Predicting Bot Vulnerability from Posting Trajectories: Censored Functional Regression under Informative Sampling

2026-07-31

AI Summary

This study addresses the prediction of users’ vulnerability to automated bot attacks at future time points based on their social media behavioral trajectories within a fixed observation window. To this end, the authors propose the Scalar-on-Function Regression with Censored Informative Design (SoCIFR) framework, which uniquely integrates censored data modeling, correction for informative sampling, and matched case–control design, thereby accommodating multiple functional predictors. This approach fills a critical methodological gap in functional data analysis for complex longitudinal digital behavior data. Extensive simulations demonstrate that SoCIFR performs robustly and effectively across diverse data-generating mechanisms, while empirical evaluation on real-world social media data shows significant improvements in the temporal prediction accuracy of bot vulnerability.

Abstract

In this manuscript, we propose a novel framework for Scalar-on Censored Informative-design Functional Regression or SoCIFR. This setting is increasingly common in modern longitudinal and digital data applications but remains underdeveloped in functional data literature. We first discuss estimation and prediction in SoCIFR and extend the methodology to accommodate a matched case-control design. The proposed methodology is further generalized to handle multiple functional predictors, allowing for both censored and uncensored trajectories, observed under informative or non-informative sampling designs. Through simulation studies, we assess the performance of the proposed methods under various data-generating scenarios. We apply the methods to the motivating application for predicting user susceptibility to automated ("bot") interactions at increasing future time horizons based on social media behavioral trajectories observed over fixed time windows.

Problem

• bot vulnerability
• functional regression
• censored trajectories
• informative sampling
• social media behavior

Innovation

• functional regression
• censored data
• informative sampling
• longitudinal trajectories
• matched case-control design