Abstract:
LinkedIn has become a key platform for professional networking, where follower count increasingly
reflects visibility, credibility, and digital influence. Existing research offers only limited
insight into how different factors jointly shape follower growth. Insights from other social media
platforms do not translate well to LinkedIn’s professional context. This study examines
factors influencing LinkedIn follower count using a multiinput deep learning model. The model
integrates three major data modalities professional, demographic, and facial-emotional features
allowing a comprehensive multimodal prediction approach. The study focuses on three key
areas: determining whether a 1D CNN based multimodal model performs better than classical
machine learning models; identifying which feature groups most strongly influence follower
count; and evaluating the extent to which a multi-input 1D CNN can learn complex non-linear
interactions more effectively than traditional approaches. Structured and LinkedIn profile data
and facial-emotional indicators obtained based on profile images were used in classification.
Standardized cleaning, one-hot encoding and MinMax scaling were used to process features.
The classical models used were compared to a 1D CNN with a multi-input. Accuracy, F1-
score, MAE, MSE and Explainable AI techniques were used as model evaluation. According
to the results, LinkedIn user following is mainly motivated by career advancement and profile
display and not by demographical factors. The research conclude that multimodal deep learning
is a highly effective way to predict and interpretable in professional network analytics, which
has both a methodological and practical implication on understanding digital influence.
