OpenRaiser/Intern-Atlas huggingface dataset:
How to Assess and Rank User-Generated Content on Web
Authors: Elaheh Momeni, Claire Cardie, Nicholas Diakopoulos
Venue: WWW
Year: 2018

User-generated content (UGC) on the Web, especially on social media platforms, facilitates the association of additional information with digital resources and online social topics and it can provide valuable supplementary content. However, UGC varies in quality and, consequently, raises the challenge of how to maximize its utility for a variety of end-users, in particular in the age of misinformation. This study aims to provide researchers and Web data curators with answers to the following questions: (1) What are the existing approaches and methods for assessing and ranking UGC (2) What features and metrics have been used successfully to assess and predict UGC value across a range of application domains This survey is composed of a systematic review of approaches for assessing and ranking UGC: results obtained by identifying and comparing methodologies within the context of short text-based UGC on the Web. This survey categorizes existing assessment and ranking approaches into four framework types and discusses the main contributions and considerations of each type. Furthermore, it suggests a need for further experimentation and encourages the development of new approaches for the assessment and ranking.
Text-Enriched Representations for News Image Classification
Authors: Elias Moons, Tinne Tuytelaars, Marie-Francine Moens
Venue: WWW
Year: 2018

Images have a prominent role in the communication of news on the Web. We propose a novel method for image classification with subject categories when limited annotated images are available for training the classifier. A neural network based encoder learns image representations from paired news images and their texts. Once trained, this encoder transforms any image to a text-enriched representation of the image, which is then used as input for the classifier that categorizes an image according to its subject category. We have trained classifiers with different amounts of annotated images and found that the image classifier that uses the text-enriched image representations outperforms a baseline model that only uses image features especially in cases with limited training examples.
Mind Your Credit: Assessing the Health of the Ripple Credit Network
Authors: Pedro Moreno-Sanchez, Navin Modi, Raghuvir Songhela, Aniket Kate, Sonia Fahmy
Venue: WWW
Year: 2018

The Ripple credit network has emerged as a payment backbone with key advantages for financial institutions and the remittance industry. Its path-based IOweYou (IOU) settlements across different (crypto)currencies conceptually distinguishes the Ripple blockchain from cryptocurrencies (such as Bitcoin and altcoins), and makes it highly suitable to an orthogonal yet vast set of applications in the remittance world for cross-border transactions and beyond. This work studies the structure and evolution of the Ripple network since its inception, and investigates its vulnerability to devilry attacks that affect the IOU credit of linnet users» wallets. We find that about 13M USD are at risk in the current Ripple network due to inappropriate configuration of the rippling flag on credit links, facilitating undesired redistribution of credit across those links. Although the Ripple network has grown around a few highly connected hub (gateway) wallets that constitute the core of the network and provide high liquidity to users, such a credit link distribution results in a user base of around 112,000 wallets that can be financially isolated by as few as 10 highly connected gateway wallets. Indeed, today about 4.9M USD cannot be withdrawn by their owners from the Ripple network due to PayRoutes, a gateway tagged as faulty by the Ripple community. Finally, we observe that stale exchange offers pose a real problem, and exchanges (market makers) have not always been vigilant about periodically updating their exchange offers according to current real-world exchange rates. For example, stale offers were used by 84 Ripple wallets to gain more than 4.5M USD from mid-July to mid-August 2017. Our findings should prompt the Ripple community to improve the health of the network by educating its users on increasing their connectivity, and by appropriately maintaining the credit limits, rippling flags, and exchange offers on their IOU credit links.
Predicting Argumentative Influence Probabilities in Large-Scale Online Civic Engagement
Authors: Gaku Morio, Katsuhide Fujita
Venue: WWW
Year: 2018

Large-scale online civic engagements (OCEs) with more than 100 participants have become possible due to recent developments in online social media technology. OCEs have the potential to achieve consensus building and collective decision-making with a large number of citizens, which is difficult to achieve in face-to-face contexts. However, most users in a large-scale OCE are rarely constantly active. Therefore, an important problem for the activation of a large-scale OCE is to facilitate the discussion by predicting which citizens will have significant influence in the discussion. This paper examines the activation prediction problem in a large-scale OCE. We propose a novel influence model based on the impulse response of activity histories and argumentative pressures, as well as an effective testing algorithm. The experimental results demonstrate that the proposed models with impulse response and the lexical pressures show better accuracy compared with baselines. In addition, the testing time required by the proposed method can be reduced significantly by employing a node-cutting algorithm.
From Alt-Right to Alt-Rechts: Twitter Analysis of the 2017 German Federal Election
Authors: Fred Morstatter, Yunqiu Shao, Aram Galstyan, Shanika Karunasekera
Venue: WWW
Year: 2018

In the 2017 German Federal elections, the "Alternative for Deutschland'', or AfD, party was able to take control of many seats in German parliament. Their success was credited, in part, to their large online presence. Like other "alt-right'' organizations worldwide, this party is tech savvy, generating a large social media footprint, especially on Twitter, which provides an ample opportunity to understand their online behavior. In this work we present an analysis of Twitter data related to the aforementioned election. We show how users self-organize into communities, and identify the themes that define those communities. Next we analyze the content generated by those communities, and the extent to which these communities interact. Despite these elections being held in Germany, we note a substantial impact from the English-speaking Twittersphere. Specifically, we note that many of these accounts appear to be from the American alt-right movement, and support the German alt-right movement.
Flood Relevance Estimation from Visual and Textual Content in Social Media Streams
Authors: Anastasia Moumtzidou, Stelios Andreadis, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris
Venue: WWW
Year: 2018

Disaster monitoring based on social media posts has raised a lot of interest in the domain of computer science the last decade, mainly due to the wide area of applications in public safety and security and due to the pervasiveness not solely on daily communication but also in life-threating situations. Social media can be used as a valuable source for producing early warnings of eminent disasters. This paper presents a framework to analyse social media multimodal content, in order to decide if the content is relevant to flooding. This is very important since it enhances the crisis situational awareness and supports various crisis management procedures such as preparedness. Evaluation on a benchmark dataset shows very good performance in both text and image classification modules.
Smart Cities at Risk!: Privacy and Security Borderlines from Social Networking in Cities
Authors: Vaia Moustaka, Zenonas Theodosiou, Athena Vakali, Anastasis Kounoudes
Venue: WWW
Year: 2018

As smart cities infrastructures mature, data becomes a valuable asset which can radically improve city services and tools. Registration, acquisition and utilization of data, which will be transformed into smart services, are becoming more necessary than ever. Online social networks with their enormous momentum are one of the main sources of urban data offering heterogeneous real-time data at a minimal cost. However, various types of attacks often appear on them, which risk users' privacy and affect their online trust. The purpose of this article is to investigate how risks on online social networks affect smart cities and study the differences between privacy and security threats with regard to smart people and smart living dimensions.
Lifecycle-Based Event Detection from Microblogs
Authors: Lin Mu, Peiquan Jin, Lizhou Zheng, En-Hong Chen, Lihua Yue
Venue: WWW
Year: 2018

Microblog like Twitter and Sina Weibo has been an important information source for event detection and monitoring. In many decision-making scenarios, it is not enough to only provide a structural tuple for an event, e.g., a 5W1H record like <who, where, when, what, whom, how>. However, in addition to event structural tuples, people need to know the evolution lifecycle of an event. The lifecycle description of an event is more helpful for decision making because people can focus on the progress and trend of events. In this paper, we propose a novel method for efficiently detecting and tracking event evolution on microblogging platforms. The major features of our study are: (1) It provides a novel event-type-driven method to extract event tuples, which forms the foundation for event evolution analysis. (2) It describes the lifecycle of an event by a staged model, and provides effective algorithms for detecting the stages of an event. (3) It offers emotional analysis over the stages of an event, through which people are able to know the public emotional tendency over a specific event at different time periods. We build a prototype system and present its architecture and implemental details in the paper. In addition, we conduct experiments on real microblog datasets and the results in terms of precision, recall, and F-measure suggest the effectiveness and efficiency of our proposal.
Reasoning on Data Workshop Chairs' Welcome and Organization
Authors: Marie-Laure Mugnier, Catherine Roussey, Pierre Senellart
Venue: WWW
Year: 2018

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Minimizing Polarization and Disagreement in Social Networks
Authors: Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis
Venue: WWW
Year: 2018

The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: \beginquotation \noindent Given n agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes \em disagreementand \em controversy simultaneously? \endquotation \noindent This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the others viewpoint of the world, and decrease overall levels of polarization and controversy? Such decisions have an important global effect on society \citewilliams2007social. Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a graph with $O(n/ε^2)$ edges that is a $(1+ε)$ approximation to the optimum. Our formulation is an instance of optimization over \em graph topologies, see also \citeboyd2004fastest,daitch2009fitting,sun2006fastest. Furthermore, for a given graph, we show how to optimize the same objective over the agents» innate opinions in polynomial time. Finally, we perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both controversy and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a reduction in controversy and disagreement of the order $6.2 \times 10^4$.
CEDAR: Semantic Web Technology to Support Open Science
Authors: Mark A. Musen, Susanna-Assunta Sansone, Kei-Hoi Cheung, Steven H. Kleinstein, Morgan Crafts, Stephan C. Schürer, John B. Graybeal
Venue: WWW
Year: 2018

There is an expectation that scientists will archive their experimental data online in public repositories to enable other investigators to verify their work and to re-explore their data in search of new discoveries. When left to their own devices, however, scientists do a poor job creating the metadata that describe their datasets. A lack of standardization makes it difficult for other investigators to find relevant datasets and to perform secondary analyses. The Center for Expanded Data Annotation and Retrieval (CEDAR) was founded with the goal of enhancing the authoring of experimental metadata to make online datasets more useful to the scientific community. CEDAR technology includes Web-based methods for creating and managing libraries of templates for representing metadata. CEDAR's templates interoperate with a repository of scientific ontologies to standardize the way in which the templates may be filled out. Collaborations with several major research projects are allowing us to explore how CEDAR may ease access to scientific data sets stored in public repositories.
Stop the KillFies! Using Deep Learning Models to Identify Dangerous Selfies
Authors: Vedant Nanda, Hemank Lamba, Divyansh Agarwal, Megha Arora, Niharika Sachdeva, Ponnurangam Kumaraguru
Venue: WWW
Year: 2018

Selfies have become a prominent medium for self-portrayal on social media. Unfortunately, certain social media users go to extreme lengths to click selfies, which puts their lives at risk. Two hundred and sixteen individuals have died since March 2014 until January 2018 while trying to click selfies. It is imperative to be able to identify dangerous selfies posted on social media platforms to be able to build an intervention for users going to extreme lengths for clicking such selfies. In this work, we propose a convolutional neural network based classifier to identify dangerous selfies posted on social media using only the image (no metadata). We show that our proposed approach gives an accuracy of $98%$ and performs better than previous methods.
Real-time Detection of Content Polluters in Partially Observable Twitter Networks
Authors: Mehwish Nasim, Andrew Nguyen, Nick Lothian, Robert Cope, Lewis Mitchell
Venue: WWW
Year: 2018

Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset.
Low Rank Spectral Network Alignment
Authors: Huda Nassar, Nate Veldt, Shahin Mohammadi, Ananth Grama, David F. Gleich
Venue: WWW
Year: 2018

Network alignment or graph matching is the classic problem of finding matching vertices between two graphs with applications in network de-anonymization and bioinformatics. There exist a wide variety of algorithms for it, but a challenging scenario for all of the algorithms is aligning two networks without any information about which nodes might be good matches. In this case, the vast majority of principled algorithms demand quadratic memory in the size of the graphs. We show that one such method---the recently proposed and theoretically grounded EigenAlign algorithm---admits a novel implementation which requires memory that is linear in the size of the graphs. The key step to this insight is identifying low-rank structure in the node-similarity matrix used by EigenAlign for determining matches. With an exact, closed-form low-rank structure, we then solve a maximum weight bipartite matching problem on that low-rank matrix to produce the matching between the graphs. For this task, we show a new, a-posteriori, approximation bound for a simple algorithm to approximate a maximum weight bipartite matching problem on a low-rank matrix. The combination of our two new methods then enables us to tackle much larger network alignment problems than previously possible and to do so quickly. Problems that take hours with existing methods take only seconds with our new algorithm. We thoroughly validate our low-rank algorithm against the original EigenAlign approach. We also compare a variety of existing algorithms on problems in bioinformatics and social networks. Our approach can also be combined with existing algorithms to improve their performance and speed.
Search, Filter, Fork, and Link Open Data: The ADEQUATe platform: data- and community-driven quality improvements
Authors: Sebastian Neumaier, Lörinc Thurnay, Thomas J. Lampoltshammer, Tomás Knap
Venue: WWW
Year: 2018

The present work describes the ADEQUATe platform: a framework to monitor the quality of (Governmental) Open Data catalogs, to re-publish improved and linked versions of the datasets and their respective metadata descriptions, and to include the community in the quality improvement process. The information acquired by the linking and (meta)data improvement steps is then integrated in a semantic search engine. In the paper, we first describe the requirements of the platform, which are based on focus group interviews and a web-based survey. Second, we use these requirements to formulate the goals and show the architecture of the overall platform, and third, we showcase the potential and relevance of the platform to resolve the requirements by describing exemplary user journeys exploring the system. The platform is available at: https://www.adequate.at/
A Better Facet of Dynamic Information Flow Control
Authors: Minh Ngo, Nataliia Bielova, Cormac Flanagan, Tamara Rezk, Alejandro Russo, Thomas Schmitz
Venue: WWW
Year: 2018

Multiple Facets (MF) is a dynamic enforcement mechanism which has proved to be a good fit for implementing information flow security for JavaScript. It relies on multi executing the program, once per each security level or view, to achieve soundness. By looking inside programs, MF encodes the views to reduce the number of needed multi-executions. In this work, we extend Multiple Facets in three directions. First, we propose a new version of MF for arbitrary lattices, called Generalised Multiple Facets, or GMF. GMF strictly generalizes MF, which was originally proposed for a specific lattice of principals. Second, we propose a new optimization on top of GMF that further reduces the number of executions. Third, we strengthen the security guarantees provided by Multiple Facets by proposing a termination sensitive version that eliminates covert channels due to termination.
Continuous-Time Dynamic Network Embeddings
Authors: Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim
Venue: WWW
Year: 2018

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Although many networks contain this type of temporal information, the majority of research in network representation learning has focused on static snapshots of the graph and has largely ignored the temporal dynamics of the network. In this work, we describe a general framework for incorporating temporal information into network embedding methods. The framework gives rise to methods for learning time-respecting embeddings from continuous-time dynamic networks. Overall, the experiments demonstrate the effectiveness of the proposed framework and dynamic network embedding approach as it achieves an average gain of 11.9% across all methods and graphs. The results indicate that modeling temporal dependencies in graphs is important for learning appropriate and meaningful network representations.
Co-Regularized Deep Multi-Network Embedding
Authors: Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang
Venue: WWW
Year: 2018

Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, multiple networks may contain complementary information, which can lead to further refined node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is flexible. It allows different networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function. With multiple layers of non-linear mappings, DMNE progressively transforms each input network to a highly non-linear latent space, and in the meantime, adapts different spaces to each other through a co-regularized learning schema. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.
Attribution Inference for Digital Advertising using Inhomogeneous Poisson Models
Authors: Zachary Nichols, Adam Stein
Venue: WWW
Year: 2018

Measuring the causal effect of advertising on driving desired behavior is an important problem to the digital publishing industry (the "attribution" problem). It is common to use observational methods for attribution, due to the high cost and difficulty of employing randomized controlled trials (RCTs). However, recent results have shown that even current sophisticated observational methods may be inaccurate, yielding incorrect estimates of the true effect of advertising. Here, we present a new observational attribution method based on a successful model of neural spiking that learns the temporal interactions between event-based time series. We train this model on data from several RCT marketing experiments, and show that it can accurately recover the true causal attribution.