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

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

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Nora Alrajebah

September 17, 2018
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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

  7. 5QQPCHVGT

  8. 3. Express admiration favourite, like a.k.a. heart

  9. 4. Spread content re += {tweet | blog | path}

    && share
  10. None
  11. %CUECFGU

  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

  34. None
  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