TY - GEN
T1 - Identifying platform effects in social media data
AU - Malik, Momin M.
AU - Pfeffer, Jürgen
N1 - Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84979625990&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84979625990
T3 - Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016
SP - 241
EP - 249
BT - Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016
PB - AAAI Press
T2 - 10th International Conference on Web and Social Media, ICWSM 2016
Y2 - 17 May 2016 through 20 May 2016
ER -