Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study

Abstract

Background: The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible.

Objective: We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (R_t) as compared to social mobility estimates reported from Google and Apple Maps.

Methods: In this observational study, the estimated R_t was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of "social distancing" or "#socialdistancing" on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between R_t and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to P<.05.

Results: Negative correlations were found between Google search interest for "social distancing" and R_t in the United States (P<.001), and between search interest and state-specific R_t for 9 states with the highest COVID-19 cases (P<.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag "#socialdistancing" and at 6 days for Twitter (P<.001). Significant correlations between R_t and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at -6 and -4 days. Meanwhile, changes in social mobility correlated best with R_t at -2 days and +1 day for workplace and grocery/pharmacy, respectively.

Conclusions: Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with R_t when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

Keywords: Apple Maps; COVID-19; Google Maps; Google Trends; Instagram; Twitter; epidemic; estimated reproduction number; pandemic; public health surveillance; reproduction number; social distancing; social media; social media surveillance.

Figure 1

The time-variant or effective reproduction number (R_t) represents the mean number of secondary cases generated by a primary case over a sliding weekly window. The estimated R_t is shown for the period of February 28 to April 5, 2020, calculated from the number of COVID-19 cases by symptom onset, with a mean serial interval of 3.96 days and SD of 4.75 days. The shaded error bands are equal to 1 SD of the estimated R_t for each date.

Figure 2

The left-most graph (a) shows the relationship between Google Trends search volume index (SVI) for “social distancing” and estimated R_t for the United States over the time period from March 5 to April 5, 2020. The light blue line represents the Google SVI and the dark grey line represents the estimated R_t. The middle and right-most graphs show the relationships between estimated R_t and total social media mentions for “#socialdistancing” on (b) Instagram and (c) Twitter over the same time period. Light blue lines refer to total number of mentions for "#socialdistancing" and dark grey lines represent the estimated R_t. Error bands shown in light gray shading represent 1 SD from the mean R_t calculated at each date. R_t: time-varying reproduction number.

Figure 3

The estimated R_t was calculated individually for the top 9 states with the most confirmed COVID-19 cases on April 5, 2020. They are graphed for the period of March 5 to April 5 for each state (dark line) along with the Google search index for “social distancing” in each state for the same time period (light blue bars). Error bands represent 1 SD from the mean estimated R_t at each date. R_t: time-varying reproduction number; SVI: search volume index.

Figure 4

Cross-correlations (represented by ρ2, the coefficient of determination) between estimated R_t and social mobility changes, and social media mentions in the United States. The black lines represent the peak correlation. Social mobility changes include traffic at grocery, pharmacy, and workplace locations based on Google Maps tracking. Social media measures include mentions of “#socialdistancing” on Instagram and Twitter. Pharm: pharmacy; R_t: time-varying reproduction number.