The role of hidden influentials in the diffusion of online information cascades
Open accessAbstract
In a diversified context with multiple social networking sites, heterogeneous activity patterns and different user-user relations, the concept of ‘information cascade’ is all but univocal. Despite the fact that such information cascades can be defined in different ways, it is important to check whether some of the observed patterns are common to diverse contagion processes that take place on modern social media. Here, we explore one type of information cascades, namely, those that are time-constrained, related to two kinds of socially-rooted topics on Twitter. Specifically, we show that in both cases cascades sizes distribute following a fat-tailed distribution and that whether or not a cascade reaches system-wide proportions is mainly given by the presence of so-called hidden influentials. These latter nodes are not the hubs, which on the contrary, often act as firewalls for information spreading. Our results contribute to a better understanding of the dynamics of complex contagion and, from a practical side, for the identification of efficient spreaders in viral phenomena.
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Hidden influentials are mostly humans, but augmented humans also fall in this category (e.g. User01).
“Hidden influentials, known to be efficient spreaders in viral phenomena [44], are mostly humans but augmented humans also falls in this category (e.g. User01).”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [44]
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Catalysing cooperation: the power of collective beliefs in structured populations
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Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory
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Tracking social provenance in chains of retweets
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Model to Generate Benchmark Graphs Based on Evolution Dynamics
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Understanding Micro-Influencer Networks
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- Authors
- Baños, R. A., Borge-Holthoefer, J., & Moreno, Y.
- Affiliations
- Universidad de Zaragoza (ES)
- Corresponding author
- Moreno, Yamir
- ORCID
- Moreno, Yamir
- Venue
- EPJ Data Science
- Published
- 2013-07-26
- Volume
- 2
- Issue
- 1
- ISSN
- 2193-1127 (ISSN-L)
- OpenAlex
- W2142125782
- Cited by
- 79
- References
- 52
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 3 · 2019: 9 · 2018: 10 · 2017: 8 · 2016: 9 · 2015: 9 · 2014: 10 · 2013: 5
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- EPJ Data Science (publishedVersion, cc-by)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- information cascades, complex contagion, Twitter, social media, heavy-tailed distributions, network hubs, firewall, viral spread, social networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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