In this presentation we discuss our work based on a global, sub-national data set on cross-societal transmission. We use data on how words sound, how they are spelled, and what they mean, to estimate the etymological roots of each word and identify loanwords, using machine learning techniques. Societies with more loanword exchange share more societal traits in common, reinforcing the idea that cross-societal linguistic transmission proxies more generally for the diffusion of traits between groups. We use this to show that improved gains from trade causes cultural convergence, but that economic leverage determines the direction of convergence within a pair of language groups. Finally, we introduce a purely language data-driven approach to exploit the relevant information encoded in language to estimate the geographic origins of a historical phenomenon. We apply the methodology by tracing the history of religious words back to their originating languages to estimate the geographic origin of religious spread for each of Buddhism, Hinduism, Islam, Judaism, and Christianity within 500km of historical estimates.