How do people communicate about sensory descriptors in social media? An investigation comprising floral descriptors, four beverages, and thirteen English‐speaking countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The internet has become a mainstay of modern‐day life, with people worldwide using social media platforms to discuss ideas and exchange opinions. In recent years, social media platforms have become a data source for research focusing on consumers' natural and spontaneous lexicons. The increased use of social media and virtual meetings means people can exchange ideas, and cultural boundaries have blurred. Thus, this study aims to investigate how culture influences the use of the beverage descriptor “floral” on social media for four different beverages when people share the same language. A social media study was performed, collecting data over 1 year in 13 English‐speaking countries: Australia, Canada, South Africa, the United Kingdom, Singapore, New Zealand, and seven Caribbean Islands. Words associated with four beverages (beer, cocktails, tea, and wine) and floral were filtered on social media mentions. A total of 258,221 mentions were obtained from different types of social media such as Twitter, Instagram, Facebook, and so forth. The mentions were filtered to obtain the frequency of words by country. Each beverage was analyzed separately, and contingency tables were arranged by country and descriptor. A correspondence analysis was performed on each contingency table to identify relative similarities and dissimilarities between the countries in the study. Overall, rose was the most cited floral descriptor, and there was a link between beer and wine descriptions. However, for cocktails and tea, the floral descriptors varied per country. Thus, it suggests that the impact of culture on the use of floral descriptors depends on the beverage being discussed. Practical Applications The results obtained in this study have relevance for people interested in sensory and specifically in beverage descriptions, as well as those involved in the alcohol industry, particularly those involved in beer and wine, and the non‐alcohol industry (tea). These results can drive the construction of applied and effective marketing strategic plans for the beverage and flavor industry in the areas covered by the study.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it