Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality.
This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis.
Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality.
This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks.
There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.
Joseph, N., Kar, A.K. and Ilavarasan, P.V. (2021), "How do network attributes impact information virality in social networks?", Information Discovery and Delivery, Vol. 49 No. 2, pp. 162-173. https://doi.org/10.1108/IDD-08-2020-0094
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