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AbstractOne of the strongest empirical regularities in spatial demography is that flows of migrants are positively associated with population stocks at origin and destination and are inversely related to distance. This pattern was formalized into what are known as gravity models of migration. Traditionally, distance is measured geographically, but other measures of distance, such as cultural distance, are also relevant in explaining migration flows. However, measures of cultural distance are not widely adopted in the literature on modeling migration flows, partially because of the difficulties associated with operationalizing and producing these measures across space and time. In this paper, we use a scalable approach to obtain proxies for measuring cultural similarity between countries by using Facebook data and illustrate the impact of incorporating these measures, based on food and drink interests, into gravity models for predicting migration. Our results show that, despite their limitations, the new measures of cultural similarity derived from Facebook data improve the prediction power of traditional gravity models and have a predictive capacity comparable to that of classic variables used in the literature, such as shared language and history. The results open up new opportunities for understanding the determinants of migration and for predicting migration when considering broader and complementary perspectives on the meaning and measurement of distance.

OBJECTIVES: Social media messages have been increasingly used in health campaigns about prevention, testing, and treatment of HIV. We identified factors leading to the retransmission of messages from expert social media accounts to create data-driven recommendations for online HIV messaging. DESIGN AND METHODS: We sampled 20,201 HIV-related tweets (posted between 2010 and 2017) from 37 HIV experts. Potential predictors of retransmission were identified based on prior literature and machine learning methods and were subsequently analyzed using multilevel negative binomial models. RESULTS: Fear-related language, longer messages, and including images (e.g., photos, gif, or videos) were the strongest predictors of retweet counts. These findings were similar for messages authored by HIV experts as well as messages retransmitted by experts but created by non-experts (e.g., celebrities or politicians). CONCLUSIONS: Fear appeals affect how much HIV messages spread on Twitter, as do structural characteristics like the length of the tweet and inclusion of images. A set of five data-driven recommendations for increasing message spread is derived and discussed in the context of current CDC social media guidelines.