Recognition of a user's influence level has attracted much attention as human interactions move online. Influential users have the ability to sway others' opinions to achieve some goals. As a result, predicting users' level of influence can help to understand social networks, forecast trends, prevent misinformation, etc. However, predicting user influence is a challenging problem because the concept of influence is specific to a situation or a domain, and user communications are limited to text. In this work, we define user influence level as a function of community endorsement and develop a model that significantly outperforms the baseline by leveraging demographic and personality data. This approach consistently improves RankDCG scores across eight different domains.
@article{arxiv.2501.04046,
title = {Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling},
author = {Denys Katerenchuk and Rivka Levitan},
journal= {arXiv preprint arXiv:2501.04046},
year = {2026}
}