Paper Info
Title
Identifying Platform Effects in Social Media Data.
Abstract
Even when external researchers have access to social media data, they are not privy to decisions that went into platform design—including the measurement and testing that goes into deploying new platform features, such as recommender systems, seeking to shape user behavior towards desirable ends. Finding ways to identify platform effects is thus important both for generalizing findings, as well as understanding the nature of platform usage. One approach is to find temporal data covering the introduction of a new feature; observing differences in behavior before and after allow us to estimate the effect of the change. We investigate platform effects using two such datasets, the Netflix Prize dataset and the Facebook New Orleans data, in which we observe seeming discontinuities in user behavior but that we know or suspect are the result of a change in platform design. For the Netflix Prize, we estimate user ratings changing by an average of about 3% after the change, and in Facebook New Orleans, we find that the introduction of the ‘People You May Know’ feature locally nearly doubled the average number of edges added daily, and increased by 63% the average proportion of triangles created by each new edge. Our work empirically verifies several previously expressed theoretical concerns, and gives insight into the magnitude and variety of platform effects.
Year
Venue
Field
2016
ICWSM
Recommender system,World Wide Web,Social media,Generalization,Computer science,Temporal database,Artificial intelligence,Suspect,Machine learning
DocType
Citations 
PageRank 
Conference
4
0.39
References 
Authors
16
2
Name
Order
Citations
PageRank
Momin M. Malik1112.18
Jürgen Pfeffer234626.57