OpenRaiser/Intern-Atlas huggingface dataset:
Hybrid Human Machine workflows for mobility management
Authors: Eddy Maddalena, Luis-Daniel Ibáñez, Elena Simperl, Richard Gomer, Mattia Zeni, Donglei Song, Fausto Giunchiglia
Venue: WWW
Year: 2019

Sustainable mobility is one of the main goals of both European and United Nations plans for 2030. The concept of Smart Cities has arisen as a way to achieve this goal by leveraging IoT interconnected devices to collect and analyse large quantities of data. However, several works have pointed out the importance of including the human factor, and in particular, citizens, to make sense of the collected data and ensure their engagement along the data value chain. This paper presents the design and implementation of two end-to-end hybrid human-machine workflows for solving two mobility problems: modal split estimation, and mapping mobility infrastructure. For modal split, we combine the use of i-Log, an app to collect data and interact with citizens, with reinforcement learning classifiers to continuously improve the accuracy of the classification, aiming at reducing the required interactions from citizens. For mobility infrastructure, we developed a system that uses remote crowdworkers to explore the city looking for Points of Interest, that is more scalable than sending agents on the field. Crowdsourced maps are then fused with existing maps (if available) to create a final map that then is validated on the field by citizens engaged through the i-Log app.
Querying Data Lakes using Spark and Presto
Authors: Mohamed Nadjib Mami, Damien Graux, Simon Scerri, Hajira Jabeen, Sören Auer
Venue: WWW
Year: 2019

Squerall is a tool that allows the querying of heterogeneous, large-scale data sources by leveraging state-of-the-art Big Data processing engines: Spark and Presto. Queries are posed on-demand against a Data Lake, i.e., directly on the original data sources without requiring prior data transformation. We showcase Squerall's ability to query five different data sources, including inter alia the popular Cassandra and MongoDB. In particular, we demonstrate how it can jointly query heterogeneous data sources, and how interested developers can easily extend it to support additional data sources. Graphical user interfaces (GUIs) are offered to support users in (1) building intra-source queries, and (2) creating required input files.
TaskMate: A Mechanism to Improve the Quality of Instructions in Crowdsourcing
Authors: V. K. Chaithanya Manam, Dwarakanath Jampani, Mariam Zaim, Meng-Han Wu, Alexander J. Quinn
Venue: WWW
Year: 2019

Developing instructions for microtask crowd workers requires time to ensure consistent interpretations by crowd workers. Even with substantial effort, workers may still misinterpret the instructions due to ambiguous language and structure in the task design. Prior work demonstrated methods for facilitating iterative improvement with help from the requester. However, any participation by the requester reduces the time saved by delegating the work—and hence the utility of using crowdsourcing. We present TaskMate, a system for facilitating worker-led refinement of task instructions with minimal involvement by the requester. Small teams of workers search for ambiguities and vote on the interpretation they believe the requester intended. This paper describes the workflow, our implementation, and our preliminary evaluation.
Exploring Video Game Searches on the Web
Authors: Behrooz Mansouri, Mohammad Sadegh Zahedi, Ricardo Campos, Mojgan Farhoodi
Venue: WWW
Year: 2019

As video games are developing fast, many users issue queries related to video games in a daily fashion. While there were a few attempts to understand their behavior, little is known on how the video game-related searches are done. Digesting and analyzing this search behavior may thus be faced as an important contribution for search engines to provide better results and search services for their users. To overcome this lack of knowledge and to gain more insight into how video game searches are done, we analyze in this paper, a number of game search queries submitted to a general search engine named Parsijoo. The analysis conducted was performed on top of 372,508 game search records extracted from the query logs within 253,516 different search sessions. Different aspects of video game searches are studied, including, their temporal distribution, game version specification, popular game categories, popular game platforms, game search sessions and clicked pages. Overall, the experimental analysis on video game searches shows that the current retrieval methods used by traditional search engines cannot be applied for game searches, thus, different retrieval and search services should be considered for these searches in the future.
Enlisting the Public to Build a Healthier Web Information Commons
Authors: Amin Mantrach, Fabrizio Silvestri
Venue: WWW
Year: 2019

Over the past three years, platforms, governments and a plethora of nonprofit initiatives have prioritized fighting online misinformation through a variety of different means. Yet the current framework is too fragmented to deliver global results. The big tech platforms have data, but no public accountability. Governments (mostly) have democratic legitimacy, but little information on what is actually going on in the platforms they're itching to regulate. And nonprofit initiatives too often lack the scale to affect change at the level needed. What if we came up with a dramatically new deliberative process that involves a global community of concerned citizens ready to share information and participate in consultations to improve collective decision-making? What if a more accountable, diverse and verifiable Web were still possible?
A Large-scale Study on the Risks of the HTML5 WebAPI for Mobile Sensor-based Attacks
Authors: Francesco Marcantoni, Michalis Diamantaris, Sotiris Ioannidis, Jason Polakis
Venue: WWW
Year: 2019

Smartphone sensors can be leveraged by malicious apps for a plethora of different attacks, which can also be deployed by malicious websites through the HTML5 WebAPI. In this paper we provide a comprehensive evaluation of the multifaceted threat that mobile web browsing poses to users, by conducting a large-scale study of mobile-specific HTML5 WebAPI calls used in the wild. We build a novel testing infrastructure consisting of actual smartphones on top of a dynamic Android app analysis framework, allowing us to conduct an end-to-end exploration. Our study reveals the extent to which websites are actively leveraging the WebAPI for collecting sensor data, with 2.89% of websites accessing at least one mobile sensor. To provide a comprehensive assessment of the potential risks of this emerging practice, we create a taxonomy of sensor-based attacks from prior studies, and present an in-depth analysis by framing our collected data within that taxonomy. We find that 1.63% of websites could carry out at least one of those attacks. Our findings emphasize the need for a standardized policy across browsers and the ability for users to control what sensor data each website can access.
Black Hat Trolling, White Hat Trolling, and Hacking the Attention Landscape
Authors: Jeanna N. Matthews, Matt Goerzen
Venue: WWW
Year: 2019

In this paper, we analogize the practice of trolling to the practice of hacking. Just as hacking often involves the discovery and exploitation of vulnerabilities in a computer security landscape, trolling frequently involves the discovery and exploitation of vulnerabilities in a media or attention landscape to amplify messages and direct attention. Also like with hacking, we consider the possibility for a range of trolling personas: from black hat trolls who push an agenda that is clearly counter to the interests of the target, to gray hat trolls who exploit vulnerabilities to draw critical attention to unaddressed issues, and white hat trolls who could help proactively disclose vulnerabilities so that attack surface can be reduced. We discuss a variety of trolling techniques from dogpiling to sockpuppetry and also a range of possible interventions.
GViz - An Interactive WebApp to Support GeoSPARQL over Integrated Building Information
Authors: Kris McGlinn, Darragh Blake, Declan O&apos, Sullivan
Venue: WWW
Year: 2019

Linked data (LD) is a technology to support publishing structured data on the web so that it may be interlinked. Building Information Modelling (BIM) is a key enabler to support integration of building data within the buildings life cycle (BLC). LD can therefore provide better access and more semantically useful querying of BIM data. The integration of BIM into the geospatial domain provides much needed contextual information about the building and its surroundings, and can support geospatial querying over BIM data. Creating GeoSPARQL queries for users who are non experts in semantic web technologies can be a challenge. In this paper we present a visualization tool built upon HTML5 and WebGL technologies that supports queries over linked data without the need to understand the resulting SPARQL queries. The interactive web interface can be quickly extended to support new use cases, for example, related to 3D geometries. The paper discusses the underlying data management, the methodology for uplifting several open data sources into Resource Description Framework (RDF), and the front-end implementation tested over a sample use case. Finally some discussion and future work is given, with a focus on how this tool can potentially support BIM integration.
Modelling the Provenance of Linked Data Interlinks for the Library Domain
Authors: Lucy McKenna, Christophe Debruyne, Declan O&apos, Sullivan
Venue: WWW
Year: 2019

As the Web of Data grows, so does the need to establish the quality and trustworthiness of its contents. Increasing numbers of libraries are publishing their metadata as Linked Data (LD). As these institutions are considered authoritative sources of information, it is likely that library LD will be treated with increased credibility over data published by other sources. However, in order to establish this trust, the provenance of library LD must be provided. In 2018 we conducted a survey which explored the position of Information Professionals (IPs), such as librarians, archivists and cataloguers, with regards to LD. Results indicated that IPs find the process of LD interlinking to be a particularly challenging. In order to publish authoritative interlinks, provenance data for the description and justification of the links is required. As such, the goal of this research is to provide a provenance model for the LD interlinking process that meets the requirements of library metadata standards. Many current LD technologies are not accessible to non-technical experts or attuned to the needs of the library domain. By designing a model specifically for libraries, with input from IPs, we aim to facilitate this domain in the process of creating interlink provenance data.
Event Detection using Hierarchical Multi-Aspect Attention
Authors: Sneha Mehta, Mohammad Raihanul Islam, Huzefa Rangwala, Naren Ramakrishnan
Venue: WWW
Year: 2019

Classical event encoding and extraction methods rely on fixed dictionaries of keywords and templates or require ground truth labels for phrase/sentences. This hinders widespread application of information encoding approaches to large-scale free form (unstructured) text available on the web. Event encoding can be viewed as a hierarchical task where the coarser level task is event detection, i.e., identification of documents containing a specific event, and where the fine-grained task is one of event encoding, i.e., identifying key phrases, key sentences. Hierarchical models with attention seem like a natural choice for this problem, given their ability to differentially attend to more or less important features when constructing document representations. In this work we present a novel factorized bilinear multi-aspect attention mechanism (FBMA) that attends to different aspects of text while constructing its representation. We find that our approach outperforms state-of-the-art baselines for detecting civil unrest, military action, and non-state actor events from corpora in two different languages.
Scalable Knowledge Graph Construction over Text using Deep Learning based Predicate Mapping
Authors: Aman Mehta, Aashay Singhal, Kamalakar Karlapalem
Venue: WWW
Year: 2019

Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work.
The Importance of Canonical Product Attributes on User Opinions: an Empirical Investigation
Authors: Tiago de Melo, Altigran S. da Silva, Edleno Silva de Moura, Pável Calado
Venue: WWW
Year: 2019

When making purchasing decisions, customers usually rely on information from two types of sources: product specifications, provided by manufacturers, and reviews, posted by other customers. Both kinds of information are often available on e-commerce websites. While researchers have demonstrated the importance of product specifications and reviews as separate and valuable sources to support purchase decision-making, a largely uninvestigated issue is what is the relationship between these two kinds of information. In this paper we present an empirical study on the use of direct and indirect mentions to canonical product attributes, that is, those defined by manufactures in product specifications, in the reviews written by customers. For this study, we analyzed more than 1,100,000 opinionated sentences available in about 650,000 user reviews from Amazon.com across five product categories. Our results indicate that user opinions are indeed guided by the attributes from product specifications and highlight the influence of canonical attributes on the user reviews.
Signals Matter: Understanding Popularity and Impact of Users on Stack Overflow
Authors: Arpit Merchant, Daksh Shah, Gurpreet Singh Bhatia, Anurag Ghosh, Ponnurangam Kumaraguru
Venue: WWW
Year: 2019

Stack Overflow, a Q&A site on programming, awards reputation points and badges (game elements) to users on performing various actions. Situating our work in Digital Signaling Theory, we investigate the role of these game elements in characterizing social qualities (specifically, popularity and impact) of its users. We operationalize these attributes using common metrics and apply statistical modeling to empirically quantify and validate the strength of these signals. Our results are based on a rich dataset of 3,831,147 users and their activities spanning nearly a decade since the site's inception in 2008. We present evidence that certain non-trivial badges, reputation scores and age of the user on the site positively correlate with popularity and impact. Further, we find that the presence of costly to earn and hard to observe signals qualitatively differentiates highly impactful users from highly popular users.
Looking Backward to See a Way Forward
Authors: Miriam J. Metzger
Venue: WWW
Year: 2019

Credibility research in the social sciences has a long history that may be particularly informative to today's efforts to combat misinformation and disinformation online. This keynote address will discuss the ways in which credibility has been studied in the disciplines of communication and psychology, including both how this notion has been conceptually and operationally defined. Key research findings will be presented with the aim of understanding what kinds of computational algorithms, tools, systems, and applications for tackling misinformation and disinformation are more versus less likely to be effective. This will help to answer a very important yet often overlooked question, which is: To what extent is misinformation a problem of information or a problem of human information processing? The answer to this question is crucial for both software developers and designers of educational intervention efforts to minimize the negative societal impacts of fabricated news and information flowing over the Internet and on social media. • Applied computing∼Psychology
Automated Fact Checking in the News Room
Authors: Sebastião Miranda, David Nogueira, Afonso Mendes, Andreas Vlachos, Andrew Secker, Rebecca Garrett, Jeff Mitchell, Zita Marinho
Venue: WWW
Year: 2019

Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation. In this paper, we present an automated fact checking platform which given a claim, it retrieves relevant textual evidence from a document collection, predicts whether each piece of evidence supports or refutes the claim, and returns a final verdict. We describe the architecture of the system and the user interface, focusing on the choices made to improve its user friendliness and transparency. We conduct a user study of the fact-checking platform in a journalistic setting: we integrated it with a collection of news articles and provide an evaluation of the platform using feedback from journalists in their workflow. We found that the predictions of our platform were correct 58% of the time, and 59% of the returned evidence was relevant.
Hack for Hire: Exploring the Emerging Market for Account Hijacking
Authors: Ariana Mirian, Joe DeBlasio, Stefan Savage, Geoffrey M. Voelker, Kurt Thomas
Venue: WWW
Year: 2019

Email accounts represent an enticing target for attackers, both for the information they contain and the root of trust they provide to other connected web services. While defense-in-depth approaches such as phishing detection, risk analysis, and two-factor authentication help to stem large-scale hijackings, targeted attacks remain a potent threat due to the customization and effort involved. In this paper, we study a segment of targeted attackers known as “hack for hire” services to understand the playbook that attackers use to gain access to victim accounts. Posing as buyers, we interacted with 27 English, Russian, and Chinese blackmarket services, only five of which succeeded in attacking synthetic (though realistic) identities we controlled. Attackers primarily relied on tailored phishing messages, with enough sophistication to bypass SMS two-factor authentication. However, despite the ability to successfully deliver account access, the market exhibited low volume, poor customer service, and had multiple scammers. As such, we surmise that retail email hijacking has yet to mature to the level of other criminal market segments.
Inferring Advertiser Sentiment in Online Articles using Wikipedia Footnotes
Authors: Shaunak Mishra, Aasish Pappu, Narayan Bhamidipati
Venue: WWW
Year: 2019

Online advertising platforms in partnerships with media companies typically have access to an online user’s history of viewed articles. If a concerned brand (advertiser) plans to run advertisement campaigns on users exposed to negative articles, it is essential to first identify articles with negative sentiment about the brand. For an advertising platform, scalable identification of such articles with little human-annotation effort is necessary for launching campaigns soon after an advertiser signs up. In this context, generic sentiment analysis tools suffer from the lack of contextual world knowledge associated with the advertiser. Human annotation of articles for supervised approaches is laborious and painstaking. To address these problems, we propose the use of publicly available Wikipedia footnote references for an advertiser, and propagate their sentiment to several articles related to the advertiser. In particular, our proposed approach has three components: (i) automatically find Wikipedia references which have negative sentiment about an advertiser, (ii) learn distributed representations (doc2vec) of article texts referred in footnotes and other unlabeled articles, and (iii) inferring sentiment in unlabeled articles using label propagation (from references) in the doc2vec space. Our experiments spanning three real brands, and data from a major advertising platform (Yahoo Gemini) show significant lifts in sentiment inference compared to existing baselines. In addition, we share valuable insights on how article sentiment influences the online activities of a user with respect to a brand.
Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory
Authors: Volodymyr Miz, Benjamin Ricaud, Kirell Benzi, Pierre Vandergheynst
Venue: WWW
Year: 2019

In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an anomaly as a localized increase in temporal activity in a cluster of nodes. The algorithm is unsupervised. It is able to detect and track anomalous activity in a dynamic network despite the noise from multiple interfering sources. We use the Hopfield network model of memory to combine the graph and time information. We show that anomalies can be spotted with good precision using a memory network. The presented approach is scalable and we provide a distributed implementation of the algorithm. To demonstrate its efficiency, we apply it to two datasets: Enron Email dataset and Wikipedia page views. We show that the anomalous spikes are triggered by the real-world events that impact the network dynamics. Besides, the structure of the clusters and the analysis of the time evolution associated with the detected events reveals interesting facts on how humans interact, exchange and search for information, opening the door to new quantitative studies on collective and social behavior on large and dynamic datasets.