Universality, criticality and complexity of information propagation in social media
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Abstract (arXiv)
Information avalanches in social media are typically studied in a similar fashion as avalanches of neuronal activity in the brain. Whereas a large body of literature reveals substantial agreement about the existence of a unique process characterizing neuronal activity across organisms, the dynamics of information in online social media is far less understood. Statistical laws of information avalanches are found in previous studies to be not robust across systems, and radically different processes are used to represent plausible driving mechanisms for information propagation. Here, we analyze almost 1 billion time-stamped events collected from a multitude of online platforms -- including Telegram, Twitter and Weibo -- over observation windows longer than 10 years to show that the propagation of information in social media is a universal and critical process. Universality arises from the observation of identical macroscopic patterns across platforms, irrespective of the details of the specific system at hand. Critical behavior is deduced from the power-law distributions, and corresponding hyperscaling relations, characterizing size and duration of avalanches of information. Neuronal activity may be modeled as a simple contagion process, where only a single exposure to activity may be sufficient for its diffusion. On the contrary, statistical testing on our data indicates that a mixture of simple and complex contagion, where involvement of an individual requires exposure from multiple acquaintances, characterizes the propagation of information in social media. We show that the complexity of the process is correlated with the semantic content of the information that is propagated. Conversational topics about music, movies and TV shows tend to propagate as simple contagion processes, whereas controversial discussions on political/societal themes obey the rules of complex contagion.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2021.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 3.
6 papers.
- 2023Data driven modeling for self-similar dynamicscited by 0
- 2024On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Predictioncited by 0
- 2025Uncovering Social Network Activity Using Joint User and Topic Interactioncited by 0
- 2026Large language models for spreading dynamics in complex systemscited by 0
- 2026PLDR-LLMs Reason At Self-Organized Criticalitycited by 0
- 2026Network Information Enhances Unreliable News Domain Detectioncited by 0
- 2026Network Information Enhances Unreliable News Domain Detection3 Aug 2026
- 2026PLDR-LLMs Reason At Self-Organized Criticality12 Mar 2026
- 2026Large language models for spreading dynamics in complex systems8 Feb 2026
- 2025Uncovering Social Network Activity Using Joint User and Topic Interaction15 Jun 2025
- 2024On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction25 Sep 2024
- 2023Data driven modeling for self-similar dynamics12 Oct 2023