| Literature DB >> 35682537 |
Melissa MacKay1, Andrea Cimino1, Samira Yousefinaghani2, Jennifer E McWhirter1, Rozita Dara2, Andrew Papadopoulos1.
Abstract
To foster trust on social media during a crisis, messages should implement key guiding principles, including call to action, clarity, conversational tone, compassion and empathy, correction of misinformation, and transparency. This study describes how crisis actors used guiding principles in COVID-19 tweets, and how the use of these guiding principles relates to tweet engagement. Original, English language tweets from 10 federal level government, politician, and public health Twitter accounts were collected between 11 March 2020 and 25 January 2021 (n = 6053). A 60% random sample was taken (n = 3633), and the tweets were analyzed for guiding principles. A tweet engagement score was calculated for each tweet and logistic regression analyses were conducted to model the relationship between guiding principles and tweet engagement. Overall, the use of guiding principles was low and inconsistent. Tweets that were written with compassion and empathy, or conversational tone were associated with greater odds of having higher tweet engagement. Across all guiding principles, tweets from politicians and public health were associated with greater odds of having higher tweet engagement. Using a combination of guiding principles was associated with greater odds of having higher tweet engagement. Crisis actors should consistently use relevant guiding principles in crisis communication messages to improve message engagement.Entities:
Keywords: COVID-19; Twitter; crisis communication; social media; social media engagement
Mesh:
Year: 2022 PMID: 35682537 PMCID: PMC9180105 DOI: 10.3390/ijerph19116954
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Key features of guiding principles for crisis communication using social media.
| Guiding Principle | Key Features | Example Tweet |
|---|---|---|
| Call to Action a |
Asks the public to do something as a result of the information [ e.g., Visit a website, share the post, watch a video, look at infographic, help others, etc. | Do your part and download it today: URL link. |
| Clarity |
Uses plain language (i.e., common terms, parallel form, short sentences) [ Conveys complex information visually [ Targets and tailors information to audience(s) [ | The Government of Canada is working hard to provide all Canadians from coast to coast to coast with access to #COVID19 vaccines. Learn more about what makes a vaccine safe. |
| Compassion and Empathy |
Validates and shows emotion [ Expresses concern and willingness to impact future tragedy [ | A few weeks ago, my six-year-old son asked me: Dad, is COVID-19 forever? It’s not. And we need to keep that in mind. Because yes, this sucks—but better days are coming. If we keep working hard and following public health guidelines, we will get through this together. |
| Conversational Tone a |
Balances friendly conversational tone with professionalism [ Uses first or second person, contractions, and implements good spelling and grammar [ | We’ve reached 5 million downloads of the #COVIDAlert app! By using the app, we can help protect ourselves, our loved ones, and our communities from #COVID19. Do your part and download it today. |
| Correction of Misinformation |
Addresses and corrects misinformation including rumors and myths [ | Federally designated quarantine sites, typically hotel rooms, are not internment camps. #Misinformation is circulating that Canada is using concentration camps for #COVID19 quarantine. This is completely false. |
| Transparency |
Provides honest and accurate information [ Shares strengths and weakness, uncertainties, and completeness of information [ Communicates future research/decisions/how they will go about finding answer [ | Today, the Government of Canada released projections on #COVID19. Our actions now can determine what our country will look like in the weeks and months to come. |
a Social media best practice. Adapted from MacKay, Colangeli, Gillis, et al., 2021.
Twitter account name, Twitter handle, number of followers as of 9 August 2021, and the number of tweets that were collected.
| Twitter Account Name | Twitter Handle | Number of Followers | Number of Tweets Collected |
|---|---|---|---|
| Government | |||
| Canada | @Canada | 980,535 | 17 |
| CIHR | @CIHR_IRSC | 61,127 | 185 |
| Finance Canada | @FinanceCanada | 87,346 | 310 |
| Politicians | |||
| CanadianPM | @CanadianPM | 543,830 | 411 |
| Chrystia Freeland | @cafreeland | 229,273 | 65 |
| Erin O’Toole | @erinotoole | 134,125 | 66 |
| Justin Trudeau | @JustinTrudeau | 5,694,063 | 453 |
| Public Health | |||
| Cdn Public Health Assoc. | @CPHA_ACSP | 11,476 | 109 |
| Dr. Theresa Tam | @CPHO_Canada | 274,920 | 3219 |
| Health Canada and PHAC | @GovCanHealth | 390,621 | 1218 |
Guiding principles that were used in tweets from government, politician, and public health Twitter accounts.
| Call to Action | Clarity | Compassion and Empathy | Conversational Tone | Correction of Misinformation | Transparency | |
|---|---|---|---|---|---|---|
|
| 299 | 28 | 35 | 95 | 0 | 27 |
| Canada | 11 | 5 | 2 | 7 | 0 | 4 |
| CIHR | 108 | 11 | 9 | 18 | 0 | 22 |
| Finance Canada | 180 | 12 | 24 | 70 | 0 | 1 |
|
| 524 | 56 | 144 | 367 | 0 | 18 |
| CanadianPM | 241 | 21 | 43 | 63 | 0 | 12 |
| Chrystia Freeland | 28 | 1 | 24 | 26 | 0 | 1 |
| Erin O’Toole | 38 | 0 | 13 | 18 | 0 | 0 |
| Justin Trudeau | 217 | 34 | 64 | 260 | 0 | 5 |
|
| 1797 | 399 | 279 | 1388 | 12 | 65 |
| Cdn Public Health Assoc. | 52 | 11 | 2 | 18 | 0 | 1 |
| Dr. Theresa Tam | 1035 | 25 | 228 | 860 | 4 | 47 |
| Health Canada and PHAC | 710 | 363 | 49 | 510 | 8 | 17 |
Results from six univariate logistic regression analyses each with a different guiding principle as the predictor variable for the level of tweet engagement.
| Odds Ratio (exp(ß)) a | Estimate (ß) | Standard Error | z Value |
| ||
|---|---|---|---|---|---|---|
| (Intercept) | 0.185 | −1.689 | 0.087 | −19.499 | <0.001 | |
| Call to Action | 0.898 | −0.107 | 0.103 | −1.041 | 0.30 | 0.511 |
| (Intercept) | 0.165 | −1.800 | 0.051 | −35.244 | <0.001 | |
| Clarity | 1.273 | 0.241 | 0.130 | 1.851 | 0.06 | 0.514 |
| (Intercept) | 0.152 | −1.886 | 0.052 | −35.940 | <0.001 | |
| Compassion and Empathy | 2.160 | 0.770 | 0.120 | 6.396 | <0.001 | 0.551 |
| (Intercept) | 0.093 | −2.380 | 0.085 | −27.980 | <0.001 | |
| Conversational Tone | 2.794 | 1.027 | 0.103 | 10.000 | <0.001 | 0.621 |
| (Intercept) | 0.171 | −1.766 | 0.047 | −37.525 | <0.001 | |
| Correction of Misinformation | 1.169 | 0.156 | 0.776 | 0.201 | 0.84 | 0.500 |
| (Intercept) | 0.172 | −1.760 | 0.048 | −36.972 | <0.001 | |
| Transparency | 0.848 | −0.165 | 0.290 | −0.569 | 0.57 | 0.502 |
a Absence of the guiding principle is the referent category for each logistic regression model.
Results from six multivariate logistic regression analyses, each with a different guiding principle and tweet source (i.e., government, politician, public health) as predictor variables for the level of tweet engagement.
| Odds Ratio | Estimate | Standard Error | z Value |
| ||
|---|---|---|---|---|---|---|
| (Intercept) | 0.064 | −2.747 | 0.359 | −7.651 | <0.001 | 0.792 |
| Call to Action | 0.454 | −0.789 | 0.127 | −6.198 | <0.001 | |
| Politicians | 35.014 | 3.556 | 0.349 | 10.204 | <0.001 | |
| Public Health | 2.025 | 0.706 | 0.351 | 2.012 | 0.04 | |
| (Intercept) | 0.028 | −3.569 | 0.339 | −10.522 | <0.001 | 0.775 |
| Clarity | 1.764 | 0.568 | 0.149 | 3.799 | <0.001 | |
| Politicians | 37.650 | 3.628 | 0.349 | 10.412 | <0.001 | |
| Public Health | 2.632 | 0.968 | 0.346 | 2.795 | 0.005 | |
| (Intercept) | 0.029 | −3.544 | 0.339 | −10.453 | <0.001 | 0.770 |
| Compassion and Empathy | 1.367 | 0.312 | 0.143 | 2.191 | 0.03 | |
| Politicians | 35.864 | 3.580 | 0.348 | 10.274 | <0.001 | |
| Public Health | 2.740 | 1.008 | 0.346 | 2.913 | 0.004 | |
| (Intercept) | 0.020 | −3.899 | 0.345 | −11.309 | <0.001 | 0.785 |
| Conversational Tone | 2.611 | 0.960 | 0.116 | 8.311 | <0.001 | |
| Politicians | 30.636 | 3.422 | 0.350 | 9.779 | <0.001 | |
| Public Health | 2.262 | 0.816 | 0.348 | 2.347 | 0.02 | |
| (Intercept) | 0.030 | −3.503 | 0.338 | −10.355 | <0.001 | 0.764 |
| Correction of Misinformation | 2.450 | 0.896 | 0.778 | 1.152 | 0.25 | |
| Politicians | 37.110 | 3.614 | 0.348 | 10.382 | <0.001 | |
| Public Health | 2.712 | 0.998 | 0.346 | 2.884 | 0.004 | |
| (Intercept) | 0.030 | −3.492 | 0.339 | −10.292 | <0.001 | 0.763 |
| Transparency | 0.876 | −0.132 | 0.334 | −0.395 | 0.69 | |
| Politicians | 36.853 | 3.607 | 0.349 | 10.351 | <0.001 | |
| Public Health | 2.706 | 0.996 | 0.347 | 2.873 | 0.004 |
a Absence of the guiding principle and government source are the referent categories.
Results from univariate logistic regression analyses with the number of guiding principles used as the predictor variable for the level of tweet engagement.
| Odds Ratio (exp(ß)) a | Estimate (ß) | Standard Error | z Value |
| ||
|---|---|---|---|---|---|---|
| (Intercept) | 0.177 | −1.729 | 0.049 | −34.940 | <0.001 | |
| Zero | 0.719 | −0.330 | 0.158 | −2.090 | 0.04 | 0.516 |
| (Intercept) | 0.217 | −1.528 | 0.056 | −27.252 | <0.001 | |
| One | 0.504 | −0.686 | 0.104 | −6.586 | <0.001 | 0.577 |
| (Intercept) | 0.153 | −1.875 | 0.059 | −31.612 | <0.001 | |
| Two | 1.373 | 0.317 | 0.097 | 3.253 | 0.001 | 0.536 |
| (Intercept) | 0.152 | −1.886 | 0.053 | −35.639 | <0.001 | |
| Three | 2.024 | 0.705 | 0.117 | 6.027 | <0.001 | 0.550 |
| (Intercept) | 0.169 | −1.780 | 0.048 | −37.433 | <0.001 | |
| Four | 2.243 | 0.808 | 0.317 | 2.546 | 0.01 | 0.507 |
a Having any number of guiding principles other than what is listed is the referent category for each logistic regression model.