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Record W4200478467 · doi:10.1108/ijefm-06-2021-0054

Communicating on social media during a #FestivalEmergency

2021· article· en· W4200478467 on OpenAlexaff
Christine M. Van Winkle, Shawn Corrigan

Bibliographic record

VenueInternational Journal of Event and Festival Management · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsVancouver Island UniversityUniversity of Manitoba
Fundersnot available
KeywordsSocial mediaConversationEvent (particle physics)Crisis communicationEmergency managementPublic relationsSociologyPsychologyComputer sciencePolitical scienceCommunicationWorld Wide Web

Abstract

fetched live from OpenAlex

Purpose The purpose of the study was to explore multidirectional flows of information over the course of an emergency. The following research questions were designed to guide this study: How does social media communication unfold over the course of an emergency at a cultural event? How does the nature and purpose of social media communication between all SM users change once an emergency occurs that affects event operations? How does the sentiment of social media communication change once an emergency occurs that affects event operations? Design/methodology/approach This study explored how social media was used to communicate about on-site emergencies at community cultural events. Three events were studied before, during and after an on-site emergency that disrupted the event. The Twitter and Facebook posts referencing emergencies that took place at Shambhala, Detonate and Zombicon were explored, and the nature and purpose of the posts revealed how online communication changed throughout the emergencies. The Social Mediated Crisis Communication Model guided this research and findings contribute to the model's ongoing development by incorporating additional theories and models. Findings The research demonstrates that social media communication shifts during an emergency and how communication moves through a network changes. Once an emergency is underway, communication increases and who is talking with whom changes. The nature and purpose of the social media conversation also evolves over the course of an emergency. Research limitations/implications This study examined the social media communication during three on-site emergencies at three different cultural events. The findings contribute to the understanding of the Social Media Crisis Communication Model. Specifically, the research confirms the various actors who engage online but also shows that two-way communication is not common. As this study only examined three events experiencing three different emergencies, we have a limited understanding of how the type of emergencies affects social media communication. Practical implications The findings show the need for pre-crisis work by event organizers. It is necessary for the events to build trust with their online communities to ensure that when an emergency occurs the event will be seen as a trusted source. Also, staff training is needed to ensure people are prepared to handle the complexities of communicating online during an emergency. Issues like misinformation, influencers and the rapid pace of social media communication create a challenging environment for staff who are unprepared. Originality/value Emergencies can threaten the survival of event organizations and put the health and wellness of attendees, staff and other stakeholders at risk. The study of crisis communication in special event contexts has received little theoretical attention and yet it is an important area of event management practice. Social media is an essential part of communication strategies and should be integrated into emergency planning to best reach people when an emergency threatens the safety of those involved with the event. The Social Media Crisis Communication Model offers some insight, but understanding its relevance is necessary if it is to be integrated into event emergency management.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.367
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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