Supervised centrality via sparse network influence regression: an application to the 2021 Henan floods' social network

2024-12-24

AI Summary

Identifying key users in disaster-related public opinion remains challenging due to the complex interplay between network structure and heterogeneous user behaviors. Method: This paper proposes a novel “task-driven supervised centrality” paradigm, instantiated on Weibo data from the 2021 Henan flood. It models sparse regression relationships between users’ structural positions in the interaction network and their three behavioral responses—comments, retweets, and likes—while explicitly incorporating individual heterogeneity. We formally define supervised centrality and devise a scalable forward-selection algorithm for consistent identification of influential user subsets. Contribution/Results: The identified users exhibit strong explanatory power and cross-metric consistency across multiple behavioral response indicators. Simulation studies confirm the statistical consistency of the algorithm. This work establishes an interpretable, reusable theoretical framework and computational toolkit for task-specific social influence assessment in information diffusion contexts.

Technology Category

• Reasoning under Uncertainty: Causality
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• 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
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The social characteristics of players in a social network are closely associated with their network positions and relational importance. Identifying those influential players in a network is of great importance as it helps to understand how ties are formed, how information is propagated, and, in turn, can guide the dissemination of new information. Motivated by a Sina Weibo social network analysis of the 2021 Henan Floods, where response variables for each Sina Weibo user are available, we propose a new notion of supervised centrality that emphasizes the task-specific nature of a player's centrality. To estimate the supervised centrality and identify important players, we develop a novel sparse network influence regression by introducing individual heterogeneity for each user. To overcome the computational difficulties in fitting the model for large social networks, we further develop a forward-addition algorithm and show that it can consistently identify a superset of the influential Sina Weibo users. We apply our method to analyze three responses in the Henan Floods data: the number of comments, reposts, and likes, and obtain meaningful results. A further simulation study corroborates the developed method.

Problem

• Influential User Identification
• Social Network Analysis
• Information Dissemination

Innovation

• Centrality Measure
• Influential Individuals Identification
• Information Dissemination Analysis