Impact of Sampling on Diffusion on Twitter
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Impact of Sampling on Diffusion on Twitter
- 1. How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? Munmun De Choudhury1, Yu-Ru Lin1, Hari Sundaram1, K. Selcuk Candan1, Lexing Xie2, Aisling Kelliher1 1Arizona State University, Tempe, AZ 2IBM T. J. Watson Research Center, Hawthorne, NY
- 2. This talk is about sampling the social web
- 3. Background and Motivation Problem Definition Sampling Diffusion Data Evaluation of Diffusion Samples Experimental Study Conclusions
- 4. 5/23/10 4 Modern Social Interactional Modes Slashdot Facebook Engadget LiveJournal Digg Twitter MetaFilter Flickr Reddit Orkut Blogger YouTube MySpace
- 5. We are attracted to social media, in part due to large scale datasets
- 6. Viral Marketing, Advertizing Campaigns
- 7. Collaboration, “Wisdom of the Crowds”
- 8. Crisis management w.r.t. real-time events
- 9. Is there something more fundamental happening here than just scale?
- 12. 140 characters can cause revolutions
- 15. Inference is based on data quality
- 16. What has been done?
- 17. Snowball
- 18. Random walk
- 19. Forest Fire [Leskovec et. al, KDD 2005]
- 20. Designed to capture topology
- 21. But not context or content!
- 22. Background and Motivation Problem Definition Sampling Diffusion Data Evaluation of Diffusion Samples Experimental Study Conclusions
- 24. What is the role of context in the sampling process?
- 25. What fraction of the social data should we sample?
- 27. Diffusion Series 27 Social graph Diffusion series 5/23/10
- 28. Background and Motivation Problem Definition Sampling Diffusion Data Evaluation of Diffusion Samples Experimental Study Conclusions
- 29. What are our sampling strategies?
- 30. Assume we are give N, the number of nodes to pick
- 31. And θ, the topic
- 32. Plus the social graph G
- 33. What if we ignored topology?
- 35. We can also sample topology using Forest Fire
- 37. Background and Motivation Problem Definition Sampling Diffusion Data Evaluation of Diffusion Samples Experimental Study Conclusions
- 38. Diffusion Saturation Metrics User-based(Volume, participation, dissemination) Topology-based(Reach, spread, cascade instances, collection size) Time-based(Rate) 38 Volume Spread Rate Participation Dissemination Reach Collection Size Cascade Instances 5/23/10
- 39. Distortion
- 41. Background and Motivation Problem Definition Sampling Diffusion Data Evaluation of Diffusion Samples Experimental Study Conclusions
- 42. Reference Set
- 43. 500 seed users using mashable.com
- 45. R1
- 48. Is this a slam dunk for forest fire + activity?
- 49. Looking at themes tells a more nuanced story
- 54. What is a “good” sampling ratio?
- 55. What happens when ρ = 0.3?
- 57. Background and Motivation Problem Definition Sampling Diffusion Data Evaluation of Diffusion Samples Experimental Study Conclusions
- 58. Social networks are causing significant changes in our lives
- 59. Inferences about social phenomena is affected by data quality
- 60. Topic + topology + seed attribute makes a difference to sampling
- 61. Thanks! Questions? munmun@asu.edu For Publications / Datasets: http://www.public.asu.edu/~mdechoud/ Twitter: @munmun10
Notas do Editor
- I believe so – I think social networks will change our understanding of media semantics in a fundamental way and not just because we have more data;
- Densification, shrinking diameter; generative model
- Volume, participation and dissemination
- rate
- Reach, spread, cascade instances and collection size