We observe a similar pattern as that observed in the goodness of fit setting, with a shift in the best performing distribution from Bernoulli (which simply models if a feature exists or not) toward something more Gaussian (Poisson or Normal) as we move along from message-level to county-level analysis and from unigrams to lexica.
note that at higher levels of analysis (at user and county levels) as the distribution of features becomes closer to Normal, modeling features as Bernoulli is clearly sub-optimal where as at the message level modeling unigrams as a Bernoulli is superior.