TY - GEN
T1 - User behavior and change
T2 - 10th ACM International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2014
AU - Gavaldà-Mirallesy, Arnau
AU - Otto, John S.
AU - Bustamante, Fabian E
AU - Amaral, Luis A N
AU - Duch, Jordi
AU - Guimerà, Roger
N1 - Publisher Copyright:
© 2014 ACM.
PY - 2014/12/2
Y1 - 2014/12/2
N2 - Though the impact of file-sharing of copyrighted content has been discussed for over a decade, only in the past few years have countries begun to adopt legislation to criminalize this behavior. These laws impose penalties ranging from warnings and monetary fines to disconnecting Internet service. While their supporters are quick to point out trends showing the efficacy of these laws at reducing use of file-sharing sites, their analyses rely on brief snapshots of activity that cannot reveal long- and short-term trends. In this paper, we introduce an approach to model user behavior based on a hidden Markov model and apply it to analyze a two-year-long user-level trace of download activity of over 38k users from around the world. This approach allows us to quantify the true impact of file-sharing laws on user behavior, identifying behavioral trends otherwise difficult to identify. For instance, despite an initial reduction in activity in New Zealand when a three-strikes law took effect, after two months activity had returned to the level observed prior to the law being enacted. Given that punishment seems to, at best, result in short-term compliance, we suggest that incentivesbased approaches may be more effective at changing user behavior.
AB - Though the impact of file-sharing of copyrighted content has been discussed for over a decade, only in the past few years have countries begun to adopt legislation to criminalize this behavior. These laws impose penalties ranging from warnings and monetary fines to disconnecting Internet service. While their supporters are quick to point out trends showing the efficacy of these laws at reducing use of file-sharing sites, their analyses rely on brief snapshots of activity that cannot reveal long- and short-term trends. In this paper, we introduce an approach to model user behavior based on a hidden Markov model and apply it to analyze a two-year-long user-level trace of download activity of over 38k users from around the world. This approach allows us to quantify the true impact of file-sharing laws on user behavior, identifying behavioral trends otherwise difficult to identify. For instance, despite an initial reduction in activity in New Zealand when a three-strikes law took effect, after two months activity had returned to the level observed prior to the law being enacted. Given that punishment seems to, at best, result in short-term compliance, we suggest that incentivesbased approaches may be more effective at changing user behavior.
KW - Copyright law
KW - File-sharing
KW - User behavior
UR - http://www.scopus.com/inward/record.url?scp=84920447436&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84920447436&partnerID=8YFLogxK
U2 - 10.1145/2674005.2675009
DO - 10.1145/2674005.2675009
M3 - Conference contribution
AN - SCOPUS:84920447436
T3 - CoNEXT 2014 - Proceedings of the 2014 Conference on Emerging Networking Experiments and Technologies
SP - 319
EP - 324
BT - CoNEXT 2014 - Proceedings of the 2014 Conference on Emerging Networking Experiments and Technologies
PB - Association for Computing Machinery
Y2 - 2 December 2014 through 5 December 2014
ER -