Evaluating the Impact of Influencer Marketing Using Social Network Analysis (SNA) Techniques

The way businesses plan their advertisements has changed as a result of the rapid rise in the number of consumers on different social networking sites. Businesses are currently focusing on influencer marketing (IM), which involves hiring freelance celebrities to market their items on social networking sites, to capitalize on the enormous scale of online communities. Social networking assessment (SNA) study is experiencing significant ethical issues as a consequence of technological advancements and a growing movement toward establishing ethical standards. Online social media accounts for a sizable portion of human communication. Thus, this research examines the impact of IM (such as., posts) are quantified by the quantity of likes and responses. In this study, the Gradient Boosted Decision Tree (GBDT) and the Chaos Gradient-oriented Algorithm (CGOA) are used. Additionally, this investigation offers a GBDT-CGOA paradigm and a productive way to find individuals who have the power to impact other people's conduct in the future. By employing a system that measures and predicts users' popularity in various areas and at various points employing machine learning algorithms and semantic evaluation, this research aims to identify domain-oriented social influencers for marketing. The outcomes demonstrate the effectiveness and utility of the GBDT-CGOA approach. The investigation's findings indicate that the GBDT-CGOA system is capable of accurately classifying ambiguous statistics, which expedites and improves processing efficiency.