Haji Heydari, N. orcid.org/0000-0003-3663-5254, Salehi, M. and Goudarzi, A. (2018) Optimizing humanitarian aids : formulating influencer advertisement in social networks. In: Camarinha-Matos, L.M., Afsarmanesh, H. and Rezgui, Y., (eds.) Collaborative Networks of Cognitive Systems. Working Conference on Virtual Enterprises, PRO-VE 2018, 17-19 Sep 2018, Cardiff, UK. Springer , pp. 101-110. ISBN 978-3-319-99126-9
Abstract
In order to solve problems encountered during natural disasters, in addition to NGOs and relief teams, various individuals intend to help the injured. Although the cooperation of people has remarkable advantages, the disparity between the needs of the injured and the people’s donations can cause problems such as trouble for relief teams and wasting the substantial resources. In generic, the influencer selection in the marketing endeavors is mainly aimed to maximize people’s awareness and attention, but this research proposes a method for influencer selection, using Social Network Analysis (SNA) and optimization techniques, by which it is possible to establish an adaptation between the public attention and the type of injured necessities. The proposed method is applied to a real sample network of Facebook friends, to evaluate the efficiency and validity of the formulated method.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © IFIP International Federation for Information Processing 2018. This is an author-produced version of a paper subsequently published in proceedings of the 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Disaster management; Humanitarian Aid; Advertisement; Influencer selection; Social Network Analysis; Optimization; Influence maximization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Aug 2019 11:25 |
Last Modified: | 19 Aug 2019 00:41 |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-99127-6_9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149094 |