Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior

Autor(es)

Barbosa, Samuel ; Cosley, Dan ; Sharma, Amit ; Cesar, Roberto M., Jr. ; ACM

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16); v. N/A, p. 13-pg., 2016-01-01.

Resumo

Online communities provide a fertile ground for analyzing people's behavior and improving our understanding of social processes. Because both people and communities change over time, we argue that analyses of these communities that take time into account will lead to deeper and more accurate results. Using Reddit as an example, we study the evolution of users based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between users yearly cohorts we find wide differences in people's behavior, including comment activity, effort, and survival. Further, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson's Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward. (AU)

• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático