Robust fake-post detection against real-coloring adversaries

TL;DR

This paper develops advanced detection mechanisms for fake posts on social networks, accounting for adversarial user behaviors, and demonstrates their improved effectiveness over existing methods through theoretical analysis and simulations.

Contribution

It introduces a new branching process model for analyzing fake post propagation with adversaries and proposes improved detection mechanisms that outperform existing warning systems.

Findings

Existing warning mechanisms underperform against adversaries.

New mechanisms effectively eliminate adversarial influence.

Enhanced mechanism works with minimal user-specific knowledge.

Abstract

The viral propagation of fake posts on online social networks (OSNs) has become an alarming concern. The paper aims to design control mechanisms for fake post detection while negligibly affecting the propagation of real posts. Towards this, a warning mechanism based on crowd-signals was recently proposed, where all users actively declare the post as real or fake. In this paper, we consider a more realistic framework where users exhibit different adversarial or non-cooperative behaviour: (i) they can independently decide whether to provide their response, (ii) they can choose not to consider the warning signal while providing the response, and (iii) they can be real-coloring adversaries who deliberately declare any post as real. To analyze the post-propagation process in this complex system, we propose and study a new branching process, namely total-current population-dependent branching process with multiple death types. At first, we compare and show that the existing warning mechanism significantly under-performs in the presence of adversaries. Then, we design new mechanisms which remarkably perform better than the existing mechanism by cleverly eliminating the influence of the responses of the adversaries. Finally, we propose another enhanced mechanism which assumes minimal knowledge about the user-specific parameters. The theoretical results are validated using Monte-Carlo simulations.