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Natural language processing has made progress in incorporating human contextinto its models, but whether it is more effective to use group-wise attributes(e.g., over-45-year-olds) or model individuals remains open. Group attributesare technically easier but coarse: not all 45-year-olds write the same way. Incontrast, modeling individuals captures the complexity of each person'sidentity. It allows for a more personalized representation, but we may have tomodel an infinite number of users and require data that may be impossible toget. We compare modeling human context via group attributes, individual users,and combined approaches. Combining group and individual features significantlybenefits user-level regression tasks like age estimation or personalityassessment from a user's documents. Modeling individual users significantlyimproves the performance of single document-level classification tasks likestance and topic detection. We also find that individual-user modeling doeswell even without user's historical data.

Comparing Human-Centered Language Modeling: Is it Better to Model Groups, Individual Traits, or Both?