OpenRaiser/Intern-Atlas huggingface dataset:
The semantic evolution of online communities
Authors: Matthew Rowe, Markus Strohmaier
Venue: WWW
Year: 2014

Despite their semantic-rich nature, online communities have, to date, largely been analysed through examining longitudinal changes in social networks, community uptake, or simple term-usage and language adoption. As a result, the evolution of communities on a semantic level, i.e. how concepts emerge, and how these concepts relate to previously discussed concepts, has largely been ignored. In this paper we present a graph-based exploration of the semantic evolution of online communities, thereby capturing dynamics of online communities on a conceptual level. We first examine how semantic graphs (concept graphs and entity graphs) of communities evolve, and then characterise such evolution using logistic population growth models. We demonstrate the value of such models by analysing how sample communities evolve and use our results to predict churn rates in community forums.
A web-based degree program in open source education: a case study
Authors: Sanjeev Kumar Saini, S. Senthil Anand, D. Arivudainambi, C. N. Krishnan
Venue: WWW
Year: 2014

In this paper, we describe the details of an interactive online web-based degree program in the area of Computer Science with specialization in Free/Open Source Software (FOSS) that has been successfully running for two years in a leading technological university in India. The subjects taught as well as the tools and platforms used in delivering the course are exclusively FOSS and put together by the university team, as described here. We also describe the details of the program, its goals and purpose, the manner of its implementation, the learnings we have had and the challenges being faced in going forward.
History-guided conversational recommendation
Authors: Yasser Salem, Jun Hong, Weiru Liu
Venue: WWW
Year: 2014

Product recommendation is an important aspect of many e-commerce systems. It provides an effective way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders. We present a new critiquing-based approach, History-Guided Recommendation (HGR), which is capable of using the recommendation pairs (item and critique) or critiques only so far in the current recommendation session to predict the most likely product recommendations and therefore short-cut the sometimes protracted recommendation sessions in standard critiquing approaches. The HGR approach shows a significant improvement in the interactions between the user and the recommender. It also enables successfully accepted recommendations to be made much earlier in the session.
iHUB: an information and collaborative management platform for life sciences
Authors: David E. Salt, Mourad Ouzzani, Eduard C. Dragut, Peter Baker, Srivathsava Rangarajan
Venue: WWW
Year: 2014

We describe ionomicshub, iHUB for short, a large scale cyber-infrastructure to support end-to-end research that aims to improve our understanding of how plants take up, transport and store their nutrient and toxic elements.
Publish data as time consistent web API with provenance
Authors: Miel Vander Sande, Pieter Colpaert, Tom De Nies, Erik Mannens, Rik Van de Walle
Venue: WWW
Year: 2014

Many organisations publish their data through a Web API. This stimulates use by Web applications, enabling reuse and enrichments. Recently, resource-oriented APIs are increasing in popularity because of their scalability. However, for organisations subject to data archiving, creating such an APIraises certain issues. Often, datasets are stored in different files and different formats. Therefore, tracking revisions is a challenging task and the API has to be custom built. Moreover, standard APIs only provide access to the current state of a resource. This creates time-based inconsistencies when they are combined. In this paper, we introduce an end-to-end solution for publishing a dataset as a time-based versioned REST API, with minimal input of the publisher. Furthermore, it publishes the provenance of each created resource. We propose a technology stack composed of prior work, which versions datasets, generates provenance, creates an API and adds Memento Datetime negotiation.
E-commerce product search: personalization, diversification, and beyond
Authors: Atish Das Sarma, Nish Parikh, Neel Sundaresan
Venue: WWW
Year: 2014

The focus of this tutorial will is e-commerce product search. Several challenges appear in this context, both from a research standpoint as well as an application standpoint. We present various approaches adopted in the industry, review well-known research techniques developed over the last decade, draw parallels to traditional web search highlighting the new challenges in this setting, and dig deep into some of the algorithmic and technical approaches developed. A specific approach that advances theoretical techniques and illustrates practical impact considered here is of identifying most suited results quickly from a large database. Settings span cold start users and advanced users for whom personalization is possible. In this context, top-$k$ and skylines are discussed as they form a key approach that spans the web, data mining, and database communities. These present powerful tools for search across multi-dimensional items with clear preferences within each attribute, like product search as opposed to regular web search.
The "expression gap": do you like what you share?
Authors: Atish Das Sarma, Si Si, Elizabeth F. Churchill, Neel Sundaresan
Venue: WWW
Year: 2014

While recommendation profiles increasingly leverage social actions such as "shares", the predictive significance of such actions is unclear. To what extent do public shares correlate with other online behaviors such as searches, views and purchases? Based on an analysis of 950,000 users' behavioral, transactional, and social sharing data on a global online commerce platform, we show that social "shares", or publicly posted expressions of interest do not correlate with non-public behaviors such as views and purchases. A key takeaway is that there is a "gap" between public and non-public actions online, suggesting that marketers and advertisers need to be cautious in their estimation of the significance of social sharing.
YouTube monetization: creating user-centric experiences using large scale data
Authors: Ramesh R. Sarukkai
Venue: WWW
Year: 2014

Over the last 4 years, YouTube has grown from a viral video sharing site to a platform that fuels a win-win ecosystem for video content creators, advertisers and users. A key driving force behind this successful transformation is building out products/platforms that focus and optimize for the user. In this talk, we will talk about user-centric efforts such as launch of user-controlled skippable ads (TrueView Instream), and Dynamic Ad Loads (machine learning system that balances realtime user impact with advertising policies). Leveraging very large amounts of real-time activity data is paramount to successfully building and deploying such user-centric models. We conclude the talk with challenges and opportunities in this important area of real-time user analysis and large data modeling.
On the predictability of recurring links in networks of face-to-face proximity
Authors: Christoph Scholz, Martin Atzmueller, Gerd Stumme
Venue: WWW
Year: 2014

This paper focuses on the predictability of recurring links: These links are generated repeatedly in a network for different forms of social ties, e.g. by face-to-face interactions in offline social networks. In particular, we analyse the predictability of recurring links in networks of face-to-face proximity using several path-based measures, and compare these to network-proximity measures based on the nodes' neighbourhood. Furthermore, we show that the current tie strength is a good predictor for this link prediction task. In addition we show that the removal of weak ties improves the predictability for most of the considered network proximity measures. For our analysis we utilize three real-world datasets collected at different scientific conferences using the Conferator (http://www.conferator.org) system.
Implicit feature detection for sentiment analysis
Authors: Kim Schouten, Flavius Frasincar
Venue: WWW
Year: 2014

Implicit feature detection is a promising research direction that has not seen much research yet. Based on previous work, where co-occurrences between notional words and explicit features are used to find implicit features, this research critically reviews its underlying assumptions and proposes a revised algorithm, that directly uses the co-occurrences between implicit features and notional words. The revision is shown to perform better than the original method, but both methods are shown to fail in a more realistic scenario.
Summarizing social image search results
Authors: Boon-Siew Seah, Sourav S. Bhowmick, Aixin Sun
Venue: WWW
Year: 2014

Most existing social image search engines present search results as a ranked list of images, which cannot be consumed by users in a natural and intuitive manner. Here, we present a novel algorithm that exploits both visual features and tags of the search results to generate high quality image search result summary. The summary not only breaks the results into visually and semantically coherent clusters, but it also maximizes the coverage of the original search results. We demonstrate the effectiveness of our method against state-of-the-art image summarization and clustering algorithms.
Analyzing behavioral data for improving search experience
Authors: Pavel Serdyukov
Venue: WWW
Year: 2014

Yandex is one of the largest internet companies in Europe, operating Russia's most popular search engine, generating 62\% of all search traffic in Russia, what means processing about 220 million queries from about 22 million users daily. Clearly, the amount and the variety of user behavioral data which we can monitor at search engines is rapidly increasing. Still, we do not always recognize its potential to help us solve the most challenging search problems and do not immediately know the ways to deal with it most effectively both for search quality evaluation and for its improvement. My talk will focus on various practical challenges arising from the need to "grok" search engine users and do something useful with the data they most generously, though almost unconsciously share with us. I will also present some answers to that by overviewing our latest research on user model based retrieval quality evaluation, implicit feedback mining and personalization. I will also summarize the experience we gained from organizing three data mining challenges at the series of workshops on using search click data (WSCD) organized in the scope of WSDM 2012 -- 2014 conferences. These challenges provided a unique opportunity to consolidate and scrutinize the work from search engines' industrial labs on analyzing behavioral data. Each year we publicly shared a fully anonymized dataset extracted from Yandex query logs and asked participants to predict editorial relevance labels of documents using search logs (in 2011), detect search engine switchings in search sessions (in 2012) and personalize web search using the long-term (user history based) and short-term (session-based) user context (in 2013).
Learning semantic representations using convolutional neural networks for web search
Authors: Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, Grégoire Mesnil
Venue: WWW
Year: 2014

This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn low-dimensional semantic vectors for search queries and Web documents. By using the convolution-max pooling operation, local contextual information at the word n-gram level is modeled first. Then, salient local fea-tures in a word sequence are combined to form a global feature vector. Finally, the high-level semantic information of the word sequence is extracted to form a global vector representation. The proposed models are trained on clickthrough data by maximizing the conditional likelihood of clicked documents given a query, us-ing stochastic gradient ascent. The new models are evaluated on a Web document ranking task using a large-scale, real-world data set. Results show that our model significantly outperforms other se-mantic models, which were state-of-the-art in retrieval performance prior to this work.
Defending against user identity linkage attack across multiple online social networks
Authors: Yilin Shen, Fengjiao Wang, Hongxia Jin
Venue: WWW
Year: 2014

We study the first countermeasure against user identity linkage attack across multiple online social networks (OSNs). Our goal is to keep as much as user's information in public and meanwhile prevent their identities from being linked on different OSNs via k-anonymity. We develop a novel greedy algorithm, incorporating an efficient manner to compute the greedy function, and validate it in terms of both solution quality and robustness using real-world datasets.
Trust and hybrid reasoning for ontological knowledge bases
Authors: Hui Shi, Kurt Maly, Steven J. Zeil
Venue: WWW
Year: 2014

Projects such as Libra and Cimple have built systems to capture knowledge in a research community and to respond to semantic queries. However, they lack the support for a knowledge base that can evolve over time while responding to queries requiring reasoning. We consider a semantic web that covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. We use ontologies to incorporate semantics to detect conflicts and resolve inconsistencies, and to infer new relations or proof statements with a reasoning engine. We consider a semantic web subject to changes in the knowledge base, the underlying ontology or the rule set that governs the reasoning. In this paper we explore the idea of trust where each change to the knowledge base is analyzed as to what subset of the knowledge base can still be trusted. We present algorithms that adapt the reasoner such that, when proving a goal, it does a simple retrieval when it encounters trusted items and backward chaining over untrusted items. We provide an evaluation of our proposed modifications that show that our algorithm is conservative and that it provides significant gains in performance for certain queries.
Beyond modeling private actions: predicting social shares
Authors: Si Si, Atish Das Sarma, Elizabeth F. Churchill, Neel Sundaresan
Venue: WWW
Year: 2014

We study the problem of predicting sharing behavior from e-commerce sites to friends on social networks via share widgets. The contextual variation in an action that is private (like rating a movie on Netflix), to one shared with friends online (like sharing an item on Facebook), to one that is completely public (like commenting on a Youtube video) introduces behavioral differences that pose interesting challenges. In this paper, we show that users' interests manifest in actions that spill across different types of channels such as sharing, browsing, and purchasing. This motivates leveraging all such signals available from the e-commerce platform. We show that carefully incorporating signals from these interactions significantly improves share prediction accuracy.
Modeling patient engagement in peer-to-peer healthcare
Authors: Elizabeth Sillence, Claire Hardy, Peter R. Harris, Pamela Briggs
Venue: WWW
Year: 2014

Patients now turn to other patients online for health information and advice in a phenomenon known as peer-to-peer healthcare. This paper describes a model of patients' peer-to-peer engagement, based upon qualitative studies of three patient or carer groups searching for online information and advice from their health peers. We describe a three-phase process through which patients engage with peer experience (PEx). In phase I (gating) patients determine the suitability and trustworthiness of the material they encounter; in phase II (engagement) they search out information, support and/or advice from others with similar or relevant experience; and in phase III (evaluation) they make judgments about the costs and benefits of engaging with particular websites in the longer term. This model provides a useful framework for understanding web based interactions in different patient groups.
Face recognition CAPTCHA made difficult
Authors: Terence Sim, Hossein Nejati, James Chua
Venue: WWW
Year: 2014

A CAPTCHA is a Turing test to distinguish human users from automated scripts to defend against internet adversarial attacks. As text-based CAPTCHAs (TBC) have become increasingly difficult to solve, image-based CAPTCHAs, and particularly face recognition CAPTCHAs (FRC), offer a chance to overcome TBC limitations. In this paper, we systematically design and implement a practical FRC, informed by psychological findings. We use gray-scale and binary images, which are computationally inexpensive to generate and deploy. Furthermore, our FRC complies with CAPTCHA design guidelines, thereby ensuring its robustness.
Zooniverse: observing the world's largest citizen science platform
Authors: Robert J. Simpson, Kevin R. Page, David De Roure
Venue: WWW
Year: 2014

This paper introduces the Zooniverse citizen science project and software framework, outlining its structure from an observatory perspective: both as an observable web-based system in itself, and as an example of a platform iteratively developed according to real-world deployment and used at scale. We include details of the technical architecture of Zooniverse, including the mechanisms for data gathering across the Zooniverse operation, access, and analysis. We consider the lessons that can be drawn from the experience of designing and running Zooniverse, and how this might inform development of other web observatories.
Understanding, leveraging and improving human navigation on the web
Authors: Philipp Singer
Venue: WWW
Year: 2014

Navigating websites represents a fundamental activity of users on the Web. Modeling this activity, i.e., understanding how predictable human navigation is and whether regularities can be detected has been of interest to researchers for nearly two decades. This is crucial for improving the Web experience of users by e.g., enhancing interfaces or information network structures. This thesis envisions to shedding light on human navigational patterns by trying to understand, leverage and improve human navigation on the Web. One main goal of this thesis is the construction of a versatile framework for modeling human navigational data with the use of Markov chains and for detecting the appropriate Markov chain order by using several advanced inference methods. It allows us to investigate memory and structure in human navigation patterns. Furthermore, we are interested in detecting whether pragmatic human navigational data can be leveraged by e.g., being useful for the task of calculating semantic relatedness between concepts. Finally, we want to find ways of enhancing human navigation models. Concretely, we plan on incorporating prior knowledge about the semantic relatedness between concepts to our Markov chain models as it is known that humans navigate the Web intuitively instead of randomly. Our experiments should be conducted on a variety of distinct navigational data including both goal oriented and free form navigation scenarios. We not only look at navigational paths over websites, but also abstract away to navigational paths over topics in order to get insights into cognitive patterns.
Network analysis of university courses
Authors: Ahmad Slim, Jarred Kozlick, Gregory L. Heileman, Jeff Wigdahl, Chaouki T. Abdallah
Venue: WWW
Year: 2014

Crucial courses have a high impact on students progress at universities and ultimately on graduation rates. Detecting such courses should therefore be a major focus of decision makers at universities. Based on complex network analysis and graph theory, this paper proposes a new framework to not only detect such courses, but also quantify their cruciality. The experimental results conducted using data from the University of New Mexico (UNM) show that the distribution of course cruciality follows a power law distribution. The results also show that the ten most crucial courses at UNM are all in mathematics. Applications of the proposed framework are extended to study the complexity of curricula within colleges, which leads to a consideration of the creation of optimal curricula. Optimal curricula along with the earned letter grades of the courses are further exploited to analyze the student progress. This work is important as it presents a robust framework to ensure the ease of flow of students through curricula with the goal of improving a university's graduation rate.
Strategic foundation of computational social science
Authors: Yunkyu Sohn
Venue: WWW
Year: 2014

For decades, scholars of various disciplines have been fretted over strategic interactions, presenting theoretical insights and empirical observations. Despite the central role played by strategic interactions in creating values in the Internet environment, our ability to understand them scientifically and to manage them in practice has remained limited. While engineering communities suffer from not having enough theoretical resource to formalize such phenomena, economics and social sciences lack adequate technology to properly operationalize their theoretical insights, thereby demanding an integrative solution. This project aims to develop a rational-choice-theory-driven framework for computational social science, focusing on social interactions on the Internet. In order to suggest theoretical foundations, validation of the predictions in a controlled environment, and verification of the results in actual platforms, general approaches and a few examples of ongoing research are presented.
Online behavioral genome sequencing from usage logs: decoding the search behaviors
Authors: Yang Song, Weiwei Cui, Shixia Liu, Kuansan Wang
Venue: WWW
Year: 2014

We present a system to analyze user interests by analyzing their online behaviors from large-scale usage logs. We surmise that user interests can be characterized by a large collection of features we call the behavioral genes that can be deduced from both their explicit and implicit online behaviors. It is the goal of this research to sequence the entire behavioral genome for online population, namely, to identify the pertinent behavioral genes and uncover their relationships in explaining and predicting user behaviors, so that high quality user profiles can be created and the online services can be better customized using these profiles. Within the scope of this paper, we demonstrate the work using the partial genome derived from web search logs. Our demo system is supported by an open access web service we are releasing and sharing with the research community. The main functions of the web service are: (1) calculating query similarities based on their lexical, temporal and semantic scores, (2) clustering a group of user queries into tasks with the same search and browse intent, and (3) inferring user topical interests by providing a probability distribution over a search taxonomy.
News from the crowd: grassroots and collaborative journalism in the digital age
Authors: Jochen Spangenberg, Nicolaus Heise
Venue: WWW
Year: 2014

Information content provided by members of the general public (mostly online) is playing an ever-increasing role in the detection, production and distribution of news. This paper investigates the concepts of (1) grassroots journalism and (2) collaborative journalism. It looks into similarities and differences and shows how both phenomena have been influenced by the emergence of the Internet and digital technologies. Then, the consequences for journalism in general will be analysed. Ultimately, strategies to meet the new challenges are suggested in order to maintain the quality and reliability of news coverage in the future.