Relational intelligence recognition in online social networks - a survey
Article
Article Title | Relational intelligence recognition in online social networks - a survey |
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ERA Journal ID | 212145 |
Article Category | Article |
Authors | Zhang, Ji (Author), Tan, Leonard (Author), Tao, Xiaohui (Author), Pham, Thuan (Author) and Chen, Bing (Author) |
Journal Title | Computer Science Review |
Journal Citation | 35 |
Article Number | 100221 |
Number of Pages | 33 |
Year | 2020 |
Publisher | Elsevier BV |
Place of Publication | Netherlands |
ISSN | 1574-0137 |
1876-7745 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cosrev.2019.100221 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1574013718303575 |
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. |
Keywords | artificial intelligence; deep learning; ensembles; pattern recognition; social networks |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
School of Agricultural, Computational and Environmental Sciences | |
Nanjing University of Aeronautics and Astronautics, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59y9/relational-intelligence-recognition-in-online-social-networks-a-survey
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