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

2025-12-18

AI Summary

In social media marketing, distinguishing “stable influencers”—those sustaining long-term influence—from “transient influencers” with only short-term impact remains a critical challenge. Method: Leveraging six months of Twitter retweet data, this work introduces the first temporal-stability-aware influencer classification framework, subdividing stable influencers into *origin spreaders* (who widely disseminate their own content) and *intermediaries* (who efficiently propagate others’ content). The approach integrates temporal social network analysis, multidimensional behavioral feature engineering—including propagation breadth, betweenness centrality, and historical stability—and supervised learning models (XGBoost/Random Forest), evaluated via AUC. Results: The framework achieves AUCs of 0.89 for origin spreaders and 0.81 for intermediaries. Historical influence provides significant incremental predictive value for origin spreaders, whereas current influence remains consistently most decisive. This study establishes a novel paradigm for precise, long-horizon influencer identification.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Temporal Planning
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Distinguish stable from transient influencers using long-term Twitter data
• Analyze characteristics of source spreaders and brokers as influencer types
• Develop classification models to predict stable influencers among temporary ones

Innovation

• Analyzed long-term Twitter retweet data for influencer stability
• Developed classification models predicting stable influencers with high AUC
• Used past and current influence as key predictive factors