Network Structure, Network Flows and the Phenomenon of Influence in Online Social Networks: An Exploratory Empirical Study of Twitter Conversations about YouTube Product Categories

Author

Nitin Venkat Mayande, Portland State UniversityFollow

Sponsor

Portland State University. Department of Engineering and Technology Management

First Advisor

Charles M. Weber

Date of Publication

Summer 8-6-2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Technology Management

Department

Engineering and Technology Management

Language

English

Subjects

Online social networks -- Marketing, Online social networks -- Influence, Information technology -- Research

DOI

10.15760/etd.2463

Physical Description

1 online resource (xv, 528 pages)

Abstract

Traditional marketing models are swiftly being upended by the advent of online social networks. Yet, practicing firms that are engaging with online social networks neither have a reliable theory nor sufficient practical experience to make sense of the phenomenon. Extant theory in particular is based on observations of the real world, and may thus not apply to online social networks. Practicing firms may consequently be misallocating a large amount of resources, simply because they do not know how the online social networks with which they interact are organized.

The purpose of this dissertation is to investigate how online social networks that are in stark contrast to real-world social networks behave and how they get organized. In particular, I explore how network structure and information flow within the network impact each other, and how they affect the phenomenon of influence in online social networks. I have collected retrospective data from Twitter conversations about six YouTube product categories (Music, Entertainment, Comedy, Science, Howto and Sports) in continuous time for a period of three months. Measures of network structure (Scale Free Metric, Assortativity and Small World Metric), network flows (Total Paths, Total Shortest Paths, Graph Diameter, Average Path Length, and Average Geodesic Length) and influence (Eigenvector Centrality/Centralization) were computed from the data. Experimental measures such as power law distributions of paths, shortest paths and nodal eigenvector centrality were introduced to account for node-level structure. Factor analysis and regression analysis were used to analyze the data and generate results.

The research conducted in this dissertation has yielded three significant findings.

1. Network structure impacts network information flow, and conversely; network flow and network structure impact the network phenomenon of influence. However, the impact of network structure and network flow on influence could not be identified in all instances, suggesting that it cannot be taken for granted.

2. The nature of influence within a social network cannot be understood just by analyzing undirected or directed networks. The behavioral traits of individuals within the network can be deduced by analyzing how information is propagated throughout the network and how it is consumed.

3. An increase or decrease in the scale of a network leads to the observation of different organizational processes, which are most likely driven by very different social phenomena. Social theories that were developed from observing real-world networks of a relatively small scale (hundreds or thousands of people) consequently do not necessarily apply to online social networks, which can exhibit significantly larger scale (tens of thousands or millions of people).

The primary contribution of this dissertation is an enhanced understanding of how online social networks, which exhibit contrasting characteristics to social networks that have been observed in the real world, behave and how they get organized. The empirical findings of this dissertation may allow practicing managers that engage with online social networks to allocate resources more effectively, especially in marketing. The primary limitations of this research are the inability to identify the causes of change within networks, glean demographic information and generalize across contexts. These limitations can all be overcome by follow-on studies of networks that operate in different contexts. In particular, further study of a variety of online social networks that operate on different social networking platforms would determine the extent to which the findings of this dissertation are generalizable to other online social networks. Conclusions drawn from an aggregation of these studies could serve as the foundation of a more broadly-based theory of online social networks.

Rights

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Persistent Identifier

http://archives.pdx.edu/ds/psu/15888

Recommended Citation

Mayande, Nitin Venkat, "Network Structure, Network Flows and the Phenomenon of Influence in Online Social Networks: An Exploratory Empirical Study of Twitter Conversations about YouTube Product Categories" (2015). Dissertations and Theses. Paper 2465.
https://doi.org/10.15760/etd.2463