Reaction on Social Media to Online News Headlines Following the Release of Canada’s Food Guide
Bibliographic record
Abstract
This paper investigated how traditional media headlines framed the release of Canada's Food Guide (CFG) online in 2019 and how audiences reacted to its release on social media. Titles of online news articles, Facebook comments on news stories, and tweets from Twitter were collected using Meltwater and manual data collection. Leximancer software conducted conceptual extraction and relational analyses on written words and visual text. Human coding was completed to contextualize the content, which identified 9 prominent frames (food guide, impact, health, sustainable plant food, who will use?, Canadian culture, food and consumption practices, meat, and dairy). Results suggested that online news headlines highlighted CFG release and alluded to potential impacts. Analysis of Facebook comments revealed that the most commonly discussed frames were health, food and consumption behaviours, sustainable plant food, and meat, while the majority of the tweets were in direct reference to CFG being released, oftentimes with a link to another webpage, and discussed the intersect between health and food and consumption practices. In conclusion, the analysis revealed how frames emerged from social media users that shifted the discussion away from CFG release and impact to the influence of health and food and a plant versus meat debate.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".