Qualitative analysis of space science communication

This section summarizes implications for UX researchers, policy analysts, and communications teams based on Chen et al.'s findings.

Use theme extraction plus addressing-term annotation to map institutional relation to audiences, and use co-occurrence networks to surface clusters that manual coding might miss.

UX and qualitative researchers should combine theme extraction with addressing-term annotation to map how institutions relate to audiences.

If your goal is to map how institutions relate to audiences, combine theme extraction with addressing-term annotation (as Chen et al. did). Use co-occurrence networks to surface clusters you might not see in manual coding.

Compare segments (platform, language, campaign) quantitatively: report frequencies plus representative concordance lines for each theme to avoid cherry-picking.

Policy and security analysts can use quantified differences to diagnose semiotic alignment with national narratives and to track shifts over time.

Quantified differences (e.g., nation-addressing 45.6% vs 3.4%) are diagnostic: they reveal semiotic alignment with national narratives. Track shifts over time to detect strategic repositioning after policy events.

Cross-check with platform moderation or partner mentions to interpret operational transparency versus legitimating discourse.

Communications teams should test addressing frames because language choices materially change perceived proximity and credibility.

Language choices (colloquial self-addressing vs standardized agency labels) materially change perceived proximity and credibility. Test alternative address frames in A/B content experiments and measure engagement by segment.

Use co-occurrence clusters to craft messages that align subject matter (mission details) with the tone your audience expects (patriotic vs professional).

Evidano supports ingestion, multilingual preprocessing, thematic and network analysis, AI-assisted coding, and traceability for this use case.

Import scraped social posts or upload CSVs, run multilingual segmentation, generate co-occurrence networks, and trace results back to source quotes within the platform.

Evidano ingests scraped social posts or CSVs and automatically segments Chinese and English text while supporting custom dictionaries.

Import scraped social posts or upload CSVs of posts and metadata; Evidano automatically segments Chinese and English text and supports custom dictionaries for domain terms (e.g., “长征”, “Artemis II”).

Optional: apply translation with a custom dictionary to align tokens across languages before cross-corpus comparison.

Evidano generates thematic clusters and co-occurrence networks quickly and exports top edges with representative concordance lines.

Generate thematic clusters and co-occurrence networks (Jaccard or PMI) in minutes; export top edges and representative concordance lines to support CDA-style interpretation.

Visualize hierarchical codes, subcodes, and network clusters for stakeholder-ready figures that replicate KH Coder outputs with interactive filters.

Evidano runs AI-assisted coding with traceability to reduce inter-coder drift and supports cross-segment frequency and significance tests.

Import or build codebooks and run AI-assisted coding to apply labels consistently; review and accept suggestions to reduce inter-coder drift.

Run cross-segment frequency and significance tests (e.g., address-term distributions by country, platform, or time window) and export tables for methods appendices.

Evidano links every result back to source quotes and post metadata and ensures data encryption without training third-party models.

Every result links back to source quotes and post metadata for transparent CDA work. Use AI chat over your corpus to iterate hypotheses (for example, “show me posts where we and China co-occur with ‘成功’”).

Data is encrypted and never used to train third-party models, important when handling state-related content or sensitive research datasets.