posted by user: winteram || 16411 views || tracked by 33 users: [display]

MLJ - CSSSC 2012 : Machine Learning Journal Special Issue on Computational Social Science and Social Computing

FacebookTwitterLinkedInGoogle

Link: http://mach.edmgr.com
 
When N/A
Where N/A
Submission Deadline Aug 17, 2012
Categories    computational social science   social computing   computer science
 

Call For Papers

************************************************************************

CALL FOR PAPERS

Machine Learning Journal Special Issue on
Computational Social Science and Social Computing

-- Submission Deadline EXTENDED: August 17, 2012 --

************************************************************************

OVERVIEW:

Computational social science is an emerging academic research area at
the intersection of computer science, statistics, and the social
sciences, in which quantitative methods and computational tools are
used to identify and answer social science questions. The field is
driven by new sources of data from the Internet, sensor networks,
government databases, crowdsourcing systems, and more, as well as by
recent advances in computational modeling, machine learning,
statistics, and social network analysis.

The related research area of social computing deals with the
mechanisms through which people interact with computational systems,
examining how and why people contribute user-generated
content. Examples of social computing systems include prediction
markets, reputation systems, and collaborative filtering systems, all
designed with the intent of capturing the wisdom of crowds.

Machine learning plays in important role in both of these areas. For
this special issue, we invite high quality submissions on work at the
intersection of machine learning and any aspect(s) of social computing
or computational social science, including but not limited to:

* Automatic aggregation of opinions or knowledge

* Incentives in social computation (e.g., game-theoretic approaches)

* Prediction markets / information markets

* Studies of events and trends (e.g., in politics)

* Quality control and reputation mechanisms for user generated content

* Analysis of and experiments on distributed collaboration and
consensus-building, including crowdsourcing (e.g., Mechanical Turk)
and peer-production systems (e.g., Wikipedia and Yahoo! Answers)

* Group dynamics and decision-making

* Modeling network-interaction content (e.g., text analysis of blog
posts, tweets, emails, chats, etc.)

* Social networks

* Games with a purpose


PAPER SUBMISSION AND TENTATIVE SCHEDULE:

Authors are encouraged to submit high-quality, original work that has
not appeared in other journals, nor is under consideration by other
journals. Submissions and reviewing will be handled electronically
using the standard procedures for Machine Learning Journal.

Deadline for submissions: August 1, 2012
Notification to authors: January 15, 2013
Revisions due: April 15, 2013


GUEST EDITORS:

Winter Mason, Stevens Institute of Technology
Jennifer Wortman Vaughan, UCLA
Hanna Wallach, University of Massachusetts Amherst

Related Resources

Ei/Scopus-ACEPE 2026   2026 3rd IEEE Asia Conference on Advances in Electrical and Power Engineering (ACEPE 2026)
Ei/Scopus-AI2A 2026   2026 IEEE 6th International Conference on Artificial Intelligence, Automation and Algorithms (AI2A 2026)
VCOI 2026   4th International Conference on Vision and Computational Intelligence
CSIT 2026   13th International Conference on Computer Science and Information Technology
IEEE-MLNLP 2026   2026 IEEE 9th International Conference on Machine Learning and Natural Language Processing (MLNLP 2026)
IJCSIT 2026   International Journal of Computer Science and Information Technology - H-index 60
MLDS 2026   7th International Conference on Machine Learning Techniques and Data Science
BMLI 2026   7th International Conference on Big Data, Machine Learning and IoT
ISCAIE 2027   17th IEEE Symposium on Computer Applications and Industrial Electronics
SEMIT 2026   7th International Conference on Software Engineering and Managing Information Technology