A Computational Model and Convergence Theorem for Rumor Dissemination in Social Networks

The spread of rumors, which are known as unverified statements of uncertain origin, may cause tremendous number of social problems. If it would be possible to identify factors affecting spreading a rumor (such as agents’ desires, trust network, etc.), then this could be used to slowdown or stop its spreading. A computational model that includes rumor features and the way a rumor is spread among society’s members, based on their desires, is therefore needed. Our research is centering on the relation between the homogeneity of the society and rumor convergence in it and result shows that the homogeneity of the society is a necessary condition for convergence of the spreading rumor.

💡 Research Summary

The paper presents a novel computational framework for analyzing how rumors—defined as unverified statements of uncertain origin—propagate through a social network and under what conditions they converge to a stable form. Unlike classic epidemic‑type models that treat information as a scalar “infectious” quantity, the authors model a rumor as a multidimensional attribute vector whose components correspond to individual propositions (e.g., “vaccines are safe”, “the government is hiding data”). Each agent in the network possesses a desire set—a binary collection of propositions the agent wishes to see affirmed—and a trust network that assigns a weight to every incoming neighbor, reflecting how much the agent relies on that neighbor’s information.

Model Specification

• Agents and Desires: For agent i, the desire set Dᵢ ⊆ Φ (Φ is the universe of possible propositions) captures the agent’s preferred truth‑values. Desires are binary (present/absent) for analytical tractability.
• Rumor Representation: A rumor r ∈