CJ 2013 - Special Issue on Mining Social Media for Knowledge Discovery

Web 2.0 applications have transformed the internet into an interactive sharing platform where users share contents within their circles. With the ever-rising popularity of social media (e.g. Facebook, Twitter, Yahoo Answer, etc.), some sort of on-the-fly experiences are being learned and obtained by daily users. This type of experience has become a particular way to cultivate domain knowledge. One typical instance is friend-sourcing. People search the web to find solutions for issues that they are dealing with, rather than finding them through conventional methods. This phenomenon not only reveals that problems are about to be solved but the process that knowledge is discovered based upon the experiences from the publics. Despite a wide spectrum of research development efforts, the question of how to prompt the knowledge discovery from an open environment (i.e. a socialised web) and provide corresponding services (e.g. decision support) remains an open challenge.
This special issue aims to foster the dissemination of high-quality research in methods, theories and techniques concerning the knowledge discovery from social media in the coming era. Its emerging applications and usages that provide tailored and precise solutions wherever and whenever human beings are active are extremely interesting. Original and research articles are solicited in all aspects of social media mining, including theoretical studies, practical applications and experimental prototypes. All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue.
**Submission Guidelines**
This special issue calls for original papers describing the latest developments, trends and solutions of active media technology and its applications. Topics of interests include, but are not limited to:
? Theoretical analysis on social media mining
? Analysis, organization and classification of social media
? Community detection, evolution and clustering in social media
? Cross space heterogeneous media analysis
? Information and spread and transformation
? Human interface and interaction techniques for social media
? Ontology construction and semantic inference
? Spatial-temporal method for event discovery and detection
? Structure, organization and storage issues for user-generated content
? Context-awareness, collective intelligence for decision-making support
? Human-centred studies and experiences
? Personalization, Adaptation, and recommender systems
? Security, privacy, and data integrity
? Tools, systems, applications, and services
Submitted papers should be submitted via The Computer Journal's online submission system, Manuscript Central, at http://mc.manuscriptcentral.com/compj. They should be accompanied by a cover letter that clearly states that the submission is for the Special Issue on “Mining Social Media for Knowledge Discovery”.
All submitted papers will be peer reviewed according to the usual standards of this journal, and will be evaluated on the basis of originality, quality and relevance to this Special Issue and the journal, and on the basis of clarity and correct use of English.
The submitted papers should be formatted according to the journal style. For more detailed information concerning the requirements for submission, please refer to the journal homepage at: www.oxfordjournals.org/our_journals/computer_journal/for_authors/msprep_submission.html
**Important Dates**
Paper submission: July 15, 2013
1st round review notification: Sept. 15, 2013
1st revision due: Oct. 15, 2013
2nd round review notification: Nov. 5, 2013
2nd revision due: Nov. 30, 2013
Final acceptance: Dec. 15, 2013
Publication (tentative): 3rd or 4th quarter, 2014
**Guest editors**
Prof. James J. Park (Corresponding GE)
Seoul National University of Science and Technology (SeoulTech), Korea
Website: http://www.parkjonghyuk.net
Email: parkjonghyuk1-AT-hotmail.com
Prof. Uyen Trang Nguyen
York University, Canada
Web: www.cse.yorku.ca/~utn
Email: utn-AT-cse.yorku.ca
Prof. Neil Y. Yen
The University of Aizu, Japan
Website: https://sites.google.com/site/nywyen
Email: neilyyen-AT-u-aizu.ac.jp