** Please forward to anyone who might be interested ** -------------------------------------------------------------- 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 ACM WWW 2017 http://www.kde.cs.uni-kassel.de/ws/msm2017/ -------------------------------------------------------------- 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@cs.uni-kassel.de Shlomo Berkovsky, CSIRO, Australia; shlomo.berkovsky@csiro.au Alvin Chin, BMW Group, USA; alvin.chin@bmwna.com Jianxin Li, University of Western Australia, Australia; Jianxin.li@uwa.edu.au Christoph Trattner, Know-Center, Austria; trattner.christoph@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@cs.uni-kassel.de Shlomo Berkovsky, CSIRO, Australia; shlomo.berkovsky@csiro.au Alvin Chin, BMW Group, USA; alvin.chin@bmwna.com Jianxin Li, University of Western Australia, Australia; Jianxin.li@uwa.edu.au Christoph Trattner, Know-Center, Austria; trattner.christoph@gmail.com Follow us on: ============= Facebook https://www.facebook.com/groups/527164050627185/ Twitter https://twitter.com/msm_workshop -- ------------------------------------------------------- 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@know-center.at Tel: +43 650 2402801 Homepage: http://christophtrattner.info ------------------------------------------------------- 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 -------------------------------------------------------