Word Frequency and Content Analysis Approach to Identify Demand Patterns in a Virtual Community of Carriers of Hepatitis C
Bibliographic record
Abstract
BACKGROUND: Orkut, a Brazilian virtual social network, is responsible for popularization of the Internet among people of low income and educational level. It's observed that rapid growth of virtual communities can be reached by low cost Internet access in community local area network houses. Orkut poses an important social resource for Brazilian patients with chronic conditions like hepatitis C virus (HCV) carriers, who face several obstacles in adapting to everyday difficulties. OBJECTIVE: Identify Patterns of Recurring Demands (PRD) expressed in messages posted by members of virtual communities dedicated to HCV carriers. METHODS: Pre-selection: we identified terms commonly associated to HCV on generic Internet searches (primary Keywords - Kps); Kps were used to identify the most representative HCV communities in a virtual community site (Orkut); all messages published along 8 years on all topics of the community were collected and tabulated; the word frequency was used to construct a "word cloud" (graphic representation of the word frequency) on which was applied a content analysis technique. RESULTS: The most cited terms expressed: search for information about medications (prescribed and "forbidden"); emphasis on counting time, which were interpreted as surviving expectations; frequent mention of God, doctors, and "husbands" (female carriers were 68%). These elements provided material for further research - they will be useful in the construction of categories in discourse analysis. CONCLUSIONS: The present work is a disclosure of preliminary findings considered original and promising. The word frequency/content analysis approach expressed needs of social support and material assistance that may provide subsidies for further qualitative approach and public health policies aimed to HCV carriers. The study of PRD by word frequency may be useful in identifying demands underestimated by other means.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".