The Crowd at HICSS 2013 Series – #4

 

CROWDSOURCING CRITICAL THINKING

One of the challenges in using social media technologies, such as Twitter, for disaster response is that information that can help save lives is buried under the sea of other information and misinformation. This was the case in the aftermath of the 2011 Great East Japan Earthquake. For example, information on Twitter helped the rescuing of children and teachers who were stranded at a school building. However, finding this information was extremely hard because a lot of unverified tweets spread during disaster response, even after people pointed out that the unverified tweets were false rumors in their criticism tweets.

Motivated by these observations, Tanaka, Sakamoto, and Matsuka examined if the critical thinking of crowds could help reduce the spread of misinformation. Using false tweets and criticism tweets related to the Great East Japan Earthquake, they conducted an experiment, in which half of the students in Japanese universities saw criticism tweets before seeing the false tweets, and the other half did not. They found that exposing subjects to criticism tweets increased the decision not to share the false tweets about 1.5 times, from 32% to 49%. When subjects decided to share the false tweets even after seeing the criticism tweets, they perceived the false tweets as more accurate, more important, and more anxiety-provoking than when they decided not to share the false tweets after seeing the criticism tweets. Their work, which won the best paper award in the Collaboration Systems and Technologies track, demonstrated that exposing people to criticism tweets could change their perceptions of and significantly reduce the decision to spread the associated false tweets.
Given these findings, the group is examining how to promote the credibility evaluation by crowds to reduce the spread of misinformation and extract useful information on social media during disasters, and if it is possible to change how crowds perceive and feel about disaster-related information on social media to direct their sharing decision. Changing the perspective of crowds was the focus of another HICSS 2013 paper, which received a best paper nomination. By following this link you can find more about their research on improving social media for disaster response.

The Crowd at HICSS 2013 Series – #2

The Theory of Crowd Capital

in Proceedings of the Hawai’i International Conference on System Science 2013

 John Prpić & Prashant Shukla

Beedie School of Business

Simon Fraser University

We are seeing more and more organizations undertaking activities to engage dispersed populations through IT. Using the knowledge-based view of the organization, this work conceptualizes the theory of Crowd Capital to explain this phenomenon. A diagram of our model is shown immediately below.

Diagram of Crowd Capital Creation Theory of Crowd Capital - Model

Crowd Capital is a heterogeneous knowledge resource generated by an organization, through its employ of Crowd Capability. An organization’s Crowd Capability engages the Dispersed Knowledge (Hayek 1945) of individuals –the Crowd.

Crowd Capability includes three dimensions by which an organization engages Dispersed Knowledge: a structure (some form of IT), content (the knowledge that the organization desires), and a process (internal work which sorts, filters, synthesizes, the incoming information).

Crowd Capital is always IT-mediated. In other words, forms of IT (web pages, mobile apps, sensors, software etc.) are always employed by organizations to engage the antecedent condition of Dispersed Knowledge.

Organizations exist in an environment of Dispersed Knowledge, hence, Dispersed Knowledge is not only external to the organization, but also  can be engaged internally, externally or both simultaneously.

Crowd Capital can be generated through episodic or continuous forms of IT.  Here we distinguish between forms of IT that necessitate community and collaboration to function, and those that do not. For example, we reason that Google’s ReCaptcha and Citizen Science applications like Foldit, do not require community and collaboration to generate Crowd Capital, whereas Innovation Communities (von Hippel 2005) and Peer Production (Benkler & Nissenbaum 2006) do.

If you’re interested, you can find a preprint copy of Prpić & Shukla (2013) here:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2193115

We very much look forward to your comments!

Visual Challenges in the Everyday Lives of Blind People

Blind people often need to access visual information, but don’t have a sighted companion available to answer questions.  While automated tools like OCR and object recognition can make some visual information accessible, other complex questions still require a human’s input.

We created VizWiz Social to connect blind users’ visual questions with sighted workers or friends who can answer them.  VizWiz Social is a mobile phone application that allows the user to take a photograph of something, record a question about it, and send it to anonymous crowd workers or to their social network for answers.  Our paper discusses what we learned from the 40,748 questions asked by 5,329 blind users in the first year of the app’s availability.

vizwiz-social-ui

The VizWiz Social user interface allows a blind user to (a) take a photograph of something they have a question about, (b) record their question, (c) choose where to send their question, and (d) wait for answers to arrive. The user can access the application through the VoiceOver screen reader.

We created a taxonomy of the types of questions asked to find out what visual information was most desired by our blind users.  We also analyzed the features of the photographs and questions send in by user, and user behaviors over time.  Some interesting results include:

  • The most popular type of questions were Identification questions (41%), where users asked for simple identifications of objects (such as food or drink items).  However, there were also a large number of Description questions (24%) and Reading questions (17%), which required more complex analysis and human insight to answer.
  • Most questions were perceived by our raters to be objective and time-sensitive, perhaps due to the fast response times available from object recognition or pre-recruited crowd workers.
  • Users who had a poor first experience with our application – either because their photographs were low-quality or or because the answers they received were unsatisfactory – had a higher-than-typical abandonment rate.

These results improve our understanding of the problems blind people face, and may help motivate new projects more accurately targeted to help blind people live more independently in their everyday lives.

For more, see our full paper, Visual Challenges in the Everyday Lives of Blind People.

Erin Brady, ROCHCI @ University of Rochester
Meredith Ringel Morris, Microsoft Research
Yu Zhong, ROCHCI @ University of Rochester
Samuel White, ROCHCI @ University of Rochester
Jeffrey Bigham, ROCHCI @ University of Rochester

 

CrowdCamp Report: DesignOracle: Exploring a Creative Design Space with a Crowd of Non-Experts

For an unconstrained design problem like writing a fiction story or designing a poster, can a crowd of non-experts be shepherded to generate a diverse collection of high quality ideas? Can the crowd also generate the actual stories or posters? With the goal of building crowd-powered creativity support tools, a group of us, Paul André, Robin N. Brewer, Krzysztof Gajos, Yotam Gingold, Kurt Luther, Pao Siangliulue, and Kathleen Tuite, set out to answer these questions. We based our approach on a technique described by Keith Johnstone in his book Impro: Improvisation and the Theatre for extracting creativity from people who believe themselves to be uncreative. An uncreative person is told that a story is already prepared and he or she has merely to guess it via yes or no questions. Unbeknown to the guesser, there is no story; guesses are met with a yes or no response, essentially randomly. For example:

  1. Is it about CSCW? Yes
  2. Is it about CrowdCamp? No
  3. Is it about a bitter rivalry? Yes

As questioning proceeds, a consistent story is revealed, entirely due to the guesser generating and then externalizing an internally consistent mental model of a story (or poster, etc.) that justifies the given answers.

To evaluate the potential of this “StoryOracle” approach, we ran a series of experiments on Amazon Mechanical Turk:

  1. We extracted dozens of surprising and creative stories and poster designs using the technique.
  2. We explored the design space in a directed manner by generating variations on well-liked stories. Every question and yes or no answer provides a possible branch-point. To branch, we selected an important “pivot” question and showed all questions and answers up to the pivot, followed by the pivot question and the same or the opposite answer, to a set of new participants with instructions to continue guessing the story. For example: A story and branches
  3. We converted question-based stories into “normal” stories. For example:

    Jack is a scientist who has dedicated his life to discovering how to generate energy using fusion power because he believes it would benefit humanity. Eventually he discovers how to do it and tells his wife, Jane, the good news. Jane is not aware that this is a secret and talk about it with Carl (to whom she was in love before meeting Jack). Carl tries to steal the secret from Jack but Jane fought with him and is able to impede him.
    However, Jack discovers that Jane told Carl about the secret and they have a fight because of it. Eventually, Carl, who is angry with both of them, decides to kill Jack and Jane. He manages to kill the couple but when he is about to steal Jack’s secret, the power fusion discovery is released to the public through the internet.
    Thus, all the world is able to produce energy using Jack’s discovery and eventually his dream of providing a better quality of life to everyone comes true.

  4. We evaluated stories’ quality.
  5. We devised domain-specific prompts for questioners, such as the setting, theme, and characters in a story.

Taken together, in the two days of CrowdCamp we managed to build the foundation for a crowd-powered tool to explore a creative design space, in an undirected or directed manner, and generate a variety of high quality artifacts.

Paul André, Carnegie Mellon University
Robin N. Brewer, University of Maryland, Baltimore County
Krzysztof Gajos, Harvard University
Yotam Gingold, George Mason University
Kurt Luther, Carnegie Mellon University
Pao Siangliulue, Harvard University
Kathleen Tuite, University of Washington

AutoMan: Programming with People

People can often perform tasks, such as natural language understanding, vision, and motion planning with greater accuracy and speed than the best algorithms available to us. Computers are good for repetitive, mechanical tasks, but many AI-style tasks remain elusive. By combining humans and computers, crowdsourcing has the potential to create a new class of applications which combine the best qualities of both.

However, unlike traditional computer programs, working with people introduces number of complications:

People don’t work for free. How much should you pay them?
Compared to programs, people are slow. How should you write your program to minimize latency?
People make mistakes. Between spammers and well-intentioned but mistaken workers, how do you know that your answers are correct?

We developed the AutoMan system with these concerns in mind. AutoMan abstracts away the issues of payment, scheduling, and quality control so that programmers can focus on the purpose of their applications. Formerly difficult crowdsourcing tasks become simple, declarative programs:

A simple classification task definition using AutoMan.

A simple classification task definition using AutoMan.

AutoMan allows programmers to combine off-the-shelf code written for the Java Virtual Machine with quality-controlled, high-performance human subroutines. We have focused our research primarily on the Mechanical Turk platform but the system was designed to be platform-agnostic, only requiring implementers to provide a backend driver.

The rendered task on Mechanical Turk.

The rendered task on Mechanical Turk.

AutoMan provides question answers with a statistical confidence guarantee. Often, there is a direct trade-off between the quality required by the programmer and the cost of a task. Task wages are determined dynamically, freeing the programmer from having to determine a fair wage.

Handling these concerns in the language’s runtime means that programs that have no or ad hoc quality control schemes and would normally need a trusted supervisor to periodically watch over them are now completely automatic. Freed from requiring constant supervision, programmers can integrate human judgment into large-scale, real-world applications.

Using AutoMan, we’ve explored a variety of tasks, ranging from image recognition and categorization tasks to complex, real-world tasks like automatic license plate identification. We’re continuing to explore what is possible with AutoMan while enhancing the simplicity, reliability, and performance of the system.

AutoMan is available on our GitHub page. Give it a try and tell us what you think!

For more information, see our OOPSLA 2012 paper: AutoMan: A Platform for Integrating Human-Based and Digital Computation.

Paying human computers by the bit

Collective human computation – presenting objective questions to multiple humans and collecting their judgments – is a powerful and increasingly popular paradigm for performing computational tasks beyond the reach of today’s algorithms. From image classification to data validation, the human computer is making a comeback.

But how should we measure the performance of a human doing a computational task? Speed without accuracy is worthless, and accuracy itself is hard to measure in classification or estimation tasks in which a close-to-correct judgment still has value.

I assert that the value of a judgment is the amount by which it reduces the surprise of learning the correct answer to a question. This is a basic concept in information theory: the pointwise mutual information between the judgment and the answer.

For example, a classification problem with four equally-likely categories has entropy of 2 bits per question. If you correctly classify a series of objects, you’re giving the full 2 bits of information for each. If you’re a spammer giving judgments that are statistically independent of the correct categories, you’re giving zero information no matter what your spamming strategy is.

Thus, the net value of a contributor’s judgments is the total amount of information they give us, a well-defined extensive quantity that we can measure in bits (or nats or digits, if you please).

This metric has the advantages of being naturally-motivated, task- and model-agnostic, and free of tuning, and it easily plugs in to any resolution algorithm that models contributors and answers as random variables.

Expected values (ie. entropy) can be used to predict a contributor’s performance on a given question, conditioned on what’s already known about that question. Contributors can be preferentially given the questions for which they’re likely to be most informative. By applying this technique to data from Galaxy Zoo 2 (a crowdsourced deep-field galaxy classification project, part of the Zooniverse program), I was able to demonstrate a substantial improvement in accuracy compared to random assignment of questions to contributors.

Finally, we can measure the cost-effectiveness of the judgment collection process or the information efficiency of the resolution algorithm in terms of the total information received from contributors. Related metrics can be used to measure the overlap in information between two contributors or the information wasted by collecting redundant judgments.

The metrics I present can be mixed in to any human computation resolution algorithm that uses a statistical model to turn judgments into answers, by using the model’s estimated parameters to compute a set of conditional probabilities and then plugging these in to the definitions of the information-theoretic quantities. The paper includes worked examples for several models.

For more, see the full paper:
Pay by the Bit: An Information-Theoretic Metric for Collective Human Judgment

Tamsyn P Waterhouse, Google Inc.

CfP: new Crowdsourcing area at ACM Multimedia 2013

Crowdsourcing Area at ACM MM 2013
The 21st ACM International Conference on Multimedia
October 21–25, 2013, Barcelona Spain
Call for Papers: http://acmmm13.org/submissions/call-for-papers/

Following the successful Crowd MM workshop at ACM Multimedia last year, we have added Crowdsourcing as an official technical program area (long and short papers) for ACM MM 2013 in Barcelona Spain.  Multimedia is the flagship conference for ACM SIGMM.

AREA DESCRIPTION

Crowdsourcing makes use of human intelligence and a large pool of contributors to address problems that are difficult to solve using conventional computation. This new area cross-cuts traditional multimedia topics and solicits submissions dedicated to results and novel ideas in multimedia that are made possible by the crowd, i.e., they exploit crowdsourcing principles and techniques. Crowdsourcing is considered to encompass the use of: microtask marketplaces, games-with-a-purpose, collective intelligence and human computation. Topics include, but are not limited to:

  • Exploiting crowdsourcing for multimedia generation, interpretation, sharing or retrieval
  • Learning from crowd-annotated or crowd-augmented multimedia data
  • Economics and incentive structures in multimedia crowdsourcing systems
  • Crowd-based design and evaluation of multimedia algorithms and systems
  • Crowdsourcing in multimedia systems and applications such as Art & Culture, Authoring, Collaboration, Mobile & Multi-device, Multimedia Analysis, Search, and Social Media.

Submissions should have both a clear focus on multimedia and also a critical dependency on crowdsourcing techniques.

CONFERENCE INFO

Since the founding of ACM SIGMM in 1993, ACM Multimedia has been the worldwide premier conference and a key world event to display scientific achievements and innovative industrial products in the multimedia field. At ACM Multimedia 2013, we will celebrate its twenty-first iteration with an extensive program consisting of technical sessions covering all aspects of the multimedia field in forms of oral and poster presentations, tutorials, panels, exhibits, demonstrations and workshops, bringing into focus the principal subjects of investigation, competitions of research teams on challenging problems, and also an interactive art program stimulating artists and computer scientists to meet and discover together the frontiers of artistic communication.

IMPORTANT DATES

  • Abstract for Full Papers: March 1, 2013
  • Manuscript for Full/Short Papers: March 8, 2013
  • Rebuttal May 8–17, 2013
  • Author-to-Author’s Advocate contact period: May 8–13, 2013
  • Notification of Acceptance: June 25, 2013
  • Camera-ready submission: July 30, 2013
  • Conference: October 21–25, 2013, Barcelona Spain

CONFERENCE WEBSITE

http://acmmm13.org

Investigating the Appropriateness of Social Network Question Asking as a Resource for Blind Users

As social networking sites (SNSs) have grown in popularity, more users are turning to their online social networks to seek answers to questions. These answers can be more personalized and trusted than answers coming from strangers, since members of your social network may know more about you than anonymous answerers on other sites.

Little is known about how blind people use SNSs, or view them as a resource for question-asking. Our research examined these questions through two distinct components – first, a large-scale online survey of blind people’s use of SNSs, and second, a smaller-scale field experiment examining motivations for SNS question-asking among blind users of the VizWiz Social application.

A Facebook newsfeed with a video clip and answers from friends in the comments

An example of a VizWiz Social question asked via Facebook. Answers are relayed back to the blind user’s phone.

Our large-scale survey of 191 blind participants found that, while many blind people are members of SNSs and consume content daily, they rarely post questions to the sites. Only about half of those surveyed said they thought SNS question-asking would be effective, or that they would feel comfortable posting questions. Some even limited their question-asking on SNSs, both for social reasons (eg. avoiding social costs of bothering friends) or practical reasons (eg. low response rates).

Our smaller-scale field experiment recruited users of VizWiz Social, a smartphone application that allows blind people to get answers to visual questions by having them snap a photograph, record a question about it, and send it to members of their social network or anonymous crowd workers.

We implemented financial limitations on the crowdsourced answers, which were previously free to VizWiz Social users. 23 users participated in the month-long experiment, but financial costs to answers did not motivate them to rely on free, friendsourced answers.

In a questionnaire after the field experiment, VizWiz Social users reported highly preferring crowdsourced responses for both social reasons (eg. preferring anonymity) and practical reasons (eg. speed of responses).

Through the combination of a large-scale online survey and a smaller-scale field experiment, we have found that blind people are reluctant to use SNSs for question-asking, even when presented with a financial motivation to do so.

For more, see our full paper, Investigating the Appropriateness of Social Network Question Asking as a Resource for Blind Users.
Erin Brady, ROCHCI @ University of Rochester
Yu Zhong, ROCHCI @ University of Rochester
Meredith Ringel Morris, Microsoft Research
Jeffrey Bigham, ROCHCI @ University of Rochester

CfP: Crowdsourcing in Virtual Communities track at AMCIS 2013

Mini-track: Crowdsourcing in Virtual Communities
19th Americas Conference on Information Systems (AMCIS 2013)
August 15-17, 2013 in Chicago, Illinois, USA
Link: http://amcis2013.aisnet.org/?option=com_content&id=69

Following the successful crowdsourcing tracks at ACIS 2011 and AMCIS 2012, we are accepting submission to this year’s AMCIS 2013 crowdsourcing track in Chicago. AMCIS is one of the biggest annual conferences in the field of Information Systems with about 1000 participants.

DESCRIPTION
Crowdsourcing harnesses the potential of large networks of people via open calls for contribution and thus enables organizations to tap into a diversity of knowledge, skills, and perspectives. Fueled by the increasing pervasiveness of the Internet, crowdsourcing has been rapidly gaining importance in a wide range of contexts, both in research and practice. In order to provide better guidance for future crowdsourcing efforts, it is crucial to gain a deeper and integrated understanding of the phenomenon. While research on crowdsourcing is multidisciplinary, information systems take a central role in realizing crowdsourcing approaches by interconnecting organizations and globally distributed contributors. By viewing crowdsourcing from an IS perspective, this track aims to channel related research directions and move from the consideration of isolated aspects and applications to a systemic foundation for the design of socio-technical crowdsourcing systems.

We encourage submissions from theoretical, empirical, and design science research on the following and adjacent topics:
- Crowdsourcing ecosystems and markets
- Platforms, tools, and technologies
- Task characteristics, task design, and task choice
- Contributor motivation and incentive structures
- Design of workflows and processes
- Mobile crowdsourcing
- Quality assurance and evaluation of contributions
- Economics of crowdsourcing
- Case studies of crowdsourcing effectiveness
- Adoption of crowdsourcing business models
- Innovative applications

IMPORTANT DATES
January 4, 2013: Bepress will start accepting paper submissions
February 22, 2013 (11:59 pm CST): Deadline for paper submissions
April 22, 2013: Authors notified of acceptance decisions
May 9, 2013: Camera-ready copy due for accepted papers

Announcing HCOMP 2013 – Conference on Human Computation and Crowdsourcing

Bjoern Hartmann, UC-Berkeley 
Eric Horvitz, Microsoft Research

Announcing HCOMP 2013, the Conference on Human Computation and Crowdsourcing,  Palm Springs, November 7-9, 2013.  Paper submission deadline is May 1, 2013.  Thanks to the HCOMP community for bringing HCOMP to life as a full conference, following on the successful workshop series.

HCOMP 2013 at Palm Springs

The First AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2013) will be held November 7-9, 2013 in Palm Springs, California, USA. The conference was created by researchers from diverse fields to serve as a key focal point and scholarly venue for the review and presentation of the highest quality work on principles, studies, and applications of human computation. The conference is aimed at promoting the scientific exchange of advances in human computation and crowdsourcing among researchers, engineers, and practitioners across a spectrum of disciplines.  Papers submissions are due May 1, 2013 with author notification on July 16, 2013.  Workshop and tutorial proposals are due May 10, 2013.  Posters & demonstrations submissions are due July 25, 2013.

For more information, see the HCOMP 2013 website.