A Statistical Model to Determine the Behavior Adoption in Different Timestamps on Online Social Network

A Statistical Model to Determine the Behavior Adoption in Different Timestamps on Online Social Network

Dhrubasish Sarkar (Amity University Kolkata, Kolkata, India), Sohom Roy (IBM India Pvt Ltd, Kolkata, India), Chandan Giri (Indian Institute of Engineering Science and Technology, Shibpur, India) and Dipak K. Kole (Department of CSE, Jalpaiguri Government Engineering College, Jalpaiguri, India)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJKSS.2019100101

Abstract

In this article, a statistical model has been proposed to determine the behavior adoption among the users in different timestamps on online social networks by using vector space models and term frequency – inverse document frequency techniques. The concepts of herd behavior and collective behavior have been used successfully in the proposed model. The result has been generated after analyzing the collected dataset. The result analysis shows the diffusion of information among the participants from an initial timestamp to later timestamps.
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Common Terms And Concepts

Brief idea of few terms and concepts which are used to build up the model.

Herd Behavior

Herd behavior explains when a group of individuals does actions that are highly correlated but not with any plans, where network is observable and only public information is available (Zafarani et al., 2014).

Collective Behavior

A group of individuals are behaving in a similar way. It might be planned and organized, but often it is spontaneous and unplanned (Zafarani et al., 2014). The concept was introduced and defined by sociologist Robert Park.

Examples:

  • Individuals standing in line for a new product release;

  • Posting messages online to support a cause or to show support for an individual.

Collective behavior can be analyzed by analyzing individuals performing the behavior and then put together the results of these analyses. The result would be the expected behavior for a large population. It is popular for Prediction purposes.

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