Content and Causality in Influence Networks

author: Sinan Aral, Stern School of Business, New York University
published: Aug. 18, 2011,   recorded: July 2011,   views: 813
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Slides

Slides
0:00 Content and Causality in Influence Networks
0:55 Invitation
1:55 Ashton Kutcher
3:34 Social
4:00 This revolution will be tweeted
4:19 Crisis response
4:34 A lot of commercial interest
4:47 Causality / Content (1)
5:06 Causality
5:20 Influence - Current Formalizations
6:31 A Stricter Definition of "Influence"
6:52 Implications of this Definition
8:43 Causal Statistical Estimation
9:18 Reflection and Endogeneity
9:49 "Obesity is Contagious"
10:33 Homophily
11:25 "Everything is Contagious"
13:58 Causal Estimation in Networks
16:22 Dynamic Matched Sampling
17:50 Distinguishing Influence from Homophily
19:49 Exaggerated Homophily Amongst Early Adopters
20:26 People standing in line for iPad2
20:55 Viral Product Design (1)
22:04 Viral Product Design (2)
23:24 Viral Feature Space
24:44 The Setup
25:37 Data
25:50 Flixster - An Example Facebook Application (1)
26:03 Flixster - An Example Facebook Application (2)
26:04 Flixster - An Example Facebook Application (3)
26:16 Flixster - An Example Facebook Application (4)
26:26 Flixster - An Example Facebook Application (5)
26:54 Flixster - An Example Facebook Application (6)
27:05 Three groups
27:23 Preventing Contamination and Leakage
28:46 Conventional Approach in Observational Data
30:48 Which Features Spread Contagion Best?
31:35 Sustained Engagement
33:06 Which Features Spread Contagion Best?
33:21 Virtuous cycle
33:34 Causality / Content (2)
33:36 Content
33:49 Content Gives ...
34:09 Some Examples of Research
34:27 Strengths of Weak Ties & Structural Holes
35:05 Information Advantage
36:00 A 40 year old assumption ...
36:30 "The Diversity-Bandwidth Tradeoff"
38:34 Information Environment Mediates Tradeoff
39:54 Study Context - Executive Search
40:03 Measuring Information Diversity
41:06 Results
42:13 Implications
44:18 "The Cramer Effect" (1)
45:36 "The Cramer Effect" (2)
49:54 Causality / Content (3)
50:09 Collaborators
50:21 - Questions

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Description

Many of us are interested in whether "networks matter." Whether in the spread of disease, the diffusion of information, the propagation of behavioral contagions, the effectiveness of viral marketing, or the magnitude of peer effects in a variety of settings, two key questions must be answered before we can understand whether networks matter: 1) how the content that flows through networks affects the patterns of outcomes we see across nodes and 2) whether the statistical relationships we observe can be interpreted causally. Aral will review what we know and where research might go with respect to content and causality in networks. He will provide two examples from each area to structure the discussion: One from an analysis of email networks and the information content that flows through them at a mid-sized executive recruiting firm and the other from a randomized field experiment on a popular social networking website that tests the effectiveness of "viral product design" strategies in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users.

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