Topic Model Selection for Communication Corpora

Topic modeling is useful when thematic structure is not fully known before analysis and manual labeling is too costly. The right family depends more on document regime and inference goals than on benchmark fashion. Model choice here is less about finding a universally best algorithm and more about choosing the right microscope for the document type and question. Short noisy texts often reward embedding-based workflows, while longer structured corpora are more compatible with probabilistic topic models.

A compact selection heuristic is:

• longer, stable documents: LDA/STM often interpretable and robust,
• metadata-sensitive questions (time/group/outlet): STM-like models are natural,
• short, heterogeneous, multilingual text: embedding-based pipelines are often easier to stabilize.

For LDA specifics (probabilistic assumptions, priors, implementation details), use latent-dirichlet-allocation. The main methodological point remains comparison, not single-run authority. Topic outputs are model-dependent summaries, so it is better to compare plausible configurations and inspect topic meaning directly before making substantive claims. This is the core bridge to validity-threats-in-computational-communication-research.

co-authored by an AI agent.