Computational Methods for Social and Behavioral Scientists

Jeremy Foote

Brian Lamb School of Communication

2026-11-03

Initial discussion

  • What do you think of when you hear “computational methods”?
  • How are computational methods used in your field?

My goals

  • Argue that digitization of data is one of the most important changes in social science
  • Introduce some computational methods tools and examples of research using them
  • Have a conversation and not just a lecture

Digital data revolution

  • Many of the actions that people take are mediated by digital technologies
  • Often, these actions are recorded and stored
    • Social media
    • Online communities
    • Phone calls
    • GPS locations
    • Financial transactions
    • Health records

Digital data revolution

  • These data are typically:
    • Big
    • Always-on
    • Non-reactive
    • Temporally fine-grained
    • Rich
  • They allow us to study people as they act, making real decisions, at scale
Salganik, M. (2018). Bit By Bit

Computing revolution

  • The cost of storing and analyzing data has plummeted

Our World in Data chart: historical price of computer memory and storage per terabyte, falling from trillions of dollars in the 1950s to under 100 dollars today.

Computing revolution

Our World in Data chart: transistors per microprocessor, rising exponentially from a few thousand in 1971 to tens of billions in 2021 (Moore's law).

What is Computational Social Science?

  • Broadest version: using computers to help with social science research
  • Could include statistical and visualization software, Qualtrics, etc.
  • I’ll focus on using computational tools that enable new kinds of research

Data science vs. Computational Social Science

  • Imagine some sort of social process; we might represent it as:
    • \(Y = \mathbf{X}\beta + \epsilon\)
  • Data science (and particularly machine learning) is often interested in prediction
    • Caring about \(\hat{Y}\) rather than \(\hat{\beta}\)
  • Computational social science is typically interested in explanation and understanding (getting \(\hat{\beta}\) close to the true \(\beta\))

Data science for social scientists

  • Sometimes scientists are interested in prediction
    • E.g., Classifying people or texts
  • Prediction tools can also improve explanation:
    • Testing on held-out data shows whether findings generalize
    • Flexibly controlling for many variables
    • Reducing dimensionality and generating hypotheses

Discussion pause

  • Are there ways that machine learning is used in your field?
  • Are there ways that you think it could be?

Examples of Computational Social Science

Large-scale analyses

Google Books Ngram chart, 1800 to 2022: 'quantitative methods' rises from about 1900, 'computational methods' from about 1950, and 'qualitative methods' climbs steeply after 1970; quantitative and qualitative end roughly tied, with computational methods at about 60 percent of their level.

Large-scale analyses

  • Goel et al. looked at over 1 billion tweets to study how information spreads

Figure from Goel et al. showing six Twitter cascades as tree diagrams, ranging from broadcast-style (one account reaching many people directly, flat and wide) to viral-style (long chains of person-to-person sharing), each with a curve of cascade size over time.

Large-scale analyses

  • Really important opportunities for studying groups, which are often expensive and difficult to study
  • E.g., Our paper looking at the early-stage structures of ~1,000 wiki communities
    • We found that they did not follow the predictions of existing theories of group formation

Social network analysis

  • Studying outcomes in terms of relationships
  • Doesn’t assume that people are independent
  • Statistical methods are complex and computationally expensive

Computational text analysis

  • Reading and analyzing texts takes a long time!
  • Automated methods can quickly analyze vast amounts of text
  • Inductive methods (unsupervised)
    • Topic modeling
    • Word embeddings

Computational text analysis

  • Deductive methods (supervised)
    • Sentiment analysis
    • Named entity recognition
    • Classification
  • Many of these methods are being replaced by large language models (LLMs) that can be used for both inductive and deductive methods

Agent-based modeling

  • Using theory, develop a model of how individuals make decisions
  • Simulate what happens when many individuals interact

    Screenshot of the Parable of the Polygons simulation: a grid of intermixed blue squares and yellow triangles, next to an empty graph of segregation over time and a Start button.

Discussion pause

  • Any questions about these methods?
  • Are there ways that you think these methods could be used in your field?

Large-scale field experiments

  • Facebook election experiments
    • 2010: 61 million users nudged to vote
    • 2020: feed and deactivation experiments
  • Our experiments on toxicity and chatbots
    • Behavior before and after participating

Reddit profile of u/the_nice_bot_, with a pinned post explaining that the bot is part of a research study on whether chatbots can help people reflect on comments that were removed as toxic.

Bond et al. (2012), Nature; Guess et al. (2023), Science; Allcott et al. (2024), PNAS

Citizen science

  • Pre-computers, organizing data was incredibly expensive and difficult

Black-and-white photo of James Murray, editor of the Oxford English Dictionary, standing in his scriptorium surrounded by shelves of handwritten paper slips.

Citizen science

  • Today, we can organize the work of thousands of people fairly easily
    • iNaturalist
    • Galaxy Zoo
    • Protein folding (FoldIt)

Generative AI tools

  • LLMs in social science research
    • Research subjects (understanding LLMs or simulating human behavior)
    • Research assistants (e.g., writing code, summarizing papers, classifying texts, interviewing people)
    • Research sites
      • Understanding how people interact with LLMs
      • LLM Interventions (e.g., persuasion, education, mental health)

Generative AI tools

  • Useful as:
    • Brainstorming partners
    • Editors / reviewers
    • Writing partners
  • Norms are still evolving

LLM difficulties

  • Validity: check against humans
  • Sensitive to prompts and models
  • Models change and disappear
  • Simulated people aren’t people
  • AI-generated “human” data
  • Sycophancy
  • Privacy and IRB rules

Discussion pause

  • Any questions about these methods?
  • How are you using AI in your current research workflow?

Ethical concerns of Computational Social Science

  • People are often unaware of how their data are being used, even if it is “public”
  • Data can be used by bad actors
  • Really important to balance privacy and research goals

Methodological concerns of Computational Social Science

  • Hard to do, especially for really large-scale analyses (LLMs are making this easier)
  • Algorithmically confounded
  • Data may be missing or biased
  • Data collection processes may change over time in invisible ways
  • Nonrepresentative samples

Learn more

  • Salganik, M. (2018). Bit By Bit
  • Take my class (Spring 2027)!

Optional activity (if there’s time)

Design a study that uses computational methods to study a question you are interested in