  Public memories of significant events shared within societies and groups have
been conceptualized and studied as collective memory since the 1920s. Thanks to
the recent advancement in digitization of public-domain knowledge and online
user behaviors, collective memory has now become a subject of rigorous
quantitative investigation using large-scale empirical data. Earlier studies,
however, typically considered only one dynamical process applied to data
obtained in just one specific event category. Here we propose a two-phase
mathematical model of collective memory decay that combines exponential and
power-law phases, which represent fast (linear) and slow (nonlinear) decay
dynamics, respectively. We applied the proposed model to the Wikipedia page
view data for articles on significant events in five categories: earthquakes,
deaths of notable persons, aviation accidents, mass murder incidents, and
terrorist attacks. Results showed that the proposed two-phase model compared
favorably with other existing models of collective memory decay in most of the
event categories. The estimated model parameters were found to be similar
across all the event categories. The proposed model also allowed for detection
of a dynamical switching point when the dominant decay dynamics exhibit a phase
shift from exponential to power-law. Such decay phase shifts typically occurred
about 10 to 11 days after the peak in all of the five event categories.
