-sentence pairs. However, it is hard to interpret them directly due to intrinsic intension. In this paper, we aim to annotate such image-sentence pairs with connotations as labels to capture the intrinsic intension. We achieve it with a connotation multimodal embedding model (CMEM) using a novel loss function. It's unique characteristics over previous models include: (i) the exploitation of multimodal data as opposed to only visual information, (ii) robustness to outlier labels in a multi-label scenario and (iii) works effectively with large-scale weakly supervised data. With extensive quantitative evaluation, we exhibit the effectiveness of CMEM for detection of multiple labels over other state-of-the-art approaches. Also, we show that in addition to annotation of image-sentence pairs with connotation labels, byproduct of our model inherently supports cross-modal retrieval i.e. image query - sentence retrieval.