Prediction of Online Social Networks Users' Behaviors with a Game Theoretic Approach

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Conference Proceeding

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Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE Annual



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In June 2016, there were an estimated 1.55 billion active users on the most popular social media platform, Facebook. As of April 26th, 2017, there were an estimated 700 million active users on Instagram. With the growing popularity of social media, community behaviors are linked to the number of people in a users follower-base and how they respond to the users posts. Instagram users are constantly competing against each other in a race, each vying to get more followers, more likes on their posts, more comments on their posts, more views of videos, etc. We used game theory to cast two Instagram users as players in a game where both of the players are trying to obtain the optimal community. This optimal community has the maximum amount of followers possible with no negativity. Additionally, Fortetsanakis et al. [1] incorporated game theory and found the Nash Equilibrium to develop an outline for analysing any outliers of utility functions of providers. Xia et al. [2] proposed a game to comprehend the uncertain cooperation between nodes using Bayesian Nash equilibrium. Nash equilibrium can be complicated, and Hajibagheri et al. [3] addressed the complication of communities. Zhan et al. The purpose of this project is to see how game theory can predict the actions of modern-day social media [4]. Game theory is based on two users who are both trying to achieve the same thing. In the game we created, the aim of these players is to be Instafamous (having a large quantity of followers and being widely known by other Instagram users). However, they might lose followers because of the negativity in their community.


Comments; Followers; Likes; Social networks


Electrical and Computer Engineering



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