User response to e-WOM in social networks : how to predict a content influence in Twitter

Dahka, Z.Y., Haji Heydari, N. orcid.org/0000-0003-3663-5254 and Rouhani, S. (2020) User response to e-WOM in social networks : how to predict a content influence in Twitter. International Journal of Internet Marketing and Advertising, 14 (1). pp. 19-47. ISSN 1477-5212

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Copyright, Publisher and Additional Information: © 2020 Inderscience Publishers. This is an author-produced version of a paper subsequently published in International Journal of Internet Marketing and Advertising. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: electronic word-of-mouth; e-WOM; social media; e-retailing; content influence; data mining; Twitter; text mining
Dates:
  • Accepted: 29 May 2019
  • Published (online): 20 March 2020
  • Published: 20 March 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Sheffield University Management School
Depositing User: Symplectic Sheffield
Date Deposited: 23 Jul 2019 13:40
Last Modified: 01 Apr 2020 13:03
Status: Published
Publisher: Inderscience
Refereed: Yes
Identification Number: https://doi.org/10.1504/IJIMA.2019.10024249

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