Can information be spread as a virus? Viral Marketing as epidemiological model
November 08, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Helena Sofia Rodrigues, Manuel JosΓ© Fonseca
arXiv ID
1611.04529
Category
cs.SI: Social & Info Networks
Cross-listed
physics.soc-ph
Citations
52
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In epidemiology, an epidemic is defined as the spread of an infectious disease to a large number of people in a given population within a short period of time. In the marketing context, a message is viral when it is broadly sent and received by the target market through person-to-person transmission. This specific marketing communication strategy is commonly referred as viral marketing. Due to this similarity between an epidemic and the viral marketing process and because the understanding of the critical factors to this communications strategy effectiveness remain largely unknown, the mathematical models in epidemiology are presented in this marketing specific field. In this paper, an epidemiological model SIR (Susceptible- Infected-Recovered) to study the effects of a viral marketing strategy is presented. It is made a comparison between the disease parameters and the marketing application, and Matlab simulations are performed. Finally, some conclusions are carried out and their marketing implications are exposed: interactions across the parameters suggest some recommendations to marketers, as the profitability of the investment or the need to improve the targeting criteria of the communications campaigns.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Social & Info Networks
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
π»
Ghosted
Natural Scales in Geographical Patterns
R.I.P.
π»
Ghosted
Representation Learning on Graphs: Methods and Applications
R.I.P.
π»
Ghosted
The COVID-19 Social Media Infodemic
R.I.P.
π»
Ghosted
OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted