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Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization
- Source :
- PLoS ONE, PLoS ONE, Vol 11, Iss 1, p e0146490 (2016)
- Publication Year :
- 2016
- Publisher :
- Public Library of Science, 2016.
-
Abstract
- In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.
- Subjects :
- Mathematical optimization
lcsh:Medicine
02 engineering and technology
Bioinformatics
Field (computer science)
Data-driven
Pattern Recognition, Automated
Artificial Intelligence
020204 information systems
Image Interpretation, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Humans
Pattern matching
lcsh:Science
Set (psychology)
Physics
Multidisciplinary
business.industry
Profit maximization
Deep learning
lcsh:R
05 social sciences
Models, Theoretical
Social dynamics
Transformation (function)
lcsh:Q
Artificial intelligence
0509 other social sciences
050904 information & library sciences
business
Social Media
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....d6c5561cd0f02f9a89f71998fab515b9