[Humanist] 23.801 events: Human Computation 2010

Humanist Discussion Group willard.mccarty at mccarty.org.uk
Thu May 6 07:48:08 CEST 2010

                 Humanist Discussion Group, Vol. 23, No. 801.
         Centre for Computing in the Humanities, King's College London
                Submit to: humanist at lists.digitalhumanities.org

        Date: Wed, 5 May 2010 13:35:29 +0100
        From: Rainer Malaka <malaka at tzi.de>
        Subject: cfp: Human Computation Workshop 2010 (HCOMP 2010)

	Human Computation Workshop 2010 (HCOMP 2010)

	July 25, 2010   Washington DC, USA

	Collocated with ACM SIG KDD-2010

Most research in data mining and knowledge discovery relies heavily on
the availability of datasets. With the rapid growth of user generated
content on the internet, there is now an abundance of sources from which
data can be drawn. Compared to the amount of work in the field on
techniques for pattern discovery and knowledge extraction, there has
been little effort directed at the study of effective methods for
collecting and evaluating the quality of data.

Human computation is a relatively new research area that studies the
process of channeling the vast internet population to perform tasks or
provide data towards solving difficult problems that no known efficient
computer algorithms can yet solve. There are various genres of human
computation applications available today. Games with a purpose (e.g.,
the ESP Game) specifically target online gamers who, in the process of
playing an enjoyable game, generate useful data (e.g., image tags).
Crowdsourcing marketplaces (e.g. Amazon Mechanical Turk) are human
computation applications that coordinate workers to perform tasks in
exchange for monetary rewards. In identity verification tasks, users
need to perform some computation in order to access some online content;
one example of such a human computation application is reCAPTCHA, which
leverages millions of users who solve CAPTCHAs every day to correct
words in books that optical character recognition (OCR) programs fail to
recognize with certainty.

Human computation is an area with significant research challenges and
increasing business interest, making this doubly relevant to KDD. KDD
provides an ideal forum for a workshop on human computation as a form of
cost-sensitive data acquisition. The workshop also offers a chance to
interact with practitioners who have complementary real-world expertise
in gaming and mechanism design.

The first Human Computation Workshop (HComp 2009) was held on June 28th,
2009, in Paris, France, collocated with KDD 2009. The overall themes
that emerged from this workshop were very clear: on the one hand, there
is the experimental side of human computation, with research on new
incentives for users to participate, new types of actions, and new modes
of interaction. On the theoretic side, we have research modeling these
actions and incentives to examine what theory predicts about these
designs.  Finally, there is work on noisy results generated by such
games and systems: how can we best handle noise, identify labeler
expertise, and use the generated data for data mining purposes?

Learning from HComp 2009, we have expanded the topics of relevance to
the workshop.  The goal of HComp 2010 is to bring together academic and
industry researchers in a stimulating discussion of existing human
computation applications and future directions of this new subject area.
We solicit papers related to various aspects of both general human
computation techniques and specific applications, e.g. general design
principles; implementation; cost- benefit analysis; theoretical
approaches; privacy and security concerns; and incorporation of machine
learning / artificial intelligence techniques. An integral part of this
workshop will be a demo session where participants can showcase their
human computation applications. Specifically, topics of interests
include, but are not limited to:

* Abstraction of human computation tasks into taxonomies of mechanisms
* Theories about what makes some human computation tasks fun and
* Differences between collaborative vs. competitive tasks
* Programming languages, tools and platforms to support human
* Domain-specific implementation challenges in human computation games
* Cost, reliability, and skill of labelers
* Benefits of one-time versus repeated labeling
* Game-theoretic mechanism design of incentives for motivation and
   honest reporting
* Design of manipulation-resistance mechanisms in human computation
* Effectiveness of CAPTCHAs
* Concerns regarding the protection of labeler identities
* Active learning from imperfect human labelers
* Creation of intelligent bots in human computation games
* Utility of social networks and social credit in garnering data
* Optimality in the context of human computation
* Focus on tasks where crowds, not individuals, have the answers
* Limitations of human computation


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