How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?


  • Munmun De Choudhury Arizona State University
  • Yu-Ru Lin Arizona State University
  • Hari Sundaram Arizona State University
  • Kasim Selcuk Candan Arizona State University
  • Lexing Xie IBM TJ Watson Research Center
  • Aisling Kelliher Arizona State University


sampling, subgraph sampling, user context, homophily, social networks, social media, social network analysis, Twitter, forest fire sampling


Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion.

We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and user context (e.g. location, activity) can improve on naive methods by a significant margin of ~15-20%.




How to Cite

De Choudhury, M., Lin, Y.-R., Sundaram, H., Candan, K. S., Xie, L., & Kelliher, A. (2010). How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 34-41. Retrieved from