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Quantifying Virality of Information in Online Social Networks

Quantifying Virality of Information in Online Social Networks

Abhishek Vaish, Rajiv Krishna G., Akshay Saxena, Dharmaprakash M., Utkarsh Goel
Copyright: © 2012 |Volume: 4 |Issue: 1 |Pages: 14
ISSN: 1942-9010|EISSN: 1942-9029|EISBN13: 9781466614659|DOI: 10.4018/jvcsn.2012010103
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MLA

Vaish, Abhishek, et al. "Quantifying Virality of Information in Online Social Networks." IJVCSN vol.4, no.1 2012: pp.32-45. http://doi.org/10.4018/jvcsn.2012010103

APA

Vaish, A., G., R. K., Saxena, A., M., D., & Goel, U. (2012). Quantifying Virality of Information in Online Social Networks. International Journal of Virtual Communities and Social Networking (IJVCSN), 4(1), 32-45. http://doi.org/10.4018/jvcsn.2012010103

Chicago

Vaish, Abhishek, et al. "Quantifying Virality of Information in Online Social Networks," International Journal of Virtual Communities and Social Networking (IJVCSN) 4, no.1: 32-45. http://doi.org/10.4018/jvcsn.2012010103

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Abstract

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

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