Neha Patvardhan

Social media websites like Twitter and Facebook have become a treasure trove of data. Even today, people have not been able to fully comprehend the consequences, both positive and negative, of being on these websites. We have modeled the risks associated with such websites as a function of the population, i.e., the number of accounts present, along the lines of the tragedy of commons. We have tracked the variations between the average Strogartz Watts local clustering coefficient, the variance of Strogartz Watts local clustering coefficient and the global clustering coefficient as the number of accounts in a database increases. Regarding the average local and global clustering coefficient, researchers observed an initial phase of rapid increase followed by a phase of a continuous relatively smaller increase in their values. The variance of the average local clustering coefficient shows an initial phase of significant variation followed by a phase of continuous reduction in its value. Thus, the increase in the population size increases the transitivity of the network, increasing the risk associated with data being leaked via the website. The purpose of this research study is to simulate the social media connections and identify the application of the concept of ‘tragedy of commons’ in the social media domain. The researchers have also tried to look at insights obtained from simulations from a network theory perspective. The scope of this research study is to look at the way the connections are made on social media depending on the common interests of the people who use social media platforms. Using network theory, the researchers have tried to find out the connections structures. The researchers have used a simulation approach using a program that was written in C language on a UNIX system. Additional accounts to a hypothetical database were simulated using this program.