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

Author

Listed:
  • Zohreh Yousefi Dahka
  • Nastaran Hajiheydari
  • Saeed Rouhani

Abstract

The purpose of this research is to find influential factors on electronic word-of-mouth effectiveness for e-retailers in Twitter social media, applying data mining and text mining techniques and through R programming language. The relationship between using hashtag, mention, media and link in the tweet content, length of the content, the time of being posted and the number of followers and followings with the influence of e-WOM is analysed. 48,129 tweets about two of the most famous American e-retailers, Amazon and eBay, are used as samples; results show a strong relationship between the number of followers, followings, the length of the content and the effectiveness of e-WOM and weaker relevance between having media and mention with e-WOM effectiveness on Twitter. Findings of this paper would help e-retailing marketers and managers to know their influential customers in social media channels for viral marketing purpose and advertising campaigns.

Suggested Citation

  • Zohreh Yousefi Dahka & Nastaran Hajiheydari & Saeed Rouhani, 2020. "User response to e-WOM in social networks: how to predict a content influence in Twitter," International Journal of Internet Marketing and Advertising, Inderscience Enterprises Ltd, vol. 14(1), pages 91-111.
  • Handle: RePEc:ids:ijimad:v:14:y:2020:i:1:p:91-111
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    Cited by:

    1. Saini, Munish & Singh, Madanjit & Kaur, Manpreet & Kaur, Manevpreet, 2021. "Analysing the tweets to examine the behavioural response of Indian citizens over the approval of national education policy 2020," International Journal of Educational Development, Elsevier, vol. 82(C).

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