Effects of source influence and peer referrals on information diffusion in Twitter

Purpose-Social media have attracted attention as an information channel for content generated in heterogeneous internet services. Focusing on social media platforms, the purpose of this paper is to examine the factors behind social transmission with content crossover from other services through hypertext link (URL). The authors investigate the effects of source influence and peer referrals on diffusion outcome and address their variations in the case of content crossover. Design/methodology/approach-The authors use a Poisson regression model due to the discrete nature of the dependent variable. The authors conduct an empirical study using 233 million real transaction data generated by 1,203,196 Korean users of Twitter. Findings-Source influence and peer referral have a positive impact on cascade size in the content dissemination process. In the case of content crossover, the impact of source influence decreases. However, the impact of peer referrals increases in the process of external content dissemination. Research limitations/implications-The authors demonstrate source and peer effects on content diffusion and that these effects vary when shared content is linked from an external service by a URL. Practical implications-The findings indicate that firms that wish to diffuse information through social media or enter the social media with new services to provide new ways of creating and sharing content should understand the nature of the social transmission process. Originality/value-Given the growing popularity of social media, particularly SNSs with online social networks as information channels, the authors first consider online social transmission as a user-driven diffusion process. Based on social factors in the diffusion process, the authors derive source and peer effects on the social transmission process.
Publisher
EMERALD GROUP PUBLISHING LTD
Issue Date
2017-05
Language
English
Keywords

WORD-OF-MOUTH; SOCIAL NETWORK; POPULARITY; PREDICT; TWEETS; SYSTEM

Citation

INDUSTRIAL MANAGEMENT DATA SYSTEMS, v.117, no.5, pp.896 - 909

ISSN
0263-5577
DOI
10.1108/IMDS-07-2016-0290
URI
http://hdl.handle.net/10203/225174
Appears in Collection
MT-Journal Papers(저널논문)
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