Abstract

Whether they are modeling bookmarking behavior in Flickr or cascades of failure in large networks, models of diffusion often start with the assumption that a few nodes start long chain reactions, resulting in large-scale cascades. While reasonable under some conditions, this assumption may not hold for social media networks, where user engagement is high and information may enter a system from multiple disconnected sources. Using a dataset of 262,985 Facebook Pages and their associated fans, this paper provides an empirical investigation of diffusion through a large social media network. Although Facebook diffusion chains are often extremely long (chains of up to 82 levels have been observed), they are not usually the result of a single chain-reaction event. Rather, these diffusion chains are typically started by a substantial number of users. Large clusters emerge when hundreds or even thousands of short diffusion chains merge together. This paper presents an analysis of these diffusion chains using zero-inflated negative binomial regressions. We show that after controlling for distribution effects, there is no meaningful evidence that a start node’s maximum diffusion chain length can be predicted with the user's demographics or Facebook usage characteristics (including the user's number of Facebook friends). This may provide insight into future research on public opinion formation.

Keywords

Merge (version control)Social mediaComputer scienceDiffusionDemographicsSocial network (sociolinguistics)AdvertisingWorld Wide WebBusinessInformation retrievalSociology

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Publication Info

Year
2009
Type
article
Volume
3
Issue
1
Pages
146-153
Citations
220
Access
Closed

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Social Impact

Social media, news, blog, policy document mentions

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220
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Cite This

Eric Sun, Itamar Rosenn, Cameron Marlow et al. (2009). Gesundheit! Modeling Contagion through Facebook News Feed. Proceedings of the International AAAI Conference on Web and Social Media , 3 (1) , 146-153. https://doi.org/10.1609/icwsm.v3i1.13947

Identifiers

DOI
10.1609/icwsm.v3i1.13947