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- Date: Sat, 11 Jul 2015 01:28:38 -0500 (EST)
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Apologies for cross-posting, 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 Prospective authors are invited to submit their original unpublished research and application papers.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)
Received on Saturday, 11 July 2015 06:33:54 UTC