Rumor spread in social network

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Rumors spread through social networks in two main ways: a big-picture view that uses epidemic-like theories, and a bottom-up view that looks at how individual people influence each other.

Macroscopic (epidemic-like) models
- Concept: See rumor spread as a population-wide process. Classic models like Daley-Kendall and Maki-Thompson divide people into three groups: ignorants (haven’t heard), spreaders (sharing the rumor), and stiflers (know the rumor but not spreading it).
- How it spreads: Spreaders meet others. If they meet an ignorant person, the ignorant becomes a spreader. If they meet someone who already knows the rumor, the interaction can cause the spreader to stop spreading and become a stifler.
- Variants: In some versions, when a spreader meets another spreader, only the initiator stays a spreader and the other becomes a stifler.
- On networks: In a network, people change state over time based on who they meet and how connected they are. Depending on how clustered or how “small-world” the network is, the rumor can either fizzle out or reach a large portion of the population.
- What you can measure: The final share of people who have spread the rumor can be described by simple formulas in well-mixed cases, and more complex behaviors can emerge on real networks.

Microscopic (individual-to-individual) models
- Independent Cascade (IC) model: When a person becomes influenced, they have one chance to influence each of their neighbors. If they fail to influence someone, that neighbor cannot be influenced by that person again, though others may try later.
- Linear Threshold (LT) model: Each person has a personal threshold. Each neighbor’s influence adds up, and once the total influence from active neighbors meets or exceeds a person’s threshold, they become active as well.
- These models focus on “who influenced whom” and are useful for studying things like viral marketing and how information spreads through a network.

Rumors in online networks and mitigation
- Online misinformation: Social networks can spread rumors quickly, which is a major concern. Researchers study how to understand and slow this spread.
- HISB model: A comprehensive approach that combines individual thinking with social influence. It uses layered networks to mirror different online platforms and accounts for how attracted a person is to a rumor, how that attraction changes over time, and how conversations across layers affect spreading.
- How it works in simple terms: People can be influenced to share or to deny a rumor based on personal judgment and what their neighbors are saying. The model tracks the overall popularity of the rumor and can predict long-term beliefs in the population.
- Why it’s useful: The HISB framework helps simulate real-time rumor diffusion and test strategies to reduce the spread of misinformation across multiple online spaces.

In short, researchers use both large-scale epidemic-style models and detailed, person-to-person models to understand how rumors move through social networks. The goal is not only to explain spread but also to identify effective ways to curb harmful misinformation.