Social and Political Data Science: Introduction

Computational Social Science

Karl Ho

School of Economic, Political and Policy Sciences

University of Texas at Dallas

Introduction

Computational social science ≠ computer science + social data 

  • What is Computational Social Science?

    • Emerging interdisciplinary field that combines social science theories and methods with computational techniques to study complex social phenomena.

    • Featuring large-scale data analysis and computer simulations to explore social dynamics, understand human behavior, and test social theories.

    • Employing data from social media, online networks, digital archives, and administrative records, to uncover patterns, trends, and insights.

  • How CCS works?

    • Introducing computational tools and algorithms that can handle the complexities of social data, such as network analysis, text mining, machine learning, and agent-based modeling.

    • Transforming social science research, offering new ways to address longstanding questions, discover novel patterns, and gain deeper insights into social phenomena.

       

  • Challenges

    • ethical considerations related to data privacy, bias, and algorithmic transparency

    • interdisciplinary collaboration and methodological rigor.

       

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  • CCS needs new training and education programs to equip researchers with the necessary skills and knowledge to leverage computational techniques effectively.

  • New Challenges

    • Misalignment of universities
      • Integration of social science with computer science and data science is slow
      • Parochialism
        • Data Science in traditional STEM disciplines not recognizing structure and generation of new big/social data
      • Multidisciplinary research may be less well recognized and rewarded.
    • Inadequate Data-Sharing Paradigm
      • Facebook, Twitter limit data access (API changes, blocks to data collection)
      • Found data DGP
      • Big tech gags research/academic freedom
  • Suggestions

    • Strengthen collaboration
    • New data structures
    • Factor ethical, legal and social implication in data design
    • Reorganize higher institutions/universities

What is difference between CSS and Data Science?

  • CSS uses Data Science methods and tools for Social Science studies and solve social and political problems:

    • Machine Learning

    • Collection of Big/Social data

    • Analytics: visualization, data/information management

How about SDAR?

  • Social Data Analytics and Research is:

    • Data Science 

    • Also CSS on computational part

    • Interdisciplinary by design, not limited to Social Science

      • Causal Inference, Methods, Forecasting (Economics)

      • Social and Political studies (Sociology, Political Science)

      • Policy studies (PPPE, PNM)

      • Spatial analysis (GIS)

Some key terms

  • Algorithm

  • Artificial Intelligence (AI)

  • Computational thinking

  • Computational modeling

  • Parallel computing

  • Machine learning

  • Deep learning (NN, CNN, RNN)

  • NLP (Natural Language Processing)

  • Language Models or Large Language Models (LLM)

  • Agent-based Modeling

ML, DL, NLP and AI

CNN Model in Action

CNN Model in Action

Parallel Computing: Amdahl's law

Amdahl's Law: 

$$S = \frac{1}{(1-P)+\frac{P}{N}}$$

Where: 

- S is the speedup of the system.
- P is the proportion of the system that can be improved.
- N is the number of processors used in the system. 

Amdahl's Law is used to calculate the theoretical speedup of a system when making improvements to only a portion of the system, given the proportion of the system that can be improved and the number of processors used in the system.

Reference:
Amdahl, Gene M. "Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities." Proceedings of the April 18-20, 1967, Spring Joint Computer Conference. 1967.

Parallel Computing: Amdahl's law

  • doSNOW has the advantage of working on both Windows and Mac OS X.

R packages: doSNOW

CSS: Introduction

By Karl Ho

CSS: Introduction

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