Identifying Stable Influencers: Distinguishing Stable and Temporal Influencers Using Long-Term Twitter Data

TL;DR

This paper proposes methods to identify stable Twitter influencers over time by analyzing long-term data, distinguishing between source spreaders and brokers, and predicting stability with high accuracy.

Contribution

It introduces a classification approach to predict stable influencers using long-term Twitter data, emphasizing the importance of current and past influence.

Findings

Stable influencers are more likely to maintain influence over time.

Classification models achieved AUCs of 0.89 for source spreaders and 0.81 for brokers.

Current influence is a key predictor, with past influence also contributing, especially for source spreaders.

Abstract

For effective social media marketing, identifying stable influencers-those who sustain their influence over an extended period-is more valuable than focusing on users who are influential only temporarily. This study addresses the challenge of distinguishing stable influencers from transient ones among users who are influential at a given point in time. We particularly focus on two distinct types of influencers: source spreaders, who widely disseminate their own content, and brokers, who play a key role in propagating information originating from others. Using six months of retweet data from approximately 19,000 Twitter users, we analyze the characteristics of stable influencers. Our findings reveal that users who have maintained influence in the past are more likely to continue doing so in the future. Furthermore, we develop classification models to predict stable influencers among temporarily influential users, achieving an AUC of approximately 0.89 for source spreaders and 0.81 for brokers. Our experimental results highlight that current influence is a critical factor in classifying influencers, while past influence also significantly contributes, particularly for source spreaders.