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A Protocol for Computer-Based Protein Structure and Function Prediction

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Proactive Bot Detection Based on Structural Information Principles.

Xianghua Zeng, Hao Peng, Angsheng Li

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 22, 2025

Summary

This summary is machine-generated.

We introduce SIAMD, a novel framework for social bot detection. It models bot behaviors using structural information and adversarial learning to proactively identify and combat sophisticated bots on social media.

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Area of Science:

Computer Science
Artificial Intelligence
Social Network Analysis

Background:

Bot detection is vital for social media authenticity.
Sophisticated bots evade current detection methods, creating an arms race.
Existing methods struggle with evolving bot behaviors.

Purpose of the Study:

To propose a novel framework, SIAMD, for proactive social bot detection.
To model bot behaviors effectively using structural information and adversarial learning.
To enhance the generalizability, robustness, and interpretability of bot detection.

Main Methods:

Organizing user-account and message interactions into a heterogeneous structure.
Quantifying historical activity uncertainty using structural entropy.
Leveraging large language models for synthetic content generation and network evolution.

Main Results:

SIAMD significantly outperforms state-of-the-art bot detection baselines.
Demonstrated effectiveness, generalizability, and robustness on real-world datasets.
Achieved enhanced proactive detection through adversarial network evolution.

Conclusions:

SIAMD offers a robust and effective approach to social bot detection.
The framework's adversarial nature enhances its ability to counter sophisticated bots.
Structural information principles and LLMs integration advance bot detection capabilities.