CLOSED Call for Papers: Special Issue on Big Cross-Modal Social Media Data Analytics with Deep Intelligence

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Submissions Due: 30 June 2020

Submission deadline: 30 June 2020
Publication: October-December 2020

Big social-media data is heralded as a powerful new resource for social media, multimedia data, and business analytics. The excitement around big multimedia data (e.g., social multimedia data, healthcare data, or business data) emerges from the recognition of the opportunities. It may advance our understanding of human behavior and social phenomena in a way that has never been possible before. In specific areas of social investigation, these different modal of big social-media data will often require analytical cross-modal approaches which are vital for undertaking appropriate analyses for big multimedia data. Recent advances of deep learning have been witnessed over visual data analytics, such as images, video, and textual data, by learning high-level discriminative feature spaces to benefit a lot of problems. The massive data generated from various social-media platforms and digitalized systems offers researchers unprecedented opportunities to study user behavior patterns and understand practical implications for various applications, which were previously difficult to explore due to the lack of data and effective deep architectures. Now, with the massive quantity of social-media data and associated useful information available, more real-world social-media problems may be understood, analyzed, and possibly addressed, including but not limited to anomalous (or even criminal) individuals and groups, multimedia data analytics, financial crises, disaster response, and all with deep intelligence.

This special issue of IEEE MultiMedia will offer a timely collection of research updates to benefit researchers and practitioners working in fields ranging from media computing, machine learning, and data mining, to business analytics. Social researchers and business agencies increasingly having access to large-scale social-media data will be informed with new disciplines to study all social phenomena. To this end, we solicit original research papers addressing topics such as (but not limited to):

  • Deep model design over big multi-modal social-media data
  • Big healthcare and diagnosis data with deep models
  • Deep learning on cross-modal social-media disadvantage studies
  • Quantitative multimedia data analysis
  • Data mining on big cross-modal social-media networks
  • Social behavior modelling, understanding, and patterns mining with deep models
  • Smart cities, smart mobility, and urban informatics
  • Computational social-media computing and applications
  • Social-media analytics and societal behavior for prediction
  • Big electronic health record data evaluations with deep models

Submission Guidelines

Only submissions that describe previously unpublished, original, state-of-the-art research and that are not currently under review by a conference or journal will be considered. Extended versions of conference papers must be at least 30 percent different from the original conference works. Articles submitted to IEEE MultiMedia should not exceed 6,500 words, including all text, the abstract, keywords, bibliography, biographies, and table text. The word count should include 200 words for each table and figure. There is a maximum of 20 references for final manuscripts. Authors should be aware that IEEE MultiMedia cannot accept or process papers that exceed this word limit. Articles should be understandable by a broad audience of computer science and engineering professionals, avoiding a focus on theory, mathematics, jargon, and abstract concepts. All manuscripts must be submitted to ScholarOne Manuscripts by the deadline in order to be considered for publication. Submissions are subject to peer review on both technical merit and relevance to IEEE MultiMedia readership. Accepted papers must be well written and understandable, as the level of editing will be a light copyedit. For accepted papers, authors will be required to provide electronic files for each figure according to the following guidelines: for graphs and charts, authors must submit them in their original editable source format (PDF, Visio, Excel, Word, PowerPoint, etc.); for screenshots or photographs, authors must submit high-resolution files (300 dpi or higher at the largest possible dimensions) in JPEG or TIFF formats.

For author guidelines and information on how to submit a manuscript, visit

For full paper submission, visit


Please direct any correspondence before submission to the guest editors at

Guest editors:

  • Prof. Yang Wang, Hefei University of Technology, China
  • Dr. Meng Fang, Tecent AI, China
  • Dr. Joey, Tianyi Zhou, A-Star, Singapore
  • A/Prof. Tingting Mu, The University of Manchester, UK
  • ARC Laurate Professor, Dacheng Tao, The University of Sydney, Australia