Google Award Program stimulates Journalism and CS collaboration

Posted by Krishna Bharat, Distinguished Research Scientist

Last fall, Google invited academic researchers to participate in a Computational Journalism awards program focused on the intersection of Computer Science and Journalism. We solicited proposals for original research projects relevant to today’s fast evolving news industry.

As technology continues to shape and be shaped by the media landscape, applicants were asked to rethink traditional models and roles in the ecosystem, and reimagine the lifecycle of the news story in the online world. We encouraged them to develop innovative tools and open source software that could benefit readers and be game-changers for reporters and publishers. Each award includes funding of $60,000 in cash and $20,000 in computing credits on Google’s Cloud Platform.

We congratulate the recipients of these awards, whose projects are described below, and look forward to the results of their research. Stay tuned for updates on their progress.

Larry Birnbaum, Professor of Electrical Engineering and Computer Science, and Journalism, Northwestern University
Project: Thematic Characterization of News Stories
This project aims to develop computational methods for identifying abstract themes or "angles" in news stories, e.g., seeing a story as an instance of "pulling yourself up by your bootstraps," or as a "David vs. Goliath" story. In collaboration with journalism and computer science students, we will develop applications utilizing these methods in the creation, distribution, and consumption of news content.

Irfan Essa, Professor, Georgia Institute of Technology
Project: Tracing Reuse in Political Language
Our goal in this project is to research, and then develop a data-mining tool that allows an online researcher to find and trace language reuse. By language reuse, we specifically mean: Can we find if in a current text some language was used that can be traced back to some other text or script. The technical innovation in this project is aimed at (1) identifying linguistic reuse in documents as well as other forms of material, which can be converted to text, and therefore includes political speeches and videos. Another innovation will be in (2) how linguistic reuse can be traced through the web and online social networks.

Susan McGregor, Assistant Director, Tow Center for Digital Journalism, Columbia Journalism School
Project: InfoScribe
InfoScribe is a collaborative web platform that lets citizens participate in investigative journalism projects by digitizing select data from scanned document sets uploaded by journalists. One of InfoScribes primary research goals is to explore how community participation in journalistic activities can help improve their accuracy, transparency and impact. Additionally, InfoScribe seeks to build and expand upon understandings of how computer vision and statistical inference can be most efficiently combined with human effort in the completion of complex tasks.

Paul Resnick, Professor, University of Michigan School of Information
Project: RumorLens
RumorLens is a tool that will aid journalists in finding posts that spread or correct a particular rumor on Twitter, by exploring the size of the audiences that those posts have reached. In the collection phase, the user provides one or a few exemplar tweets and then manually classifies a few hundred others as spreading the rumor, correcting it, or labeling it as unrelated. This enables automatic retrieval and classification of remaining tweets, which are then presented in an interactive visualization that shows audience sizes.

Ryan Thornburg, Associate Professor, School of Journalism and Mass Communication, University of North Carolina at Chapel Hill
Project: Public Records Dashboard for Small Newsrooms
Building off our Knight News Challenge effort to bring data-driven journalism to readers of rural newspaper websites, we are developing an internal newsroom tool that will alert reporters and editors to potential story tips found in public data. Our project aims to lower the cost of finding in public data sets stories that shine light in dark places, hold powerful people accountable, and explain our increasingly complex and interconnected world. (Public facing site for the data acquisition element of the project at http://open-nc.org)

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Sergey and Larry awarded the Seoul Test of Time Award from WWW 2015

Posted by Andrei Broder, Google Distinguished Scientist

Today, at the 24th International World Wide Web Conference (WWW) in Florence, Italy, our company founders, Sergey Brin and Larry Page, received the inaugural Seoul Test-of-Time Award for their 1998 paper “The Anatomy of a Large-Scale Hypertextual Web Search Engine”, which introduced Google to the world at the 7th WWW conference in Brisbane, Australia. I had the pleasure and honor to accept the award on behalf of Larry and Sergey from Professor Chin-Wan Chung, who led the committee that created the award.

Except for the fact that I was myself in Brisbane, it is hard to believe that Google began just as a two-student research project at Stanford University 17 years ago with the goal to “produce much more satisfying search results than existing systems.” Their paper presented two breakthrough concepts: first, using a distributed system built on inexpensive commodity hardware to deal with the size of the index, and second, using the hyperlink structure of the Web as a powerful new relevance signal. By now these ideas are common wisdom, but their paper continues to be very influential: it has over 13,000 citations so far and more are added every day.

Since those beginnings Google has continued to grow, with tools that enable small business owners to reach customers, help long lost friends to reunite, and empower users to discover answers. We keep pursuing new ideas and products, generating discoveries that both affect the world and advance the state-of-the-art in Computer Science and related disciplines. From products like Gmail, Google Maps and Google Earth Engine to advances in Machine Intelligence, Computer Vision, and Natural Language Understanding, it is our continuing goal to create useful tools and services that benefit our users.

Larry and Sergey sent a video message to the conference expressing their thanks and their encouragement for future research, in which Sergey said “There is still a ton of work left to do in Search, and on the Web as a whole and I couldn’t think of a more exciting time to be working in this space.” I certainly share this view, and was very gratified by the number of young computer scientists from all over the world that came by the Google booth at the conference to share their thoughts about the future of search, and to explore the possibility of joining our efforts.

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Googler Shumin Zhai awarded with the ACM UIST Lasting Impact Award

Posted by Alfred Spector, Vice President, Engineering

Recently, at the 27th ACM User Interface Software and Technology Symposium (UIST’14), Google Senior Research Scientist Shumin Zhai and University of Cambridge Lecturer Per Ola Kristensson received the 2014 Lasting Impact Award for their seminal paper SHARK²: a large vocabulary shorthand writing system for pen-based computers. Most simply put, this is one of those rare works that is responsible for fundamental and lasting advances in the industry, and is the basis for the rapidly growing number of keyboards that use gesture typing, including products such as ShapeWriter, Swype, SwiftKey, SlideIT, TouchPal, and Google Keyboard.

First presented 10 years ago at UIST’04, Shumin and Per Ola’s paper is a pioneering work on word-gesture keyboard interaction that described the architecture, algorithms and interfaces of a high-capacity multi-channel gesture recognition system-SHARK². SHARK² increased recognition accuracy and relaxed precision requirements by using the shape and location of gestures in addition to context based language models. In doing so, Shumin and Per Ola delivered a paradigm of touch screen gesture typing as an efficient method for text entry that has continued to drive the development of mobile text entry across the industry.

"Awarded for its scientific contribution of algorithms, insights, and user interface considerations essential to the practical realization of large-vocabulary shape-writing systems for graphical keyboards, laying the groundwork for new research, industrial applications, and widespread user benefit."

Prior to joining Google in 2011, Shumin worked at the IBM Almaden Research Center for 15 years, where he originated and led the SHARK project, further developing and refining it to include a low latency recognition engine that introduced the ability to accurately recognize a large vocabulary of words based upon the patterns (sokgraphs) drawn on a touchscreen device. SHARK and SHARK² subsequently continued further development as ShapeWriter. During his tenure at IBM, Shumin additionally pursued a wide variety of HCI research areas including, but not limited to, studying the ease and efficiency of HCI interfaces, camera phone based motion sensing, and cross-device user experience.

At Google, Shumin has continued to inspire the Human-Computer Interaction research community, publishing prolifically and leading a group that incorporates HCI research, machine learning, statistical language modeling and mobile computing to advance the state of the art of text input for smart touchscreen keyboards. Building on his earlier work with SHARK/ShapeWriter, Gesture Typing is just one of the innovations that make things like typing messages on mobile device easier for hundreds of millions of people each day, and remains one of the most prominent features on Android keyboards.

Shumin has been highly active in academia during his career, as both visiting professor and lecturer at world-class universities, and is currently the Editor-in-Chief of ACM Transactions on Computer- Interaction, a Fellow of the ACM and a Member of the CHI Academy. We’re proud to congratulate Shumin and Per Ola on receiving one of the most prestigious honors in the Human-Computer Interaction (HCI) research community, and look forward to their future contributions.

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The iSANZ 2015 Best International Superstar award goes to

Our very own Clarke Thomborson! The iSANZ Awards are a showcase of excellence in New Zealand information security. Their mission is to formally recognise the achievements of outstanding New Zealand InfoSec professionals, companies and initiatives / events. The Best International Superstar award category is open to individuals who achieved significant results in the development or promotion of work that has had a high international profile. Clarke won the award for his contributions in trust, identity and privacy management which have helped significantly raise the profile of ICT within New Zealand.

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KDD 2015 Best Research Paper Award “Algorithms for Public Private Social Networks”

Posted by Posted by Corinna Cortes, Head, Google Research NY

The 21st ACM conference on Knowledge Discovery and Data Mining (KDD’15), a main venue for academic and industry research in data management, information retrieval, data mining and machine learning, was held last week in Sydney, Australia. In the past several years, Google has been actively participating in KDD, with several Googlers presenting work at the conference in the research and industrial tracks. This year Googlers presented 12 papers at KDD (listed below, with Googlers in blue), all of which are freely available at the ACM Digital Library.

One of these papers, Efficient Algorithms for Public-Private Social Networks, co-authored by Googlers Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni, former Googler intern Alessandro Epasto and research visitor Flavio Chierichetti, was awarded Best Research Paper. The inspiration for this paper comes from studying social networks and the importance of addressing privacy issues in analyzing such networks.

Privacy issues dictate the way information is shared among the members of the social network. In the simplest case, a user can mark some of her friends as private; this would make the connections (edges) between this user and these friends visible only to the user. In a different instantiation of privacy, a user can be a member of a private group; in this case, all the edges among the group members are to be considered private. Thus, each user in the social network has her own view of the link structure of the network. These privacy issues also influence the way in which the network itself can be viewed and processed by algorithms. For example, one cannot use the list of private friends of user X for suggesting potential friends or public news items to another user on the network, but one can use this list for the purpose of suggesting friends for user X.

As a result, enforcing these privacy guarantees translates to solving a different algorithmic problem for each user in the network, and for this reason, developing algorithms that process these social graphs and respect these privacy guarantees can become computationally expensive. In a recent study, Dey et al. crawled a snapshot of 1.4 million New York City Facebook users and reported that 52.6% of them hid their friends list. As more users make a larger portion of their social neighborhoods private, these computational issues become more important.

Motivated by the above, this paper introduces the public-private model of graphs, where each user (node) in the public graph has an associated private graph. In this model, the public graph is visible to everyone, and the private graph at each node is visible only to each specific user. Thus, any given user sees their graph as a union of their private graph and the public graph.

From algorithmic point of view, the paper explores two powerful computational paradigms for efficiently studying large graphs, namely, sketching and sampling, and focuses on some key problems in social networks such as similarity ranking, and clustering. In the sketching model, the paper shows how to efficiently approximate the neighborhood function, which in turn can be used to approximate various notions of centrality scores for each node - such centrality scores like the PageRank score have important applications in ranking and recommender systems. In the sampling model, the paper focuses on all-pair shortest path distances, node similarities, and correlation clustering, and develop algorithms that computes these notions on a given public-private graph and at the same time. The paper also illustrates the effectiveness of this model and the computational efficiency of the algorithms by performing experiments on real-world social networks.

The public-private model is an abstraction that can be used to develop efficient social network algorithms. This work leaves a number of open interesting research directions such as: obtaining efficient algorithms for the densest subgraph/community detection problems, influence maximization, computing other pairwise similarity scores, and most importantly, recommendation systems.

KDD’15 Papers, co-authored by Googlers:

Efficient Algorithms for Public-Private Social Networks (Best Paper Award)
Flavio Chierichetti, Alessandro Epasto, Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling

TimeMachine: Timeline Generation for Knowledge-Base Entities
Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, Wei Zhang

Algorithmic Cartography: Placing Points of Interest and Ads on Maps
Mohammad Mahdian, Okke Schrijvers, Sergei Vassilvitskii

Stream Sampling for Frequency Cap Statistics
Edith Cohen

Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams
Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J.Smola, Le Song

Adaptation Algorithm and Theory Based on Generalized Discrepancy
Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina (now at Google)

Estimating Local Intrinsic Dimensionality
Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle Ken-ichi Kawarabayashi, Michael Nett

Unified and Contrasting Cuts in Multiple Graphs: Application to Medical Imaging Segmentation
Chia-Tung Kuo, Xiang Wang, Peter Walker, Owen Carmichael, Jieping Ye, Ian Davidson

Going In-depth: Finding Longform on the Web
Virginia Smith, Miriam Connor, Isabelle Stanton

Annotating needles in the haystack without looking: Product information extraction from emails
Weinan Zhang, Amr Ahmed, Jie Yang, Vanja Josifovski, Alexander Smola

Focusing on the Long-term: Its Good for Users and Business
Diane Tang, Henning Hohnhold, Deirdre OBrien

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