Coding Influencer Follower and Engagement Analysis

Abstract

With the rapid growth of social media platforms, coding influencers play a crucial role in disseminating programming knowledge, promoting tools, and shaping the learning behavior of aspiring developers. This paper presents an analytical study of coding influencers by examining their follower growth, engagement patterns, and content effectiveness across social media platforms. Using metrics such as likes, comments, shares, views, and engagement rate, the study identifies key factors that influence audience interaction. The analysis helps understand how content type, posting frequency, and platform choice impact engagement. The results provide insights for educators, developers, and marketers to optimize content strategies and enhance digital learning outreach.This research aims to conceptualize, develop, and validate a specific instrument for measuring the engagement of followers towards influencers on social media, and more specifically, in this first research, on Instagram.

 We surveyed (in-depth interviews, and questionnaires) 32 marketing experts and 1170 Instagram followers.Based on the applications of factor analysis and structural equation modelling, we determined 21 valid items. The scale assesses the cognitive, affective, and behavioral characteristics of follower’s engagement across five dimensions. The results provide insight into the interactive, personal, and social aspects of this type of virtual engagement. It is the first scale to measure this engagement in a multidimensional framework, which advances future research. Additionally, it will help managers identify the strongest dimensions of their influencers’ engagement and thus be able to adjust marketing communication strategies to foster multidimensional follower engagement and subsequent partnerships.