Will Facebook lose 80 percent subscribers between 2015-2017?

According to a research done by Princeton University, Facebook will lose 80 percent of its peak user base between 2015 and 2017.

The university has applied irSIR (infectious recovery SIR) model of OSN (online social networks) dynamics on Google data for search query Myspace. The same model was then applied to the Google data for search query Facebook. Extrapolating the best fit model into the future suggests that Facebook will undergo a rapid decline in the coming years, losing 80 percent of its peak user base between 2015 and 2017.

Facebook has hit back at Princeton University and said, “In keeping with the scientific principle “correlation equals causation,” our research unequivocally demonstrated that Princeton may be in danger of disappearing entirely.”

This trend suggests that Princeton will have only half its current enrolment by 2018, and by 2021 it will have no students at all, agreeing with the previous graph of scholarly scholarliness says Facebook.

For e.g. MySpace, founded in 2003, reached its peak in 2008 with 75.9 million unique monthly visits in the US before subsequently decaying to obscurity by 2011. MySpace was purchased by News Corp for $580 million during its rapid growth phase in 2005 and was sold six years later at a loss for $35 million in 2011.

The Google data for search query “MySpace” and search query “Facebook” were analyzed by Princeton researchers. MySpace, exhibited a rise in 2005, a peak between 2007 and 2008, and a steady decline between 2009 and 2011.

Princeton Research based on Epidemiological Modelling 

Fit                              20 Percent Date

MySpace, SIR           12/2010

MySpace, irSIR         11/2010

Facebook, Best           1/2015

Facebook, Early         7/2014

Facebook, Late           2/2016

Source: Princeton University

The Google search query data for the term Facebook shows a large jump in search queries during early October 2012. This jump is assumed to be artifactual, as it is unlikely that search activity for Facebook suddenly jumped by 20 percent in under a week and remained consistently at this elevated level in the weeks following but the Facebook curve shows a decline in search activity starting in 2013, which corroborates reports that the OSN started losing some of its younger user base in 2013.

Applying the irSIR model to Facebook shows a high quality fit for the available search query data over the time period of January 2004 to the last reported data point at the time of writing. Extrapolating the best fit into the future shows that Facebook is expected to undergo rapid decline in the upcoming years, shrinking to 20 percent of its maximum size by December 2014.

Princeton University has use epidemiological models to analyze publicly available Google search query data for different OSNs, which can be obtained from Google’s “Google Trends” service. Google search query data has been used in a range of studies, including the monitoring of disease outbreak, economic forecasting and the prediction of financial trading behavior.

To test the theory of disease-like adoption dynamics of OSNs, we use publicly available historical Google search query data as a proxy for OSN usership, as has been done in previous work. The public nature of this data is an important feature of the approach used in this paper, as historical OSN user activity data is typically proprietary and difficult to obtain. The Google search query data is obtained from Google’s “Google Trends” service and reports the relative number of Google search queries for a given search term.

The researchers have modified the traditional SIR model of disease spread by incorporating infectious recovery dynamics such that contact between a recovered and infected member of the population is required for recovery. The proposed infectious recovery SIR model (irSIR model) is validated using publicly available Google search query data for “MySpace” as a case study of an onnline social networks (OSN) that has exhibited both adoption and abandonment phases.

The irSIR model is then applied to search query data for “Facebook,” which is just beginning to show the onset of an abandonment phase. Extrapolating the best fit model into the future predicts a rapid decline in Facebook activity in the next few years.

The dynamics governing the rapid rises and falls of OSNs are therefore not only of academic interest, but also of financial interest to incumbent and emerging OSN providers and their stakeholders.

The research was based on Epidemiological modeling of online social network dynamics and was conducted by John Cannarella, Joshua A Spechler, Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA.

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