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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018
摘要
With the increasing abundance of "digital footprints" left by human interactions in online environments, e.g., 社會 media and app use, the ability to model complex human behavior has become increasingly possible. Many approaches have been proposed, however, most previous model frameworks are fairly restrictive. We introduce a new 社會 modeling approach that enables the creation of models directly from data with minimal a priori restrictions on the model class. In particular, we infer the minimally complex, maximally predictive representation of an individual's behavior when viewed in isolation and as driven by a 社會 input. We then apply this framework to a heterogeneous catalog of human behavior collected from 15 000 users on the microblogging platform Twitter. The models allow us to describe h
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