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The Egyptian protest movement in the twittersphere: An investigation of dual sentiment pathways of communication

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  • Bang, Chulhwan Chris
  • Lee, Jaeung
  • Rao, H. Raghav

Abstract

During the course of the Egyptian civil movement in 2011, excessive suppression of the protesters caused a great deal of humanitarian concerns across the world. Egyptian protesters were supported not only in the Arabic-speaking world, but also throughout the English speaking world. The Twittersphere11Twittersphere is a term that represents an ecosystem formed around Twitter. The term is also known as the Twitterverse. became a valuable arena for individuals to communicate amongst each other regarding important social movement issues. This paper is a study of the communication on Twitterverse consisting of both English and Arabic tweets and the sentiments expressed therein during the Egyptian protest movement. We focus on the research questions: what sentiments of Tweeters relate to signals of protest communication?, and how do protest related tweets in two languages in the Twitter sphere, that are a proxy of two different and important cultural groups, compare with each other? In order to understand the protest communications in Twittersphere, we examine a dual pathways model that relates to emotional and goal related sentiments. We apply this model to examine the online protest in Egypt. Our findings reveal the emotions and goal related sentiments that are fundamental for intention to protest across the two languages. We find that anger, fear, pride and hope were the prime sentiments regarding intention to or support of protest, regardless of language.

Suggested Citation

  • Bang, Chulhwan Chris & Lee, Jaeung & Rao, H. Raghav, 2021. "The Egyptian protest movement in the twittersphere: An investigation of dual sentiment pathways of communication," International Journal of Information Management, Elsevier, vol. 58(C).
    • Handle: RePEc:eee:ininma:v:58:y:2021:i:c:s0268401221000219
    DOI: 10.1016/j.ijinfomgt.2021.102328
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    Cited by:

    1. Yoon Sang Lee & Chulhwan Chris Bang, 2022. "Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network," Information Systems Frontiers, Springer, vol. 24(6), pages 1795-1809, December.

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