Manipulation Of Social Behaviour From Social Media Networks

Abstract

Recently, online social networks sites produce an enormous quantity of data such as Twitter, YouTube, and Facebook. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Analyzing this vast quantity of unstructured data presents challenges in knowing virtual community so that the properties of the social graph have been studied extensively, and has lead us to useful algorithms such as centrality, density, diameter etc. Social network analysis (SNA) is an important and valuable tool for knowledge extraction for such large and unstructured online social network data’s. This study provides brief introduction to representation and analysis of social networks, SNA models and methods. The aim of this study is to analyze Facebook Fun Page (DireTube.com) i.e. understanding network structure and content such as network density, degree distributions, sub-groups and key players in the network. The dataset of this study is collected using NodeXL and analyzed by python. This studies social network is made up of 128 users and 2521 links. The first sub-group and influential user’s identification measurement in online social networks was conducted, issues which are associated with the investigation and analysis of social relationships of people in Facebook was addressed such as network structure, focusing on how users are connecting to one another, importance of actors and detecting sub-communities in a given social network. This study presents strength and weakness of the given network and finally discusses the implications of these structural properties for the design of social network based systems. To the best of my knowledge this is the first study to analyze online social networks in Ethiopia