Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants

The Impact of Hurricane Maria on Out‐migration from Puerto Rico: Evidence from Facebook Data

“…From the methodological point of view, previous research using Facebook data for advertisers focused on assessing biases by comparing, in a statistical sense, social media data and representative samples to provide timely estimates of changes in annual migration stocks (Zagheni et al. ). For this paper, we do not have “ground truth” information to calibrate social media data, in part because the American Community Survey is not designed to provide estimates of movements at a time granularity finer than 1‐year intervals.…”

“…However, a major issue with these data is that they are not representative of underlying populations, and thus can lead to biased inference. Notwithstanding that, previous research has shown that biases can be modeled and filtered out using statistical approaches, in particular when social media data can be calibrated with data from representative surveys (Yildiz et al 2017;Zagheni et al 2014Zagheni et al , 2017.…”

Promises and Pitfalls of Using Digital Traces for Demographic Research

“…Interest in the use of digital trace data among demographers is growing, as evidenced by multiple sessions in recent Population Association of America (PAA) meetings (Blumenstock and Toomet 2014; Cesare et al 2015; Kashyap et al 2017; Massey 2016; Mateos and Durand 2014; Reeder et al 2014; Rosello and Filgueira 2016; Williams et al 2015; Zagheni et al 2017); recent publications (Blumenstock 2012; Blumenstock and Eagle 2012; Malik and Pfeffer 2016; Mendieta et al 2016; Palmer et al 2013; Stevenson 2014; Willekens et al 2016; Zagheni and Weber 2012; Zagheni et al 2014); and special issues of relevant social science journals, such as Social Science Research . Big Data research appears within the journal Demography as well, as illustrated by Barry’s (2006) analysis of interracial friendship using wedding photos posted online and Palmer et al’s (2013) work on spatial mobility with data collected via a smartphone app.…”

“…Estimating and correcting for bias in Facebook data is a crucial step toward extracting information from these data. Zagheni et al (2017) used data from Facebook Ads Manager to estimate stocks of migrants in the United States and to understand biases in the population of Facebook users. For each combination of age, sex, country of origin, and U.S. state of destination, they examined the difference between the fraction of foreign-born individuals estimated by the ACS and the respective quantity for Facebook users.…”

“…The discrepancy between the two estimates (i.e., the bias) was then modeled using a linear regression framework to evaluate the extent to which patterns emerged. For example, using a model in which the bias was regressed against a series of indicator variables for different demographic groups, countries of origin, and U.S. states of destination, Zagheni et al (2017) found important regularities in profiles of migrants by age and sex across U.S. states of destination or countries of origin. The authors then leveraged these regularities to improve predictions.…”