IMPACT OF THE COVID-19 PANDEMIC ON ENGAGEMENT OF THE TOURIST INFORMATION CENTRE'S FACEBOOK PAGE
Bibliographic record
Abstract
One of the biggest questions on all marketers' minds is, How do we get more Facebook engagement for our brand? Facebook engagement is any action someone takes on a Facebook page or one of someone's posts. The most common examples are likes, comments, and shares, but it can also include checking in to your location or tagging you in a post. Facebook engagement matters because it can help extend organic reach. According to Statista with over 2.7 billion monthly active users as of the second quarter of 2020, Facebook is the biggest social network worldwide. This study aims to identify the impact of the Covid-19 pandemic on engagement of the tourist information centre's Facebook page. We empirically analyse data from the official tourist Facebook account Visit Kosice using Facebook Insight for observed period March to June 2019 and 2020. The Covid-19 pandemic has affected the usage of social media by the world's general population, and we compare data before and during the Covid-19 pandemic. The results show whether or not the pandemic affects the behaviour of Facebook users.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".