Title: Propagation of user-generated content online
Authors: Harsha Gangadharbatla; Masoud Valafar
Department of Advertising, Public Relations and Media Design, College of Media, Communication and Information, Boulder, CO 80309, USA
Twitter, San Francisco, CA, USA
Abstract: In this paper, using a large amount of data collected from social media, we test theories of information propagation that are popular and have been applied extensively as theoretical frameworks in advertising and marketing literature. More specifically, we crawled Twitter in two waves for over 30 days to capture information from a sample of 300,000 users to test two-step flow and diffusion of information theories. Findings support the two-step flow theory and suggest that a minority of users account for a majority of influence, opinion leaders follow other opinion leaders to form a community of influencers, and information dissemination on Twitter follows a power-law distribution. These results are contrary to the popular notion that social media are democratic and, without a gatekeeper, everyone with a smartphone can broadcast messages. Managerial implications for advertising professionals are drawn.
Keywords: user-generated content; big data; social media; social networking sites; two-step flow; information propagation; information dissemination; diffusion of information.
Int. J. of Internet Marketing and Advertising, 2017 Vol.11, No.3, pp.218 - 232
Submission date: 08 Nov 2016
Date of acceptance: 30 Dec 2016
Available online: 31 Jul 2017