IEEE TRANSACTIONS ON MULTIMEDIA - Special Issue on Social Media as Sensors |
16 August 2012 |
This special issue targets a mixed audience of researchers from several communities, i.e. computer vision, multimedia analysis, data mining, machine learning, information extraction, social networks, complex systems, and information retrieval. The emphasis of the special issue is on recognition, discovery, and detection of topics and real-world events, as well as on monitoring and prediction of trends and events using social media sites as sensors. Both theoretical contributions and applications validated on larges-cale social media datasets are welcome. |
Web 2.0 applications and social media have transformed the Web into an interactive sharing platform where users upload data and media, comment on and share this content within their social circles. Each content item is associated with an abundance of metadata and related information such as location, tags, comments, favourites, access patterns and logs etc. At the same time all this information is implicitly or explicitly interconnected based on various properties such as social links between users, groups, communities, etc. These properties transform social media to data sources of an extremely dynamic nature that reflect topics of interests, events, and the evolution of community opinion and focus. Social media offer a unique opportunity to structure and extract information and to benefit multiple areas ranging from computer vision to sociology and marketing. Topics of interest include, but are not limited to:
SUBMISSION PROCEDURE: Prospective authors should submit high quality, original manuscripts that have not appeared, nor are under consideration, in any other journals. Manuscripts should be submitted electronically through the online IEEE manuscript submission system. All papers will be reviewed by at least three independent reviewers. Papers should be formatted according to the IEEE Transactions on Multimedia guidelines for authors IMPORTANT DATES:
GUEST EDITORS: Yiannis Kompatsiaris, CERTH-Informatics and Telematics Institute, Greece Daniel Gatica-Perez, Idiap Research Institute, Swiss Federal Institute of Technology (EPFL) , Switzerland Jiebo Luo, University of Rochester, US Xing Xie, Microsoft Research Asia
|