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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Tue, 19 Jun 2012 22:57:59 -0700
Jeffrey W Nichols <[log in to unmask]>
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Jeffrey W Nichols <[log in to unmask]>
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Data-driven User Behavioral Modelling and Mining from Social Media,
Workshop at CIKM 2012
Oct 29, 2012  -  Maui, Hawaii
For questions, contact: Jalal Mahmud, [log in to unmask]

Important Dates
Submission Deadline:	July 30, 2012
Author Notification:     	August 13, 2012
Camera-Ready version: 	August 25, 2012
Workshop:                  	October 29, 2012

This workshop aims to achieve three main objectives:

   1) Identify key research issues and challenges in designing and
developing techniques for modeling and mining users and their behavior from
social media;
   2) Establish and grow a community that consists of researchers and
practitioners from multiple disciplines to tackle the difficult  problems
of user behavior understanding from social media; and
   3) Initiate proper collaboration (e.g., creating and sharing public
datasets) among different teams.

We hope to bring together researchers and practitioners from diverse areas,
such as user modeling, social media analysis, natural language processing,
data mining, machine learning, privacy and security, to discuss these
issues and share results. We seek high quality submissions in any of the
following areas:

  * Interactive visual analytics for understanding derived user models
  * Integrated content and social network mining and analysis to derive
user insights
  * Mining of heterogeneous data sources to derive user insights
  * Discovery of trustworthy information and information sources
  * Identification of significant user attributes (e.g., personality or
preferences) for task-specific user behavior modeling and prediction (e.g.,
information collection or spreading tasks)
  * Predictive analytics of user behavior
  * User behavior modeling and mining at scale using Big Data approaches

Feasibility/challenges of understanding individual users from social media
  * What aspects of an individual can be modeled from their public social
media postings?
  * What aspects cannot be modeled?
  * What aspects should not be modeled?
  * How accurate are the models that can be extracted?
  * What are the best techniques to model user behavior?
  * How might the creation of such models be thwarted? (e.g. to preserve
privacy while still allowing participation on a social network)

Protecting user privacy
  * What information about a user can be modeled while keeping the
sensitive information private?
  * How can users monitor what information has been revealed about
themselves on social media and obfuscate any sensitive information that has
been accidently revealed?

Domains and Applications
  * Domain-specific user modeling using public social media including
Twitter,Facebook, MySpace, social Q&A sites, and reviews for:
       - Retail
       - Healthcare
       - Education
       - Sports
       - News
  * Domain-independent user modeling using public social media such as
twitter, facebook, myspace, and foursquare to derive a wide variety of user
traits including:
       - Locations
       - Behaviors/Personality
       - Demographics
       - Age
       - Gender
  * Enterprise-focused user modeling using social media data on public
social networks and communications (e.g., emails and blogs) within an
       - Employees’ social and collaboration patterns in a workplace
       - Work-related personality traits such as innovativeness,
flexibility, and adaptiveness
  * Task-specific user modeling for:
       - Information recommendation
       - Crowdsourcing
       - Expert finding
       - Social Q&A

Submission Instructions
We will accept submissions in the following two categories:
  * Position paper (2 pages)
  * Short papers (4 pages)

All submissions should be prepared according to the standard ACM
publications format
( and submitted

Jalal Mahmud, IBM Research – Almaden, USA
James Caverlee, Texas A&M University, USA
Jeffrey Nichols, IBM Research – Almaden, USA
John O' Donovan, University of California, Santa Barbara, USA
Michelle Zhou, IBM Research Almaden, USA

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