Exploring Social Media Data to Understand How Stakeholders Value Local Food: A Canadian Study Using Twitter
Bibliographic record
Abstract
The consumption of local food, a major trend in industrialized countries around the world has experienced an unprecedented craze in the pandemic context that we are experiencing. Since the beginning of the crisis and in various media, communication about local food seems inconsistent. However, companies would have every interest in better communicating the multifaceted areas of the locality that customers value or adopting the same language if they wish to collaborate with each other. This research aims to identify and evaluate the “fit” or the “gap” of the different local food’ meanings of Canadian agri-food stakeholders through data mining of one of their communication media: Twitter. Using tweets by over 1300 Twitter accounts from Canadian agri-food companies and a popular hashtag, we analyze a sample of their tweets in 2019 and 2020 by creating and using a local food’ keyword dictionary based on the concept of proximity. Term frequency and multivariate analysis of variance of 16,585 tweets about local food show significant differences in dimensions of proximity used in communications. This study shows the interest of using the concept of proximity to better define and understand the valuation of local food products. In addition, it offers a methodology capable of distinguishing the nuances of meaning of the locality of products using natural data that is accessible via social media.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".