Relational intelligence recognition in online social networks - a survey

Zhang, Ji and Tan, Leonard and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Pham, Thuan ORCID: https://orcid.org/0000-0001-7433-858X and Chen, Bing (2020) Relational intelligence recognition in online social networks - a survey. Computer Science Review, 35:100221. ISSN 1574-0137


Abstract

Information networks today play an important, fundamental role in regulating real life activities. However, many methods developed on this framework lack the capacity to adequately represent sophistication contained within the information it carries. As a result, they suffer from problems such as inaccuracies, reliability and performance. We define relational intelligence as a combination of affective (Cambria, 2016; 2015 [1,2]; Hidalgo et al., 2015 [3]), sentimental (Ferrara and Yang, 2015 [4]; Wang et al., 2013 [5]; Madhoushi et al., 2015 [6]) and ethical (Vayena et al., 2015 [7]; Nunan and Di Domenico, 2013 [8]; Anderson and Guyton, 2013 [9]) developments reflected in the evolving patterns of online social structures. These developments involve the ability of actors to adaptively regulate emotions, values, interest and demands between each other in an online social scene. In this paper, we provide a state-of-the-art overview of approaches used in recognizing relational intelligence-with special focus given to Online Social Networks (OSNs). The important core processes of data mining, identification (extraction), detection (labeling), classification, prediction and learning which empower machine recognition tasks will be discussed in detail. In addition, widely affected applications like recommending, ranking, influence, topic modeling, evolution, etc. will also be introduced along with their basic concepts uncovered to a detailed degree. We also include some discussions on more advanced topics that point to further interesting future research directions.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Historic - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 - 31 Mar 2020)
Date Deposited: 23 Apr 2020 23:46
Last Modified: 08 May 2020 02:28
Uncontrolled Keywords: artificial intelligence; deep learning; ensembles; pattern recognition; social networks
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1016/j.cosrev.2019.100221
URI: http://eprints.usq.edu.au/id/eprint/38125

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