Uncertainty-based False Information Propagation in Social Networks
Abstract
Many network scientists have investigated the problem of mitigating or removing false information propagated in social networks. False information falls into two broad categories: disinformation and misinformation. Disinformation represents false information that is knowingly shared and distributed with malicious intent. Misinformation in contrast is false information shared unwittingly, without any malicious intent. Many existing methods to mitigate or remove false information in networks concentrate on methods to find a set of seeding nodes (or agents) based on their network characteristics (e.g., centrality features) to treat. The aim of these methods is to disseminate correct information in the most efficient way. However, little work has focused on the role of uncertainty as a factor in the formulation of agents’ opinions. Uncertainty-aware agents can form different opinions and eventual beliefs about true or false information resulting in different patterns of information diffusion in networks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 13, 2019
- Source ID
- 10.1145/3311091
Entities
People
- Benjamin D. Horne
- Jin-Hee Cho
- John O’donovan
- Scott Rager
- Sibel Adali
Organizations
- RTX
- Rensselaer Polytechnic Institute
- United States Army Research Laboratory
- University of California, Santa Barbara
- Virginia Tech