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Sinan Aral (NYU) will review what we know and where work might lead in the future with respect to identifying causal peer influence in social networks.
Measuring influence and finding influential people in social networks is now all the rage. But, true estimates of influence are fraught with statistical difficulties that naïve scoring methods cannot address. So, how can we robustly measure influence and identify influential people in networks? Whether in the spread of disease, the diffusion of information, the propagation of social contagions, the effectiveness of viral marketing, or the magnitude of peer effects in a variety of settings, a key problem is understanding whether and when the statistical relationships we observe can be interpreted causally.
Sinan Aral will review what we know and where work might lead in the future with respect to identifying causal peer influence in social networks and the importance of causal inference for understanding the spread of products, political views, and public health behaviors through society. He will provide examples from large scale observational and experimental studies in online social media networks and organizational email networks, and will focus the second half of the talk on recent experimental work measuring “Social Influence Bias" in online ratings.
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