What Happened on Tumblr? A Look into Tumblr as a Platform for Content Propagation

Transcript

1. What Happened on Tumblr? A Look into Tumblr as a Platform for Content Propagation Nora Alrajebah
2. Things I’ll cover today: 1. Building Blocks 2. Settings 3. Findings 4. Remarks
3. # 5JQTV *KUVQT[ QH 150U
4. The core affordances of OSNs
5. 1. Connect to others Follow Friend
6. 2. Create content
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8. 3. Express admiration favourite, like a.k.a. heart
9. 4. Spread content re += {tweet | blog | path} && share
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12. A cascade is considered as an artefact to study information diffusion on OSN It is the manifestation of the information diffusion process
13. Why analysing cascades is important? 1. A proxy to unravel the way information is spread on the Web 2. Explain the popularity-gaining phenomenon on the Web 3. Estimate influence and homophily between users 4. Estimate the value of the content 5.They are better indicators of users’ interest and trust networks 6.Explain social network evolution
14. Things I’ll cover today: 1. Building Blocks 2. Settings 3. Findings 4. Remarks
15. Tumblr • Diffusion on Tumblr is powered by the Reblogging functionality • The ability to reblog allow content to spread • The traces of the spread create cascades that can be observed • The reblogging events appears as a list of notes attached to posts and their reblogged copies
16. Content Unified, explicit and ordered
17. The two facets of a cascade Structural Temporal Who influenced whom to spread the content? How many shares are there at any point in time (Day/Hour) 0! 6! 12! 18! 24! 30! 0! 2! 4! 6! 8! 10! Number of shares! Days after publishing!
18. The Web Social Interaction Data Sharing Data
19. The Web Social Interaction Data Sharing Data Other Interactions
20. The Web https://www.flickr.com/photos/frauhoelle/8464661409 https://commons.wikimedia.org/wiki/File:Tumblr_icon.png Social Interaction Data Sharing Data Other Interactions 5VTWEVWTCN 6GORQTCN 2NCVHQTO Explicit Cascades
21. Where do research tasks fit? Netw ork Science Data Science Web Science Cascades Construction Structural Analysis Data collection & preprocessing Temporal & Platform Analysis
22. Tumblr’s Year in Review “Tumblr’s Year in Review is a showcase of the best stuff on the Internet from 2014. Follow along for a daily dose of creativity, humor, humanity, fandom, and sharing. And GIFs. Lots of GIFs!” http://2014inreblogs.tumblr.com/2014
23. Stats Number of posts 1292 No. or reblogs (rebloggers) 73,048,903 No. of reblogees 3,541,110 No. of likes 48,822,318 No. of comments ??
24. Cascades networks construction • Ideally, cascade networks will have a neat tree topology .. • However .. • That is not the case, at least not all the time • This is due to cases where: • Reblogs are deleted • Users deactivate their accounts • Users reblog more than once Isolated components Repeated appearances of users
25. Things I’ll cover today: 1. Building Blocks 2. Settings 3. Findings 4. Remarks
26. Platform Analysis
27. High reblogging rate = Cascades are large! 0 1 2 3 4 5 6 x ⇥105 0.0 0.2 0.4 0.6 0.8 1.0 P(Number of reblogs >= x) 78% 18% Yet another long tail on the Web!
28. But why? Short answer: reblogging is a cult Long answer: Tumblr employs numerous content exposure mechanisms
29. How ‘big’ are large cascades?
30. The Silent majority 1.55% of reblogs are with comments, 0.32% with @ There are 315 replies only 0.16 comment for 10 reblogs
31. Reblogs > Likes A shamelessly committed community Their actions are stronger than their words 7.9 like for 10 reblog
32. 0 2 4 6 Cumulative sum ⇥107 Reblogs Likes Comments
33. Structural Analysis
35. 6YQRWTRQUGUŎ 1- Influence estimators 2- Structural estimators
36. Branching Factors i.e., the out-degree for each user
37. Q: How many users a user influences? 0 20 40 60 80 100 % BF = 0 BF = 1 BF > 1 ~68% ~12% ~20%
38. Subcascade sizes The size of the subcascade per user
39. Q: What is the overall impact of users? 0 20 40 60 80 100 % Subcascade = 1 Subcascade > 1 13% 87%
40. Q: Small branching factor but high impact? - Compute: - Ratio = Branching factor / sub cascade size - if ratio > 1: - The user generates a subcascade that goes beyond its immediate effect, i.e. their branching factor - if ratio = 1: - The user generates subcascades that equal one
41. Q: What is the impact of the content’s author? 0 20 40 60 80 100 x % 0.0 0.1 0.2 0.3 0.4 0.5 0.6 P(Reblogs from author >= x%) Shallow Deep
42. Depth
43. Q: How far are the reblogging users from the post’s author? 100 101 102 Depth 100 101 102 103 104 105 106 No of Nodes Mean
44. Temporal Analysis
45. Posts Age • The eldest post was active for 617 days • The youngest was active for 28 days • Surprisingly, the old post has a very small cascade size of 131 reblogs, but still managed to survive for 617 days! Accumulating popularity slowly but steady!
46. How long it takes a post to be reblogged? 1 24 Hours after publishing 87% 97%
47. Growth patterns
48. how posts accumulate popularity? 0 50 100 150 200 250 300 350 Days after publishing 0 20000 40000 60000 80000 100000 Reblogs per day school: 416282 chill: 480379 business: 371600 17th
49. Q: Is there a cascade growth pattern on Tumblr?
50. 0.0 0.2 0.4 0.6 0.8 1.0 Days after publishing 0.0 0.2 0.4 0.6 0.8 1.0 Cascade size (cumulative) 0.0 0.2 0.4 0.6 0.8 1.0 Days after publishing 0.0 0.2 0.4 0.6 0.8 1.0 Cascade size (cumulative) 0.0 0.2 0.4 0.6 0.8 1.0 Days after publishing 0.0 0.2 0.4 0.6 0.8 1.0 Cascade size (cumulative) 0.0 0.2 0.4 0.6 0.8 1.0 Days after publishing 0.0 0.2 0.4 0.6 0.8 1.0 Cascade size (cumulative) Q1 Q2 Q3 Q4
51. Things I’ll cover today: 1. Building Blocks 2. Settings 3. Findings 4. Remarks
52. Wrapping up .. • Tumblr ‘year in review’ blog features some really ‘large’ cascades! • Cascades matter! • Users’ influence might be underestimated if only the branching factor was taken into account • Cascades on Tumblr have non-trivial sizes and depths • Cascades grow in size in so many ways .. • Large cascades exist!
53. 6JCPMU