Corpus-Assisted Qualitative Analysis for Space Communication

Chen et al. (PLoS ONE, Jul 9, 2026) compared state science agency posts on Weibo and X and found systematic differences in topics, addressing-term mixes, and co-occurrence networks.

This post summarizes the study window (Apr 1–Oct 1, 2023), the methods used (KH Coder, Jaccard co-occurrence, addressing-term frequencies), and a practical workflow to reproduce the analyses using Evidano.

• Study corpora: Chinese = 895 posts (24, 769 tokens); English = 1, 204 posts (25, 063 tokens).
• Chinese accounts mixed national pride, personable address, and operational updates; U.S. accounts emphasized professional expertise, mission execution, and audience engagement.
• Methods included token frequencies, co-occurrence networks (Jaccard), and addressing-term frequency analysis, with a minimum term-frequency filter of ≥30 and post-level analysis units.
• You can reproduce and extend these analyses in days using the seven-step runbook and Evidano’s ingestion, multilingual tokenization, AI-assisted coding, and export features.

Chen et al. (published July 9, 2026 in PLoS ONE) compared state science agency posts on Weibo and X using KH Coder and a corpus-assisted discourse framework.

Read the original paper on PLoS ONE.

• Scope: posts from Apr 1 to Oct 1, 2023; Chinese corpus = 895 posts / 24, 769 tokens; English corpus = 1, 204 posts / 25, 063 tokens.
• Key contrasts: Chinese accounts mix national pride, personable address, and operational updates; U.S. accounts emphasize professional expertise, mission execution, and audience engagement.
• Methods: co-occurrence networks, addressing-term frequencies, and word-association (Jaccard coefficient) via KH Coder.

The paper built two cleaned corpora and used KH Coder to run quantitative content analysis, including token frequencies, co-occurrence networks, and word-association around self-addressing terms.

The authors then read concordances and interpreted patterns through positioning theory and critical discourse analysis.

• Why it matters: combining corpus linguistics with critical discourse analysis increases reproducibility while preserving contextual interpretation.
• Key methodological choices to note: manual segmentation checks, minimum term-frequency filter (≥30), and analysis unit = post-level segments.

This runbook gives seven steps to reproduce and extend the study in two weeks using public post exports, a codebook, and Evidano’s ingestion and analysis features.

• Minimal inputs: public post exports (CSV/JSON) or direct scrape from platform handles, an optional codebook, and a target date window.
• 1) Import: upload Weibo/X exports or point the Evidano scraper at verified accounts.
• 2) Preprocess: enable Chinese/English tokenization, add project-specific dictionary entries (e.g., ‘长征’, ‘Artemis II’).
• 3) Auto-code: run AI-assisted thematic coding, then review a 200-item sample and accept or adjust codes.
• 4) Network: generate co-occurrence network (post-level unit) and export top 150 edges (Jaccard) for visualization.
• 5) Addressing-term analysis: define self/audience/nation groups and compute frequencies by segment.
• 6) Cross-segment: run cross-tab analysis (platform × theme × time) and flag significant shifts.
• 7) Report: use Evidano’s dashboard to export visuals, exemplar quotes, and a stakeholder one-page brief.

Chen et al.’s July 9, 2026 PLoS ONE paper demonstrates how corpus methods make discourse claims testable, scalable, and comparative across languages and platforms.

If teams need to move from anecdote to evidence, for example to compare framing across countries, measure shifts after a campaign, or produce audit-ready reports, Evidano streamlines the whole pipeline.

• Ready to try this on your own corpora? Start a secure trial and reproduce these analyses in days with Try Evidano for free.