← Back to Projects

Correcting Misconceptions with Crowdsourced Narratives

Behavioral Science Belief Change Belief Modeling
Covid-19 Surveys
Interactive visualization communicating causes of racial inequity in Chicago based on an internet discussion mined from the Reddit forum Change My View. Participants interacted with maps demonstrating how historical redlining practices produced present day income disparities between African Americans and Whites. Participants who interacted with this data narrative demonstrated a signifant shift in post-interaction beliefs about racial inequality, demonstrating how crowdsourced data narratives can be applied to education random populations about social issues. Visualization software used open source software to merge data from the US census and University of Richmond Redline Mapping Project.

πŸ” Quick Summary

πŸ’» Quick Access to Open Data and Code

πŸ“„ What this Project Does

The internet has given rise to conspiracy thinking, AI-generated media, and identity politics, which all have the potential to severely limit democratic decision-making and national consensus. However, not all corners of the internet promote this type of information. This project explores how naturally occurring arguments on the internet, including those sourced from communities like Reddit's Change My View, can be used as data narratives to correct public misconceptions.

Using data science methods, I mine and parse online arguments, identify belief-shifting conversations, extract the narrative and evidentiary structure of persuasive responses, and repurpose them into educational interventions tested on randomized populations of Americans.

Rather than designing corrective messages from scratch and relying on prior academic literature on belief change and higher-level reasoning, this crowdsourcing approach provides a scalable, bottom-up approach to curating corrective information that leverages readily available dynamic online data.

πŸ’‘ Key Findings

🎯 Why It Matters

Traditional approaches to correcting public misconceptions use top-down intervention tactics. These methods develop slowly, depend heavily on intervention designers' expertise, and often struggle to reach diverse populations with varying beliefs.

A better approach sources naturalistically validated information to create educational content. This approach enables scalable, AI-assisted designs that can both accelerate misconception correction and uncover novel pathways not yet explored in research.

πŸ› οΈ How It Works

  1. Source Argument Data: Collected 30,000+ argument threads from Reddit’s Change My View using the Reddit API and custom NLP pipelines.
  2. Identify Belief Change Messages: Developed NLP methods to extract features of corrective content and identify messages marked with a Ξ” (delta), indicating belief change.
  3. Recompose as Educational Interventions: Converted these arguments into persuasive vignettes and interactive data narratives.
  4. Deploy and Evaluate Interventions: Tested them with randomized groups (N = 3,000) using pre/post belief assessments.
  5. Model Belief Updating: Used multilevel ordinal models to analyze post-intervention belief shifts.

πŸ“š Related Publications

Click here to view an interactive data narrative based on a Reddit argument that was effective at correcting misconceptions about racial inequality on Change My View and in random samples of Americans.