The linguistic features driving information diffusion on Twitter: The case of #RevolutionNow in Nigeria



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Prior research investigating political activism on social media concluded that that the number of followers, importance of a matter, and frames of a movement are factors that drive information diffusion. However, little research attention has been given to the linguistic features of highly diffused messages, although social media by nature facilitates information diffusion via messages. Therefore, this study utilizes Linguistic Word Count (LIWC) along with SPSS to explore the linguistic nature of words that drive diffusion in the case of #RevolutionNow in Nigeria between July 31, 2019 and August 29, 2019. This study also addresses the limitations of prior research that have measured diffusion on Twitter and/or social media simply in terms of shares and retweets. In this study, diffusion is measured in terms of retweets, likes, and replies that a tweet attracts. This study will yield practical strategies for future political movements on social media.

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Twitter, Social media, Political activism, Hashtags, Protests, Diffusion of innovations, Information diffusion, Linguistic Inquiry and Word Count (LIWC)