A special issue of the IEEE Computational Intelligence Magazine (IEEE CIM) will
be dedicated to Computational Intelligence for Big Social Data Analysis.
Prospective authors are invited to submit their original unpublished research
and application papers. Comprehensive tutorial and survey papers will also be
considered. For more information, please visit http://sentic.net/ci4bigdata

RATIONALE
In the era of social connectedness, Web users are becoming increasingly
enthusiastic about interacting, sharing, and collaborating through online
collaborative media. In recent years, this collective intelligence has spread to
many different areas, with particular focus on fields related to everyday life
such as commerce, tourism, education, and health, causing the size of the social
Web to expand exponentially. The distillation of knowledge from such a large
amount of unstructured information, however, is an extremely difficult task, as
the contents of today’s Web are perfectly suitable for human consumption, but
remain hardly accessible to machines.

Big social data analysis grows out of this need and combines disciplines such as
social network analysis, multimedia management, social media analytics, trend
discovery, and opinion mining. For example, studying the evolution of a social
network merely as a graph is very limiting as it does not take into account the
information flowing between network nodes. Similarly, processing social
interaction contents between network members without taking into account
connections between these is limited by the fact that information flows cannot
be properly weighted.

Big social data analysis, instead, aims to study large-scale Web phenomena such
as social networks from a holistic point of view, i.e., by concurrently taking
into account all the socio-technical aspects involved in their dynamic
evolution. Hence, big social data analysis is inherently interdisciplinary and
spans areas such as machine learning, graph mining, information retrieval,
knowledge-based systems, linguistics, common-sense reasoning, natural language
processing, and big data computing.

Besides these areas, the Special Issue also aims to cover application domains of
big social data analysis, e.g., stock market prediction, political forecasting,
time-evolving opinion mining, social network analysis, cyber-issue detection,
customer experience management, computer mediated human-human communication,
personalization and persuasion, human-agent, -computer and -robot interaction,
intelligent user interfaces, and social media marketing.

TOPICS
Big social data is high volume, high velocity, and high variety information
assets that require new forms of processing to enable enhanced sentiment
analysis, trend discovery and marketing prediction. The main motivation for this
Special Issue is to explore how computational intelligence can help process such
assets and, hence, enable a more efficient passage from (unstructured) social
information to (structured) machine-processable data, in potentially any domain.
Topics include, but are not limited to:
• Computational Intelligence for opinion mining
• Computational Intelligence for social network analysis
• Computational Intelligence for explicit and latent semantic analysis of big
social data
• Computational Intelligence for big social knowledge construction and
integration
• Computational Intelligence for transfer learning of big social data
• Computational Intelligence for time-evolving social data analysis
• Computational Intelligence for recommendation across heterogeneous social data
• Computational Intelligence for corpora and resources for big social data
analysis
• Computational Intelligence for social language normalization
• Computational Intelligence for multi-modal sentiment analysis
• Computational Intelligence for multi-domain and cross-domain evaluation
• Computational Intelligence for multi-lingual sentiment analysis

SUBMISSION PROCESS
The paper length for the manuscript is typically 20 pages in a single-column
double-space format including tables, figures and references (10 pages in a
two-column single-space format). Authors of papers should specify in the first
page of their manuscripts the corresponding author’s contact and up to 5
keywords. Submission should be made via EasyChair
(http://easychair.org/conferences/?conf=ci4bigdata).

TIMEFRAME
15th November, 2015: Submission of Manuscripts
15th January, 2016: Notification of Review Results
15th February, 2016: Submission of Revised Manuscripts
15th March, 2016: Submission of Final Manuscripts
August 2016: Publication

GUEST EDITORS
• Erik Cambria, Nanyang Technological University (Singapore)
• Newton Howard, MIT Media Lab (USA)
• Yunqing Xia, Tsinghua University (China)
• Tat-Seng Chua, NUS (Singapore)