Influence in Microblogs: Impact of User Behavior on Diffusion and Engagement

41 Pages Posted: 13 Jan 2014

See all articles by Prem Swaroop

Prem Swaroop

University of Maryland - Decision and Information Technologies Department

Yogesh V. Joshi

University of Maryland - Department of Marketing

William Rand

North Carolina State University

Louiqa Raschid

University of Maryland - Decision and Information Technologies Department; University of Maryland - College of Computer, Mathematical and Natural Sciences; University of Maryland - Robert H. Smith School of Business

Date Written: January 12, 2014

Abstract

Feature-rich social media can reveal many facets of individual user behavior; yet it is difficult to model such behavior due to both the noise and the overwhelming volume and heterogeneity in the data. In this paper, we address these challenges by building a model of user behavior in social media to understand the impact of users' actions on key influence outcomes. We specify models, inspired by social learning theory, that examine the outcome measures of followership (audience), diffusion (reach) and conversations (engagement) as functions of individual actions taken by a focal user within the medium. Our unique panel dataset is prepared by capturing and mining a snowball network of several thousand Twitter users and their activities over an extended period of time.

Our results confirm that users' choices about specific actions can indeed positively affect performance on the above outcome measures; and establish a significant role for reciprocity within each outcome. We report several nuances of individual user behavior that may inform managers tasked with designing and executing social media strategies for firms. We find that creating fresh content of specific types as well as engaging in conversations are most effective in improving diffusion as well as leading to conversations. Interestingly, diffusing messages of other users in a focal user's reciprocal network helps with the focal user's own diffusion and conversations; whereas doing so for users outside the reciprocal network tends to hurt own diffusion and conversations.

Keywords: Big Data, Social Media, Microblogs, Influence, Diffusion of Information, Count Models, Hurdle Models

Suggested Citation

Swaroop, Prem and Joshi, Yogesh V. and Rand, William and Raschid, Louiqa, Influence in Microblogs: Impact of User Behavior on Diffusion and Engagement (January 12, 2014). Robert H. Smith School Research Paper. Available at SSRN: https://ssrn.com/abstract=2378094 or http://dx.doi.org/10.2139/ssrn.2378094

Prem Swaroop (Contact Author)

University of Maryland - Decision and Information Technologies Department ( email )

Robert H. Smith School of Business
4313 Van Munching Hall
College Park, MD 20815
United States

Yogesh V. Joshi

University of Maryland - Department of Marketing ( email )

College Park, MD 20742
United States

William Rand

North Carolina State University ( email )

Raleigh, NC 27695
United States

Louiqa Raschid

University of Maryland - Decision and Information Technologies Department ( email )

Robert H. Smith School of Business
4313 Van Munching Hall
College Park, MD 20815
United States

University of Maryland - College of Computer, Mathematical and Natural Sciences ( email )

2300 Symons Hall,
University of Maryland
College Park, MD 20742-3255
United States

University of Maryland - Robert H. Smith School of Business

College Park, MD 20742-1815
United States

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