Influencer recommendation system: choosing the right influencer using a network analysis approach

• Published in: Marketing intelligence & planning Vol. 41; no. 8; pp. 1197 - 1212
• Main Authors: Jha, Abhishek Kumar, Ray, Sanjog
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 07.11.2023; Emerald Group Publishing Limited
• Subjects: Accuracy; Algorithms; Collaboration; Influencer marketing; Purchase intention; Recommender systems; Semantics; Social networks; Success; Social media influencers; Network analytics; Influencer marketing; Prediction model; Semantic network
• ISSN: 0263-4503; 1758-8049
• DOI: 10.1108/MIP-04-2023-0149

PurposeThe rise of social media has led to the emergence of influencers and influencer marketing (IM) domains, which have become important areas of academic inquiry. However, despite its prominence as an area for study, several significant challenges must be addressed. One significant challenge involves identifying, assessing and recommending social media influencers (SMIs). This study proposes a semantic network model capable of measuring an influencer's performance on specific topics or subjects to address this issue. This study can assist managers in identifying suitable SMIs based on their estimated reach.Design/methodology/approachData from popular YouTube influencers and publicly available performance measures (views and likes) are extracted. Second, the titles of the past videos made by the influencer are used to develop a semantic network connecting all the videos to other videos based on similarity measures. Third, the nearest neighbor approach extracts the neighbors of the target title video. Finally, based on the set of neighbors, a range prediction is made for the views and likes of the target video with the influencer.FindingsThe results show that the model can predict an accurate range of views and likes based on the suggested video titles and the content creator, with 69–78% accuracy across different influencers on YouTube.Research limitations/implicationsThe current study introduces a novel and innovative approach that exploits the textual association between a SMI's previous content to forecast the outcome of their future content. Although the findings are encouraging, this research recognizes various constraints that upcoming researchers may tackle. Forecasting views of posts concerning novel subjects and precisely adjusting video view counts based on their age constitute two primary limitations of this study.Practical implicationsManagers interested in hiring influencers can employ the suggested approach to evaluate an influencer's potential performance on a specific topic. This research aids managers in making informed decisions regarding influencer selection, utilizing data-based metrics that are simple to comprehend and explain.Originality/valueThe study contributes to outreach evaluation and better estimating the impact of SMIs using a novel semantic network approach.