OpenRaiser/Intern-Atlas huggingface dataset:
Analyzing a User's Contributive Social Capital Based on Acitivities in Online Social Networks and Media
Authors: Sebastian Schams, Jan Hauffa, Georg Groh
Venue: WWW
Year: 2018

To improve the quality of communication in Online Social Networks and Media (OSNEM), we envision a system that models a person's contributive social capital (CSC), which encompasses their competence, trustworthiness, and social responsibility. Having the CSC score available may inspire social behavior and mutual support. The system is based on three pillars: the analysis of OSNEM activity, interactions in virtual social capital market systems, and personal endorsements. In this paper we present our investigations regarding the first pillar. To obtain a dataset, we ran an experiment where 165 participants interacted on a custom social networking platform and assessed each other. Ground truth data was derived from these assessments. The dataset shows characteristics that are similar to larger OSNs. With different machine learning algorithms we investigated the hypothesis that contributive social capital can be extracted from network properties and networking activity, which were assessed with features such as the number of contributions of each participant. The prediction of contributive social capital showed an improvement over the baseline. A ranking of the participants following their predicted CSC scores showed a moderate correlation with the ranking according to the ground truth assessment. We also investigated the relative importance of the features for the analysis, and the effect of excluding inactive users to better understand network dynamics on a micro level. The selected features are also available in most other OSNEM platforms, like Facebook and Twitter. This allows a large-scale application of our investigations.
The Effects of Real-world Events on Music Listening Behavior: An Intervention Time Series Analysis
Authors: Markus Schedl, Eelco Wiechert, Christine Bauer
Venue: WWW
Year: 2018

We approach the research question whether real-world events, such as sport events or product launches, influence music consumption behavior. To this end, we consider events of different categories from Google Trends and model listening events as time series using Last.fm data. Performing an auto-regressive integrated moving average analysis to decompose the signal and subsequently an intervention time series analysis, we find significant signal discontinuities, in particular for the Google news category. We found that news and events are likely to increase the number of songs listened to per person per day by about 2%, while tech events commonly cause 1% less music being consumed.
Improving Word Embedding Compositionality using Lexicographic Definitions
Authors: Thijs Scheepers, Evangelos Kanoulas, Efstratios Gavves
Venue: WWW
Year: 2018

We present an in-depth analysis of four popular word embeddings (Word2Vec, GloVe, fastText and Paragram) in terms of their semantic compositionality. In addition, we propose a method to tune these embeddings towards better compositionality. We find that training the existing embeddings to compose lexicographic definitions improves their performance in this task significantly, while also getting similar or better performance in both word similarity and sentence embedding evaluations. Our method tunes word embeddings using a simple neural network architecture with definitions and lemmas from WordNet. Since dictionary definitions are semantically similar to their associated lemmas, they are the ideal candidate for our tuning method, as well as evaluating for compositionality. Our architecture allows for the embeddings to be composed using simple arithmetic operations, which makes these embeddings specifically suitable for production applications such as web search and data mining. We also explore more elaborate and involved compositional models. In our analysis, we evaluate original embeddings, as well as tuned embeddings, using existing word similarity and sentence embedding evaluation methods. Aside from these evaluation methods used in related work, we also evaluate embeddings using a ranking method which tests composed vectors using the lexicographic definitions already mentioned. In contrast to other evaluation methods, ours is not invariant to the magnitude of the embedding vector, which we show is important for composition. We consider this new evaluation method, called CompVecEval, to be a key contribution.
Leveraging Social Media Signals for Record Linkage
Authors: Andrew T. Schneider, Arjun Mukherjee, Eduard C. Dragut
Venue: WWW
Year: 2018

Many data-intensive applications collect (structured) data from a variety of sources. A key task in this process is record linkage, which is the problem of determining the records from these sources that refer to the same real-world entities. Traditional approaches use the record representation of entities to accomplish this task. With the nascence of social media, entities on the Web are now accompanied by user generated content. We present a method for record linkage that uses this hitherto untapped source of entity information. We use document-based distances, with an emphasis on word embedding document distances, to determine if two entities match. Our rationale is that user evaluations of entities converge in semantic content, and hence in the word embedded space, as the number of user evaluations grows. We analyze the effectiveness of the proposed method both as a stand-alone method and in combination with record-based record linkage methods. Experimental results using real-world reviews demonstrate the high effectiveness of our approach. To our knowledge, this is the first work exploring the use of user generated content accompanying entities in the record linkage task.
Linked Data for Production (LD4P): a Multi-Institutional Approach to Technical Services Transformation
Authors: Philip E. Schreur
Venue: WWW
Year: 2018

Linked Data for Production (LD4P) is a collaboration between six institutions (Columbia, Cornell, Harvard, Library of Congress, Princeton, and Stanford) to begin the transition of technical services production workflows from a series of library-centric data formats (MARC) to ones based in Linked Open Data (LOD). This first phase of the transition focuses on the development of the ability to produce metadata as LOD communally, the enhancement of the BIBFRAME ontology to encompass the multiple resource formats that academic libraries must process, and the engagement of the broader academic library community to ensure a sustainable and extensible environment. As its name implies, LD4P focuses on the immediate needs of metadata production such as ontology coverage and workflow transition. The LD4P partners' work will be based, in part, on a collection of tools that currently exist, such as those developed by the Library of Congress. The cyclical feedback of use and enhancement request to the developers of these tools will allow for their enhancement based on use in an actual production environment. The six institutions involved will focus on materials ranging from art to rare books, from cartographic materials to music, from annotations to workflows. Tool development and enhancement will also be a key aspect of the project. By the end of the first phase of this project (Spring 2018), the partners will have the minimal tooling, workflows, and standards developed to begin the transformation from MARC to LOD in Phase 2 of the project.
MMM: May I Mine Your Mind
Authors: Diego Sempreboni, Luca Viganò
Venue: WWW
Year: 2018

Consider the following set-up for the plot of a possible future episode of the TV series Black Mirror: human brains can be connected directly to the net and MiningMind Inc. has developed a technology that merges a reward system with a cryptojacking engine that uses the human brain to mine cryptocurrency (or to carry out some other mining activity). Part of our brain will be committed to cryptographic calculations (mining), leaving the remaining part untouched for everyday operations, i.e., for our brain's normal daily activity. In this short paper, we briefly argue why this set-up might not be so far fetched after all, and explore the impact that such a technology could have on our lives and our society.
FIESTAIoT Project: Federated Interoperable Semantic IoT/cloud Testbeds and Applications
Authors: Martin Serrano, Amelie Gyrard, Elias Z. Tragos, Hung Nguyen Dang
Venue: WWW
Year: 2018

FIESTA-IoT project provides a blueprint experimental infrastructure, software tools, semantic techniques, certification processes and best practices enabling IoT testbed/platforms to interconnect their facility's resources in an interoperable semantic way. FIESTA-IoT project enables the integration of IoT platform's resources, testbeds infrastructure and their associated applications. FIESTA-IoT opens up new opportunities in the development and deployment of experiments using data from IoT testbeds. The FIESTA-IoT infrastructure enables experimenters to use a single EaaS API (i.e. the FIESTA-IoT EaaS API) for executing experiments over multiple IoT federated testbeds in a testbed agnostic way i.e. like accessing a single large scale virtualized testbed. The main goal of the FIESTA-IoT project is to open new horizons in the development and deployment of IoT applications and experiments at a EU (and global) scale, based on the interconnection and interoperability of diverse IoT platforms and testbeds. FIESTA-IoT project's experimental infrastructure provides to the European experimenters in the IoT domain with the unique capability for accessing and sharing IoT semantically annotated datasets in a testbed-agnostic way. FIESTA-IoT enables execution of experiments across multiple IoT testbeds, based on a single API for submitting the experiment and a single set of credentials for the researcher and the portability of IoT experiments across different testbeds and the provision of interoperable standards-based IoT/cloud interfaces over diverse IoT experimental facilities.
Web Programming Chairs' Welcome & Organization
Authors: Manuel Serrano, Sukyoung Ryu
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Web Programming alternate track. We received 14 proposals from all around the world covering a broad range of topics. We evaluated them regarding relevance, quality, and novelty, selecting 1 full-day talks.
Investigating Similarity of Nodes' Attributes in Topological Based Communities
Authors: Rajesh Sharma, Danilo Montesi
Venue: WWW
Year: 2018

One of the important problems in the domain of network science is the community detection. In the past, various topological based community detection algorithms have been proposed. Recently, researchers have taken into account at- tributes of the nodes while proposing community detection algorithms. In this work, we investigate if the nodes in a community, identified through topology based algorithms al- so exhibit attribute similarity. Using four different kinds of similarity metrics, we analyse the attribute similarity of the nodes within the communities derived using five different types of topological based community detection algorithms. Based on our analysis of three real social network datasets, we found on an average of 50% attribute similarity among the nodes in the communities.
Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations
Authors: Tian Shi, Kyeongpil Kang, Jaegul Choo, Chandan K. Reddy
Venue: WWW
Year: 2018

Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate descent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various real-world short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.
Visualizing the Flow of Discourse with a Concept Ontology
Authors: Baoxu Shi, Tim Weninger
Venue: WWW
Year: 2018

Understanding and visualizing human discourse has long being a challenging task. Although recent work on argument mining have shown success in classifying the role of various sentences, the task of recognizing concepts and understanding the ways in which they are discussed remains challenging. Given an email thread or a transcript of a group discussion, our task is to extract the relevant concepts and understand how they are referenced and re-referenced throughout the discussion. In the present work, we present a preliminary approach for extracting and visualizing group discourse by adapting Wikipedia's category hierarchy to be an external concept ontology. From a user study, we found that our method achieved better results than 4 strong alternative approaches, and we illustrate our visualization method based on the extracted discourse flows.
Aspect-based Financial Sentiment Analysis with Deep Neural Networks
Authors: E. Shijia, Li Yang, Mohan Zhang, Yang Xiang
Venue: WWW
Year: 2018

Aspect-based financial sentiment analysis, which aims to classify the text instance into a pre-defined aspect class and predict the sentiment score for the mentioned target. In this paper, we propose a neural network model, Attention-based LSTM model with the Aspect information (ALA), to solve the financial opinion mining problem introduced by the WWW 2018 shared task. The proposed neural network model can adapt to the financial dataset so that the neural network can effectively understand the semantic information of the short text. We evaluate our model with the 10-fold cross-validation, and we compare our model with a variety of related deep neural network models.
Discovering Progression Stages in Trillion-Scale Behavior Logs
Authors: Kijung Shin, Mahdi Shafiei, Myunghwan Kim, Aastha Jain, Hema Raghavan
Venue: WWW
Year: 2018

User engagement is a key factor for the success of web services. Studying the following questions will help establishing business strategies leading to their success: How do the behaviors of users in a web service evolve over time? To reach a certain engagement level, what are the common stages that many users go through? How can we represent the stage that each individual user lies in? To answer these questions, we propose a behavior model that discovers the progressions of users' behaviors from a given starting point - such as a new subscription or first experience of certain features - to a particular target stage such as a predefined engagement level of interest. Under our model, transitions over stages represent progression of users where each stage in our model is characterized by probability distributions over types of actions, frequencies of actions, and next stages to move. Each user performs actions and moves to a next stage following the probability distributions characterizing the current stage. We also develop a fast and memory-efficient algorithm that fits our model to trillions of behavioral logs. Our algorithm scales linearly with the size of data. Especially, its distributed version implemented in the MapReduce framework successfully handles petabyte-scale data with one trillion actions. Lastly, we show the effectiveness of our model and algorithm by applying them to real-world data from LinkedIn. We discover meaningful stages that LinkedIn users go through leading to predefined target goals. In addition, our trained models are shown to be useful for downstream tasks such as prediction of future actions.
Training 100, 000 Classes on a Single Titan X in 7 Hours or 15 Minutes with 25 Titan Xs
Authors: Anshumali Shrivastava
Venue: WWW
Year: 2018

In this talk, I will present Merged-Averaged Classifiers via Hashing (MACH) for K-classification with ultra-large values of K. Compared to traditional one-vs-all classifiers that require $O(Kd)$ memory and inference cost, MACH only need $O(dłogK)$ (d is dimensionality) memory while only requiring $O(KłogK + dłogK )$ operation for inference. MACH is a generic K-classification algorithm, with provably theoretical guarantees, without any assumption on the relationship between classes. MACH uses universal hashing to reduce classification with a large number of classes to few (logarithmic many) independent classification tasks with small (constant) number of classes. I will show the first quantification of discriminability-memory tradeoff in multi-class classification. Using the simple idea of hashing, we can train ODP dataset with 100,000 classes and 400,000 features on a single Titan X GPU, with the classification accuracy of 19.28%, which is the best-reported accuracy on this dataset. Before this work, the best performing baseline is a one-vs-all classifier that requires 40 billion parameters (160 GB model size) and achieves 9% accuracy. In contrast, MACH can achieve 9% accuracy with 480x reduction in the model size (of mere 0.3GB). With MACH, we also demonstrate complete training of feature extracted fine-grained imagenet dataset (compressed size 104GB), with 21,000 classes, on a single GPU. To the best of our knowledge, this is the first work to demonstrate complete training of these extreme-class datasets on a single Titan X. Furthermore, the algorithm is trivially parallelizable. Our experiments show that we can train ODP datasets in 7 hours on a single GPU or in 15 minutes with 25 GPUs. Similarly, we can train classifiers over the fine-grained imagenet dataset in 24 hours on a single GPU which can be reduced to little over 1 hour with 20 GPUs.
Four-Dimensional Shopping Mall: Sequential Group Willingness Optimization under VR Environments
Authors: Hong-Han Shuai, Yueh-Hsue Li, Chun-Chieh Feng, Wen-Chih Peng
Venue: WWW
Year: 2018

Nowadays, people get used to buying a variety of commodities from e-commerce platforms because of the convenience and easy access to Internet. As a result, the sales on e-commerce platforms have grown exponentially. It is promising to customize the online shops for different users under VR environments since users do not need to waste time of going upstairs or downstairs for finding interesting commodities. Therefore, in this demo paper, we propose a novel four-dimensional shopping mall that offers online group shopping services with the customized shop recommendation, where a group of friends in the proposed four-dimensional shopping mall can teleport to the next shop by one click. We formulate a Sequential group wIllingness OptimizatioN (SION) problem, prove SION is NP-hard, and provide an efficient algorithm called ζ-GSS. The experimental results show that the solution quality of the proposed ζ-GSS is close to the optimal solution, while the execution time only requires 3.3%. Finally, we build the prototype of the four-dimensional shopping mall, which can be demonstrated for users to experience the next-generation online shopping.
Spectral Algorithms for Temporal Graph Cuts
Authors: Arlei Silva, Ambuj K. Singh, Ananthram Swami
Venue: WWW
Year: 2018

The sparsest cut problem consists of identifying a small set of edges that breaks the graph into balanced sets of vertices. The normalized cut problem balances the total degree, instead of the size, of the resulting sets. Applications of graph cuts include community detection and computer vision. However, cut problems were originally proposed for static graphs, an assumption that does not hold in many modern applications where graphs are highly dynamic. In this paper, we introduce sparsest and normalized cuts in temporal graphs, which generalize their standard definitions by enforcing the smoothness of cuts over time. We propose novel formulations and algorithms for computing temporal cuts using spectral graph theory, divide-and-conquer and low-rank matrix approximation. Furthermore, we extend temporal cuts to dynamic graph signals, where vertices have attributes. Experiments show that our solutions are accurate and scalable, enabling the discovery of dynamic communities and the analysis of dynamic graph processes.
Loops of Humans and Bots in Wikidata
Authors: Elena Simperl
Venue: WWW
Year: 2018

Wikidata is one of most successful knowledge graphs ever created. It expresses knowledge in the form of subject-property-value statements accompanied by provenance information. A project of the Wikimedia Foundation, Wikidata is supported by a community of currently 19 thousand active users and 234 bots, who together are responsible for editing more than 45 million entities since the start of the project in 2012. This makes Wikidata a prime example for what human-in-the-loop technology can achieve. In this talk, we are going to present several studies that aim to understand the links between its socio-technical fabric and its success.
Strategies for Geographical Scoping and Improving a Gazetteer
Authors: Sanket Kumar Singh, Davood Rafiei
Venue: WWW
Year: 2018

Many applications that use geographical databases (a.k.a. gazetteers) rely on the accuracy of the information in the database. However, poor data quality is an issue when data is integrated from multiple sources with different quality constraints and sometimes with little information about the sources. One major consequence of this is that the geographical scope of a location and/or its position may not be known or may not be accurate. In this paper, we study the problem of detecting the scope of locations in a geographical database and its applications in identifying inconsistencies and improving the quality of a gazetteer. We develop novel strategies, including probabilistic and geometric approaches, to accurately derive the geographical scope of places based on the spatial hierarchy of a gazetteer as well as other public information (such as area) that may be available. We show how the boundary information derived here can be useful in identifying inconsistencies, enhancing the location hierarchy and improving the applications that rely on gazetteers. Our experimental evaluation on two public-domain gazetteers reveals that the proposed approaches significantly outperform, in terms of the accuracy of the geographical bounding boxes, a baseline that is based on the parent-child relationship of a gazetteer. Among applications, we show that the boundary information derived here can move more than 20% of locations in a public gazetteer to better positions in the hierarchy and that the accuracy of those moves is over 90%.