Do Diffusion Protocols Govern Cascade Growth?

Large cascades can develop in online social networks as people share information with one another. Though simple reshare cascades have been studied extensively, the full range of cascading behaviors on social media is much more diverse. Here we study how diffusion protocols, or the social exchanges that enable information transmission, affect cascade growth, analogous to the way communication protocols define how information is transmitted from one point to another. Studying 98 of the largest information cascades on Facebook, we find a wide range of diffusion protocols – from cascading reshares of images, which use a simple protocol of tapping a single button for propagation, to the ALS Ice Bucket Challenge, whose diffusion protocol involved individuals creating and posting a video, and then nominating specific others to do the same. We find recurring classes of diffusion protocols, and identify two key counterbalancing factors in the construction of these protocols, with implications for a cascade’s growth: the effort required to participate in the cascade, and the social cost of staying on the sidelines. Protocols requiring greater individual effort slow down a cascade’s propagation, while those imposing a greater social cost of not participating increase the cascade’s adoption likelihood. The predictability of transmission also varies with protocol. But regardless of mechanism, the cascades in our analysis all have a similar reproduction number (≈1.8), meaning that lower rates of exposure can be offset with higher per-exposure rates of adoption. Last, we show how a cascade’s structure can not only differentiate these protocols, but also be modeled through branching processes. Together, these findings provide a framework for understanding how a wide variety of information cascades can achieve substantial adoption across a network.

Presented at ICWSM 2018.

Transcript

1. Justin Cheng, Jon Kleinberg, Jure Leskovec, David Liben-Nowell, Bogdan State, Karthik Subbian, and Lada Adamic
2. i.e., how do memes go viral? how (not why)
5. Prior work Cha, et al. (2009, 2010); Romero, et al. (2011); Goel, et al. (2012); Weng, et al. (2013); Cheng, at al. (2014, 2016) cascade growth prediction cascade recurrence contagion/diffusion models network structure network evolution social influence
6. A focus on reshare cascades Prior work Hubs crucial to growth Simple replication Goldenberg, et al. (2009); Cheng, at al. (2014) cascade growth prediction cascade recurrence contagion/diffusion models network structure network evolution social influence
7. Types of cascades (This figure for illustrative purposes only.) Reshare Cascades Other Cascades
8. What other types of cascades exist?
10. Reshare Post
11. Reshare Post Ice Bucket Challenge Post
13. 17M videos 200K participants 2M supporters
14. Do different social mechanisms influence cascade growth? Research Question diffusion protocols Explain variations in cascade growth
15. What is a diffusion protocol?
16. Inspiration: Communications Protocols Client Server 1. SYN 2. SYN-ACK 3. ACK
17. Inspiration: Communications Protocols Client Server 1. SYN 2. SYN-ACK 3. ACK
18. Inspiration: Communications Protocols Client Server 1. SYN 2. SYN-ACK 3. ACK
19. Inspiration: Communications Protocols Client Server 1. SYN 2. SYN-ACK 3. ACK
21. Transient Copy Protocol
22. Transient Copy Protocol
24. Transient Copy Protocol
25. Nomination Protocol Transient Copy Protocol
26. Nomination Protocol Transient Copy Protocol
27. Nomination Protocol Transient Copy Protocol
29. Transient Copy Diffusion Protocols Nomination
30. Transient Copy Persistent Copy Nomination Volunteer Diffusion Protocols
31. Transient Copy Persistent Copy Nomination Volunteer Also see: State and Adamic (2015) Diffusion Protocols
32. Transient Copy Persistent Copy Nomination Volunteer Diffusion Protocols
33. Transient Copy Persistent Copy Nomination Volunteer Diffusion Protocols
34. Transient Copy Persistent Copy Nomination Volunteer Each protocol can generate large cascades! ~1M adoptions per cascade ~800K adoptions per cascade ~6M adoptions per cascade ~1M adoptions per cascade
35. Diffusion protocols govern cascade growth Our Hypothesis Individual effort Social cost
36. Participation requires individual effort Marwell, Oliver, and Prahl (1988)
37. Participation can have social costs Milgram (1969); Granovetter (1978); Banerjee (1992)
38. Participation can have social costs Cialdini (1993); Goffman (1967)
39. How does individual effort and social cost explain differences?
40. Cascades on Facebook 98 large cascades 200M users 4 protocols (All data was de-identified and analyzed in aggregate.)
41. How do cascades of different diffusion protocols vary on… 1 Propagation Speed 2 Reliance on Hub Nodes 3 Tie Strength 4 Simple or Complex Diffusion
42. How do cascades of different diffusion protocols vary on… 1 Propagation Speed 2 Reliance on Hub Nodes 3 Tie Strength 4 Simple or Complex Diffusion
43. How quickly do these cascades propagate?
44. Persistent Copy Nomination % Users How quickly do cascades propagate? 26 21 211 216 0% 12% 24% 0% 5% 10% 15% Transient Copy Volunteer Adoption Delay (s) 221 26 21 211 216 221 0% 8% 16% 0% 8% 16%
45. Persistent Copy Nomination % Users Copy cascades spread quickly… 26 21 211 216 0% 12% 24% 0% 5% 10% 15% Transient Copy Volunteer Adoption Delay (s) 221 26 21 211 216 221 0% 8% 16% 0% 8% 16%
46. Persistent Copy Nomination % Users …while non-copy cascades spread slowly 26 21 211 216 0% 12% 24% 0% 5% 10% 15% Transient Copy Volunteer Adoption Delay (s) 221 26 21 211 216 221 0% 8% 16% 0% 8% 16% ~ 1 day
47. Transient Copy Nomination Increasing effort reduces propagation speed Less effort? More effort?
48. Transient Copy Volunteer Increasing effort reduces propagation speed 2 1 3 4 1
49. How do cascades of different diffusion protocols vary on… 1 Propagation Speed 2 Reliance on Hub Nodes 3 Tie Strength 4 Simple or Complex Diffusion
50. Are hubs always crucial in the development of large cascades? Goldenberg, et al. (2009); Hinz, et al. (2011); Cheng, et al. (2014)
51. Persistent Copy Nomination How significant are hub nodes? Transient Copy Volunteer % Adoptions Originating from the Top 1% of Users 10 0 20 30 40 10 0 20 30 40
52. Persistent Copy Nomination Low-effort cascades benefit more from hubs Transient Copy Volunteer % Adoptions Originating from the Top 1% of Users 10 0 20 30 40 10 0 20 30 40 13.7 7.6 5.8 37.3
53. Hubs Non-Hubs Katona, et al. 2011 Hubs are less influential per individual
54. How do cascades of different diffusion protocols vary on… 1 Propagation Speed 2 Reliance on Hub Nodes 3 Tie Strength 4 Simple or Complex Diffusion
55. Does tie strength vary across cascades?
56. Persistent Copy Nomination How does tie strength vary? Transient Copy Volunteer # Mutual Friends 20 0 40 60 80 20 0 40 60 80
57. Persistent Copy Nomination Transient copy cascades use weaker ties… Transient Copy Volunteer # Mutual Friends 20 0 40 60 80 20 0 40 60 80 28.8
58. Persistent Copy Nomination …while other cascades use stronger ties Transient Copy Volunteer # Mutual Friends 20 0 40 60 80 20 0 40 60 80 28.8 44.6 58.1 70.7
59. Transient Copy Nomination Social costs invoke strong ties Lower social cost Greater social cost
60. Transient Copy Persistent Copy Social costs invoke strong ties Demonstrates group belonging
61. How do cascades of different diffusion protocols vary on… 1 Propagation Speed 2 Reliance on Hub Nodes 3 Tie Strength 4 Simple or Complex Diffusion
62. Is the diffusion process simple or complex? Centola and Macy (2007)
63. Persistent Copy Nomination P(Adopt) Is diffusion simple or complex? 3 1 5 7 Transient Copy Volunteer # Prior Exposures 9 3 1 5 7 9 0 0.003 0.006 0.009 0 0.002 0.004 0.006 0 0.06 0.12 0.18 0 0.003 0.006 0.009
64. Persistent Copy Nomination P(Adopt) Social costs can increase adoption rates 3 1 5 7 Transient Copy Volunteer # Prior Exposures 9 3 1 5 7 9 0 0.002 0.004 0.006 0 0.003 0.006 0.009 0 0.003 0.006 0.009 0 0.06 0.12 0.18
65. Persistent Copy Nomination P(Adopt) Complexity depends on cascade type 3 1 5 7 Transient Copy Volunteer # Prior Exposures 9 3 1 5 7 9 0 0.002 0.004 0.006 Complex 0 0.003 0.006 0.009 Simple 0 0.003 0.006 0.009 0 0.06 0.12 0.18
66. Transient Copy Persistent Copy Social proof leads to complex diffusion
67. Individual effort and social cost influence cascade growth
68. Transient Copy Persistent Copy Nomination Volunteer
69. High Indiv. Effort Low Indiv. Effort Low Social Cost High Social Cost Persistent Copy Nomination Transient Copy Volunteer
70. High Indiv. Effort Low Indiv. Effort Low Social Cost High Social Cost Hub-Centric Strong Ties Complex Diffusion Fast Persistent Copy Not Hub-Centric Strong Ties Simple Diffusion Slow Nomination Hub-Centric Weak Ties Simple Diffusion Fast Transient Copy Not Hub-Centric Strong Ties Complex Diffusion Slow Volunteer
71. How do cascades of different diffusion protocols vary on… 1 Propagation Speed 2 Reliance on Hub Nodes 3 Tie Strength 4 Simple or Complex Diffusion
72. How do cascades of different diffusion protocols vary on… 1 Propagation Speed 2 Reliance on Hub Nodes 3 Tie Strength 4 Simple or Complex Diffusion 5 Adoption Rate
73. How do adoption rates vary across cascades?
74. How do adoption rates vary? (Considering only non-leaf nodes) % Users Adopted 0% 15% 30% 45% 60% # Users Exposed 0 75 150 225 300
75. How do adoption rates vary? (Considering only non-leaf nodes) % Users Adopted 0% 15% 30% 45% 60% # Users Exposed 0 75 150 225 300 Ice Bucket Challenge cascade
76. How do adoption rates vary? (Considering only non-leaf nodes) % Users Adopted 0% 15% 30% 45% 60% # Users Exposed 0 75 150 225 300 Transient copy cascade Ice Bucket Challenge cascade
77. Adoption rates drop as exposures increase (Considering only non-leaf nodes) % Users Adopted 0% 15% 30% 45% 60% # Users Exposed 0 75 150 225 300 Transient Copy Persistent Copy Nomination Volunteer
78. The reproduction number is constant! (Considering only non-leaf nodes) % Users Adopted 0% 15% 30% 45% 60% # Users Exposed 0 75 150 225 300 y = 1.81 x
79. Parallels with r/K selection theory Pianka (1970) K-selected Species r-selected Species Stayin’ alive!
80. Parallels with r/K selection theory “K-selected” Protocol “r-selected” Protocol
81. Parallels with r/K selection theory “K-selected” Protocol “r-selected” Protocol
82. Large cascades can result from diverse diffusion protocols