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Related Experiment Video

Updated: Mar 27, 2026

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

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Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

Huan-Kai Peng1, Hao-Chih Lee2, Jia-Yu Pan3

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Plos One
|January 16, 2016
PubMed
Summary

This study introduces data-driven engineering of social dynamics for social media interventions. It uses deep learning and optimization to find effective short-term actions for desired long-term outcomes, validated with Twitter data.

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Area of Science:

  • Social Media Analysis
  • Computational Social Science
  • Artificial Intelligence

Background:

  • Social media dynamics are complex and challenging to engineer.
  • Predicting and influencing long-term social outcomes from short-term interventions is difficult.

Purpose of the Study:

  • To define and solve the problem of data-driven engineering of social dynamics.
  • To develop a general formulation for social media intervention tasks like pattern matching and profit maximization.
  • To propose a data-driven evaluation method to replace costly field experiments.

Main Methods:

  • Formulation of a general engineering framework for social dynamics.
  • Integration of a deep learning model with convex relaxation and quadratic programming.
  • Development of a data-driven evaluation methodology.

Main Results:

  • Demonstrated effectiveness of the dynamics engineering approach on a Twitter dataset for pattern matching and profit maximization.
  • Analysis of the interplay between solution validity, pattern-matching accuracy, and intervention cost.
  • Validation of the proposed data-driven evaluation method.

Conclusions:

  • The proposed method provides an effective approach for engineering social dynamics on social media platforms.
  • The framework is versatile and applicable to multi-dimensional time series data, suggesting broad applicability.
  • This research offers a computationally efficient alternative to traditional experimental methods for social dynamics research.