To Study the Impact of Social Network Analysis on Social Media Marketing Using Graph Theory

To Study the Impact of Social Network Analysis on Social Media Marketing Using Graph Theory

Rupsha Kar
DOI: 10.4018/IJSSCI.304437
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Abstract

Marketing requires an understanding of relationships and current research has progressed much beyond the simple dyadic relationships to look at how social media networks influence the behavior of customers. Social media's power is fascinating as a seemingly inconsequential figure emerges from the ruins and attracts tens of thousands, if not millions, of followers and thus providing an average individual a huge platform to interact with the rest of the world. Academics have used Network theory and formal network analysis approaches to harvest the large pool of social media influencers available on the internet. The goal of this paper is to use various graph theory algorithms to portray the impact of social network analysis on internet marketing, with a primary focus on social media influencers, and to illustrate a variety of network measurements ideas that may be employed in social media management research that takes into account the enormous social media communication network.
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1. Introduction

Currently social media forms an integral part of most human lives in this world. Anybody with a smartphone and internet connection has access to these social media sites, which are continuously growing in numbers every day. Approximately 4.66 billion people around the world are currently using the internet which is close to 60 percent of the world’s population. The Advent of social media have created a global environment that is vast and interconnected. Businesses now should assess how consumers interact on social networks and how their purchasing intents differ in order to attract more customers, comprehend their behaviour patterns and ambitions, and create intimate relationships with them. The majority of business, whether it's b2b, b2c, or c2c, centres about partnerships. Establishing and nurturing supplier-customer relationships, consolidating cross-functional associations within organisations, assessing how competitors are placed in the market, and determining when, how and to what level customers use their professional and personal contacts are all important concerns in the field. Collaboration, trust, authority, and choice are all complicated issues that go beyond simple formal interactions; in fact, the majority of them are imbedded in networks of connections.

Influencer marketing is one of the most rapidly expanding methods for attracting new clients to business websites. Influencer marketing, when done correctly, is a cost-effective way of promoting goods, people, or ideas, as well as delivering creative material to the firm and allowing you to reach target audiences in a natural way. An “influencer” is a person with a significant social media following who is compensated by businesses to advertise their products to their followers/subscribers/viewers in exchange for free products, trips, and/or monetary compensation per promotional post (Kadekova et al., 2018) The goal is to urge followers to buy things of a particular brand. Some of the most prominent social media channels for influencing are Facebook, Twitter, and Instagram. Because of their authority, education, position, or relationship with their audience, influencers have the power to influence others' buying decisions. It's important to realise that these individuals aren't simply marketing tools; they're also social connection assets that businesses can use to reach their marketing objectives.

The study of relationships and interactions between individuals, groups, and items is known as social network analysis (SNA). This method aids us in comprehending who collaborates with whom, how knowledge is provided or gained, how power is centralised or distributed within an entity, and how special interest groups shape and operate. We can measure the centrality of users in a network using SNA. The distribution of ties among the nodes is depicted by degree centrality (Alan E. & Mislove, 2009). Closeness centrality determines “how similar an actor is to all other actors in the network” (Catanese et al, 2012). Knowledge goes in a specific way from one actor to the next, and it might be directed or undirected (Caroline & Haythornthwaite, 2012). Closeness centralities (inside and out) deals with incoming and outgoing connections that are assessed independently. Betweenness is another sort of centrality that looks at how essential the actor is at filling the gap between all the other network actors (Katherine Faust. et al, 1994). Finally, eigenvector centrality states that an actor's centrality is influenced not just by the number of neighbours, but also by the centrality values of these neighbours (Abbasi et al, 2011).

Through this study the authors are attempting to suggest ways to apply various graph mining techniques like finding patterns in connections, classification of the most important links/person in the network, detection, or prediction of efficiency of the links, etc. to explore the social network of the people who are having a positive viewpoint about a product of interest to find interesting connections which will eventually help in propagating a better online presence for a product.

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