Crowdsourcing Better Validation for Topic Models Used as Measures

🔎 The problem

Topic models from computer science are powerful for exploring large text collections, but when social scientists use them as measures, extra care is required to ensure the model outputs actually capture the intended concepts. A review of current practice shows that extensive model validation is increasingly rare or, at minimum, not systematically reported in papers and appendices.

🧭 What was done

• Refined an existing crowdsourcing validation procedure developed by Chang and coauthors to assess topic quality.
• Developed new crowdsourced procedures specifically designed to validate conceptual labels that researchers attach to topics (i.e., whether a topic actually represents the researcher’s intended concept).

🧪 How the approach was demonstrated

• Applied the combined validation procedures to an analysis of Facebook posts by U.S. Senators.
• Packaged software and practical guidance so other researchers can run the same validation workflow on their own topic models.

✔ Key findings and contributions

• Current reporting often omits systematic topic-model validation, creating a gap between exploratory use and reliable measurement.
• Crowdsourced validation—both for topic coherence and for researcher-assigned labels—provides a transparent, replicable way to evaluate whether topics function as intended measures.
• The paper delivers a general-purpose toolset (method + software + guidance) that supplements existing, case-specific validation practices.

💡 Why this matters

Reliable measurement is essential when topic models are used to test social-science hypotheses. By offering a practical, crowd-based validation workflow and software, the work improves standards for documenting and defending topic-based measures while acknowledging that tailored, case-specific validation will always be ideal.