# Computational landscape of user behavior on social media

## TL;DR

This paper introduces a flexible data-driven approach to model complex human behavior on social media, revealing that despite diversity, user actions often follow simple underlying computational patterns.

## Contribution

The authors develop a minimally restrictive modeling framework that infers the simplest, most predictive representations of individual and social-driven behavior from large-scale Twitter data.

## Key findings

- Most user behavior models are within a small subclass of finite-state processes.
- User behavior, despite complexity, is governed by simple computational structures.
- The framework effectively captures how users process past actions and social inputs.

## Abstract

With the increasing abundance of 'digital footprints' left by human interactions in online environments, e.g., social 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 social 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 social input. We then apply this framework to a heterogeneous catalog of human behavior collected from fifteen thousand users on the microblogging platform Twitter. The models allow us to describe how a user processes their past behavior and their social inputs. Despite the diversity of observed user behavior, most models inferred fall into a small subclass of all possible finite-state processes. Thus, our work demonstrates that user behavior, while quite complex, belies simple underlying computational structures.