[Air-L] 2nd [CfP] WWW'17 Workshop on Modeling Social Media (MSM'2017)

[Christoph Trattner]

** Please forward to anyone who might be interested **
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                      CALL FOR PAPERS
8th International Workshop on Modeling Social Media (MSM'2017)
Machine Learning and AI for Modeling and Analyzing Social Media

        to be held on April 3-7, 2017, Perth, Australia
                  co-located with WWW 2017

        http://www.kde.cs.uni-kassel.de/ws/msm2017/
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Important Dates:
================
** Submission Deadline: Jan 7, 2017 (23:59 Australian Western Standard Time)
** Notification of Acceptance: Jan 31, 2017
** Camera-Ready Versions Due: Feb 14, 2017
** Workshop date: April 3-7, 2017 (exact date to be determined)


Workshop Organizers:
====================
Martin Atzmueller, University of Kassel, Germany;atzmueller at cs.uni-kassel.de
Shlomo Berkovsky, CSIRO, Australia; shlomo.berkovsky at csiro.au
Alvin Chin, BMW Group, USA; alvin.chin at bmwna.com
Jianxin Li, University of Western Australia, Australia; Jianxin.li at uwa.edu.au
Christoph Trattner, Know-Center, Austria; trattner.christoph at gmail.com

In this workshop, we aim to attract researchers from all over the
world working on Machine Learning and AI models for social media data
analytics and predictive insights. Social networks such as Facebook,
Twitter, and LinkedIn have paved the way for generating huge amount of
diverse data in a short period of time. Such social media data require
the application of big data analytics to produce meaningful
information to both information consumers and data generators. Machine
learning and AI techniques are particularly effective in situations
where deep and predictive insights need to be uncovered from such
social media data sets that are large, diverse and fast changing. We
aim to focus on machine learning and AI driven data analytics and
predictive modeling on social media and the web. We invite researchers
that are interested in going beyond standard analytics approaches and
discovering the knowledge/insights hidden in the large and
fast-changing social media data.

In this context, we would also like to invite researchers in the
machine learning, AI, natural language processing, data and web mining
community to lend their expertise to help to increase our
understanding of the web and social media. We are interested in
receiving papers related to the following topics which include but are
not limited to:

* AI, machine learning and natural language processing for social
media, big data and the web
* Learning analytics methods or frameworks for social media, big data
and the web
* Learning activities, applications and interventions
* Approaches for social influence learning
* Learning methods for social link prediction
* Methods for learning social activities and behavioral analytic metrics
* Approaches and algorithms for efficient learning
* Evaluation of learning analytics frameworks and metrics
* Applications of any of the above methods and technologies

The goal of this workshop is to study the application of machine
learning and AI approaches and algorithms to social media, big data
and the web.

Submissions: We solicit full research papers (4-6 pages), and short
papers (1-4 pages) both in the ACM conference paper style.
Papers should be submitted in EasyChair
tohttps://www.easychair.org/conferences/?conf=msm2017

Program Committee:
==================
Javier Luis Canovas Izquierdo, IN3 - UOC, Spain
Arkaitz Zubiaga, University of Warwick, UK
Shaghayegh Sahebi, University of Pittsburgh, USA
Sharon Hsiao, Arizona State University, USA
Bin Guo, Northwestern Polytechnical University, China
Michael Granitzer, University of Passau, Germany
Kjetil Norvag, Norwegian University of Science and Technology, Norway
Mark Kibanov, University of Kassel, Germany
Eelco Herder, L3S Research Center, Germany
Denis Parra, Pontificia Universidad Catolica de Chile, Chile
Robin Burke, DePaul University, USA
Su Yang, Fudan University, China
Geert-Jan Houben, TU Delft, Netherlands


Proceedings:
============
Contributions will be included in the Companion volume of the ACM
WWW2017 conference, which will be published by ACM and included
in the ACM Digital Library. However, to make that happen at least one
author of the accepted paper has to register. At the time of
submission of the final
camera-ready copy, authors will have to indicate the already
registered person for that publication.

Any paper published by the ACM, IEEE, etc. which can be properly
cited constitutes research which must be considered in judging the
novelty of a WWW submission, whether the published paper was in a
conference, journal, or workshop. Therefore, any paper previously
published as part of a WWW workshop must be referenced and suitably
extended with new content to qualify as a new submission to the
Research Track at the WWW conference.

Submission guidelines:
======================
All submitted papers must
    * be written in English;
    * contain author names, affiliations, and email addresses;
    * be formatted according to the ACM SIG Proceedings template
	(http://www.acm.org/sigs/publications/proceedings-templates)
	with a font size no smaller than 9pt;
    * be in PDF (make sure that the PDF can be viewed on any
	platform), and formatted for US Letter size;
    * occupy no more than six pages, including the abstract,
	references, and appendices.

It is the authors' responsibility to ensure that their submissions
adhere strictly to the required format.
Submissions that do not comply with the above guidelines may be
rejected without review.

All submissions must be entered into the reviewing
system:https://www.easychair.org/conferences/?conf=msm2016

Contact:
========
Martin Atzmueller, University of Kassel, Germany;atzmueller at cs.uni-kassel.de
Shlomo Berkovsky, CSIRO, Australia; shlomo.berkovsky at csiro.au
Alvin Chin, BMW Group, USA; alvin.chin at bmwna.com
Jianxin Li, University of Western Australia, Australia; Jianxin.li at uwa.edu.au
Christoph Trattner, Know-Center, Austria; trattner.christoph at gmail.com

Follow us on:
=============
Facebook https://www.facebook.com/groups/527164050627185/
Twitter https://twitter.com/msm_workshop

-- 
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Dipl.-Ing. Dr.techn. Christoph Trattner BSc
Know-Center
Research Center for Big Data Analytics & Data Driven Business
Graz University of Technology, Austria
E-mail: ctrattner at know-center.at
Tel: +43 650 2402801 <+43%20650%202402801>
Homepage: http://christophtrattner.info
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New book: Mining, Modeling, and Recommending 'Things' in Social Media*
by M. Atzmueller, A. Chin, C. Scholz, C. Trattner (eds.)
http://link.springer.com/978-3-319-14723-9
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