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Virality prediction and community structure in social networks
Lilian Weng1, Filippo Menczer, Yong-Yeol Ahn
1Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.
Scientific Reports
|August 29, 2013
Summary
Network structure impacts information spread differently based on contagion type. While most memes spread within communities, viral memes diffuse broadly, similar to diseases.
Area of Science:
- Computational Social Science
- Network Science
- Social Media Analytics
Background:
- Network structure influences the spread of information (contagions).
- Complex contagions, unlike simple contagions (e.g., diseases), are affected by social reinforcement and homophily, hindering cross-community spread.
- Memes and behaviors are often hypothesized as complex contagions.
Purpose of the Study:
- To investigate how network structure affects the diffusion of memes.
- To differentiate meme spread patterns from simple contagions.
- To develop a predictive method for meme virality based on early spreading patterns.
Main Methods:
- Analyzing meme spreading patterns across different community structures.
- Quantifying early spreading dynamics to assess community concentration.
- Comparing meme diffusion to disease (simple contagion) spread models.
Main Results:
- Most memes spread like complex contagions, primarily within clustered communities.
- A subset of viral memes exhibit broad diffusion across multiple communities, akin to simple contagions.
- Early community concentration of a meme predicts its future popularity and virality.
Conclusions:
- Meme diffusion is not uniform; some behave as complex contagions, while others spread widely like simple contagions.
- A meme's ability to permeate diverse communities is a key indicator of its virality.
- Predicting information spread based on network structure and early diffusion patterns is feasible for applications in social media analytics and marketing.
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