An Efficient Feedback Control Mechanism for Positive/Negative Information Spread in Online Social Networks

Area of Science:

• Computational Social Science
• Network Science
• Information Science

Background:

• Online social networks (OSNs) enable rapid information dissemination, but also facilitate the spread of misinformation.
• Unsubstantiated rumors and negative information on OSNs can lead to public misperceptions and economic damage.
• Controlling negative information spread alongside positive content is a significant, underexplored challenge.

Purpose of the Study:

• To propose an efficient feedback control mechanism for managing simultaneous positive and negative information spread in OSNs.
• To develop a novel computational model for analyzing the temporal dynamics of information diffusion.
• To devise and implement synergetic intervention strategies for effective misinformation control.

Main Methods:

• A novel computational model was developed to represent the temporal dynamics of positive and negative information spread.
• An efficient feedback control mechanism was designed using three synergetic intervention strategies.
• The mechanism dynamically adjusts control plans based on feedback from seed user interventions in real-world networks.

Main Results:

• The proposed mechanism effectively restrains negative information spread.
• The control strategy minimizes system expenses.
• Experimental results on two real-world networks validate the mechanism's efficiency.

Conclusions:

• The developed feedback control mechanism offers an efficient solution for managing misinformation on OSNs.
• The study highlights the importance of dynamic, adaptive strategies for information control.
• This research addresses a critical gap in understanding and mitigating negative information diffusion in online environments.