Park Agency Social Media Communication During the COVID-19 Crisis
Notice bibliographique
Résumé
The COVID-19 pandemic has affected all industries and organizations, including park agencies. There is a lack of research on how park agencies utilize Twitter during times of crisis, specifically during the COVID-19 pandemic. How park agencies communicate with the public and how they use their social media has not been extensively studied. In addition, the coronavirus pandemic is a novel management issue for these agencies, and there has been no empirical analysis in the ways in which information is being communicated to the public or how that information is being perceived.\nThis study aims to better understand park agency response to COVID-19 through a literature review, to explore how social media is used as communication tool related to COVID-19 by park agencies, to use NVivo and NCapture software to assess the content of park agency tweets, and to provide recommendations for park agencies in future times of crisis to enhance communication effectiveness using social media.\nQualitative analysis methods are used, guided by grounded theory and content analysis. NVivo and NCapture software was utilized to gather 8045 tweets from 21 Canadian park agencies, individual national parks, and individual provincial parks. In addition, the United States (U.S.) National Park Service (NPS) was also examined to compare social media response from both Canadian and American national park agencies. Those tweets were then coded into three major categories: pre COVID-19, COVID-19, and non COVID-19. Coding the tweets and organizing them thematically through inductive reasoning was done within the software. Chi-squared analyses were conducted to determine if there were any statistically significant differences between the various agencies and themes found within the data.\nThe key findings were that messaging frequency regarding COVID-19 was reduced after the summer months due to peak season ending, even though the pandemic was in full swing and the number of cases was rising. There was a statistically significant difference found between the themes tweeted about by Parks Canada and the U.S. NPS in terms of frequency. Geographically, there was a statistically significant difference between themes tweeted about by various Canadian national and provincial parks, referencing the lack of standardized messaging and cohesiveness when it comes to social media content across the country. In addition, there was a lack of promotion of virtual programming from Canadian individual national and provincial parks, contrary to the U.S. NPS.\nFrom this study, it can be concluded that parks and protected area agencies should work closer with other departments, such as the health department, to potentially get the necessary messaging across. It is also recommended that capacity in parks needs to increase through virtual programming, as there have been successful cases of this already in other industries such as zoos and mental health institutes. Public access to parks and protected areas provides mental and physical health benefits, and virtual engagements can provide an alternative to this throughout the COVID-19 pandemic and future crises. Finally, parks and protected area agencies can benefit from a more standardized social media strategy, especially during a crisis, to better inform and update the public.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».