Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling

TL;DR

This paper presents a sociolinguistic model to predict user influence levels online, leveraging demographic and personality data to outperform baselines across multiple domains.

Contribution

The work introduces a novel influence prediction model based on sociolinguistic features, addressing the challenge of influence being domain-specific and text-limited.

Findings

Model significantly outperforms baseline in influence prediction.

Consistent improvement in RankDCG scores across eight domains.

Incorporates demographic and personality data for better accuracy.

Abstract

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.