Misinformation Propagation¶
Study of how false claims, misleading narratives, and fabricated information spread through social networks and online communities. This includes computational models (agent-based, epidemic models, point-process models), empirical measurement of cascades, and investigation of the individual and structural factors that determine speed and reach of spread.
Key papers¶
- Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X — Identifies the "engagement fungibility mechanism" in X's recommendation algorithm as the root cause of faster misinformation propagation: the additive scoring layer treats fast engagement (likes/retweets) and slow engagement (replies/quotes) as interchangeable, advantaging low-credibility content
- How neighbourhood ideology shapes misinformation belief in densely tied social networks — agent-based contagion model showing network clustering amplifies ideological influence on misinformation adoption
- From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake — LLM-based agent simulation with personality-driven opinion evolution toward fake news
Related topics¶
- Propagation Models — broader category of information propagation models
- Social Influence — mechanisms by which exposure affects belief
- Network Dynamics — structural properties that shape information flow
- Ideological bias — how ideology predisposes belief in misinformation
- Motivated reasoning — psychological foundation for selective belief
See also¶
- Cascade Analysis — characterizing information cascades
- Diffusion Networks — network structures and their effect on spread