Influence and social networks

Abstract

Studying large-scale social networks can be a complex and challenging task when considering social media's rapid development. Mapping large networks and studying interactions present barriers in terms of access to data, limitations on analysis, and an approach to identify unseen influencers in the network. This study examines how connections between data points and users in a network can be mapped and understood. This method of mapping connections can allow a researcher to identify influencers within a network and find optimal routes through which content can be distributed to a broad group of connected users. This is accomplished by comparing the role of network groups to that of users. This is done by mapping organizations and connected groups of students on social media networks over time to identify influential network members. The project involves studying several campus and community-based pro-Israel student groups organized into four geographically themed clusters. Data was collected from Twitter using various methods, including Python language code and NodeXL. Once the data was collected and analyzed using network link analysis and statistics for interconnections, visualizations and sociograms were generated using Gephi. Through analyzing network data for users and organizations, network statistics and metrics can be calculated to identify network influencers. The study shows that otherwise unseen influencers can be mapped within a social network and that their relative social influence can be identified. Studying organizations and exponential mapping layers of connected users reveals new connections and patterns. The relative social influence, position, and communication patterns within a network generate new insights into network members. Hidden influencers were identified and show a connection between users and otherwise unknown clusters of the network. The study results show that influencers can be identified and mapped within large and complex networks and that their relative social influence can be quantitatively calculated. This has implications for disseminating information within a network, mapping complex interactions within a social network, and understanding the structural communication pathways of social networks. This approach can be used in market analysis, research, and other social networks.