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Slavik et al. BMC Public Health (2024) 24:379
https://doi.org/10.1186/s12889-024-17907-1 BMC Public Health
*Correspondence:
Catherine E. Slavik
cslavik@uoregon.edu
Full list of author information is available at the end of the article
Abstract
Background Wildre smoke contributes substantially to the global disease burden and is a major cause of air
pollution in the US states of Oregon and Washington. Climate change is expected to bring more wildres to this
region. Social media is a popular platform for health promotion and a need exists for eective communication about
smoke risks and mitigation measures to educate citizens and safeguard public health.
Methods Using a sample of 1,287 Tweets from 2022, we aimed to analyze temporal Tweeting patterns in relation to
potential smoke exposure and evaluate and compare institutions’ use of social media communication best practices
which include (i) encouraging adoption of smoke-protective actions; (ii) leveraging numeric, verbal, and Air Quality
Index risk information; and (iii) promoting community-building. Tweets were characterized using keyword searches
and the Linguistic Inquiry and Word Count (LIWC) software. Descriptive and inferential statistics were carried out.
Results 44% of Tweets in our sample were authored between January-August 2022, prior to peak wildre smoke
levels, whereas 54% of Tweets were authored during the two-month peak in smoke (September-October).
Institutional accounts used Twitter (or X) to encourage the adoption of smoke-related protective actions (82% of
Tweets), more than they used it to disseminate wildre smoke risk information (25%) or promote community-building
(47%). Only 10% of Tweets discussed populations vulnerable to wildre smoke health eects, and 14% mentioned
smoke mitigation measures. Tweets from Washington-based accounts used signicantly more verbal and numeric
risk information to discuss wildre smoke than Oregon-based accounts (p = 0.042 and p = 0.003, respectively);
however, Tweets from Oregon-based accounts on average contained a higher percentage of words associated with
community-building language (p < 0.001).
Conclusions This research provides practical recommendations for public health practitioners and researchers
communicating wildre smoke risks on social media. As exposures to wildre smoke rise due to climate change,
reducing the environmental disease burden requires health ocials to leverage popular communication platforms,
distribute necessary health-related messaging rapidly, and get the message right. Timely, evidence-based, and theory-
driven messaging is critical for educating and empowering individuals to make informed decisions about protecting
themselves from harmful exposures. Thus, proactive and sustained communications about wildre smoke should be
prioritized even during wildre “o-seasons.”
Clearing the air: evaluating institutions’
social media health messaging on wildre
and smoke risks in the US Pacic Northwest
Catherine E.Slavik1,2*, Daniel A.Chapman1,2, Alex SegrèCohen1,2, NahlaBendefaa1 and EllenPeters1,2,3
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Page 2 of 16
Slavik et al. BMC Public Health (2024) 24:379
Background
Wildre smoke contributes substantially to the global
disease burden, and it is getting worse with climate
change [1, 2]. e Pacic Northwest region of the United
States (US) has seen an especially large increase in the
quantity and scope of wildres, and populations there
already face increased risks of health harms attribut-
able to smoke exposure [3]. e region’s poor air quality
due to wildre smoke is a signicant and growing public
health challenge [4].
Wildre smoke contains several compounds hazardous
to health, such as Fine Particulate Matter (PM2.5), car-
bon monoxide, nitrogen oxides, methane, trace metals,
and carcinogens like formaldehyde, polycycle aromatic
hydrocarbons, and acrolein [5]. Although adverse health
impacts associated with wildre smoke can aect all pop-
ulations, sensitive groups like children, pregnant people,
and those with existing respiratory and cardiovascular
conditions are especially at risk [6].
To prevent diseases attributable to wildre smoke, pub-
lic education and risk communication about smoke risks
and exposure mitigation measures are needed [7]. Smoke
education and communication eorts require a variety of
actors, each responsible for communicating specic types
of information to particular audiences [8]. Smoke travels
across geographic boundaries following wind patterns,
and thus, can impact air quality in places far from the
original source [9]. As a result, communications emerge
from local, regional, and national sources. In addition,
since air quality risks are both environmental and public
health issues, government agencies and communicators
from both domains tend to undertake risk communica-
tion activities around wildre smoke [10].
During wildre events, individuals have displayed a
keen sense of place and seek region-specic updates con-
cerning re impact and wildre smoke [11, 12]. While
conveying health risks across diverse geographic areas
presents obstacles, populations gain from messages that
highlight the unique conditions of an ongoing emergency,
and from information that feels genuine and are useful to
them [13]. Some evidence also suggests that the public
trusts local sources of wildre smoke information more
than state-level or federal-level sources [14]. Despite
large demand for hyper-local wildre risk information in
the US, eective communications that promote risk com-
prehension and awareness of exposure mitigation mea-
sures are lacking [15].
Twitter (now “X”) is a popular social media platform
used by government agencies and ocials to dissemi-
nate information, providing citizens with a direct link
to those leading the response to environmental hazards
and public health emergencies [16–18]. In the US, most
federal government ocials have had a Twitter account,
and some sources indicate that about a quarter of the
public used Twitter as recently as 2021 [19, 20]. Twitter
is a common tool for health promotion and risk com-
munication [21, 22], and in some areas of the US Pacic
Northwest—a region that includes the states of Washing-
ton and Oregon—the platform has served as a popular
means for citizens to express wildre smoke concerns,
seek updates on risks, and learn about intervention strat-
egies [23, 24]. In these states, the percentage of adults
with a Twitter account was estimated to be 27% in 2021
[25]. Van Deventer et al. found that the majority of gov-
ernment-authored communications about wildre smoke
in this region were disseminated via Twitter and other
social media platforms [10]. An active social media pres-
ence is increasingly viewed as important for ocials to
communicate smoke risks to communities proactively
[26]; however, in practice, advice from governments on
smoke-protective actions is often reactive and/or comes
too late after a smoke event [10, 27, 28]. e US Environ-
mental Protection Agency (EPA) recommends that o-
cials communicate smoke risks and instruct households
on what preparations to make during wildre o-seasons
[29].
Prior research studying institutional health messaging
on Twitter and communication best practices found that
Tweets authored by public health organizations generally
served at least one of three message functions [30, 31].
ese message functions, originally conceptualized by
Lovejoy and Saxton, can be classied based on whether
Tweets: (i) encourage members of the public to adopt an
action or behavior (“action”); (ii) provide information to
the public (“information”); or (iii) promote community-
building, give recognition and thanks to community
members, or otherwise signal community engagement
(“community”) [32].
Although limited studies have tied social media com-
munications like Tweets directly to healthy behavior
change [33, 34], governments increasingly consider their
communications to be key for motivating the public to
take individual actions that reduce exposure to wildre
smoke [35]. One way to encourage action-taking may be
through application of constructs from Protection Moti-
vation eory, which is a model of disease prevention in
social psychology and health promotion. e theory pos-
its that people’s intentions to protect themselves from
harm are inuenced by four cognitions: risk severity, like-
lihood of experiencing harm, eectiveness of mitigative
Keywords Wildre smoke, Public health, Risk communication, Social media, Protection motivation theory,
Environmental health
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Page 3 of 16
Slavik et al. BMC Public Health (2024) 24:379
measures to protect from harm, and the belief, known as
self-ecacy, that one can successfully execute these mea-
sures [36]. Health agencies and ocials have employed
messaging based on these Protection Motivation eory
cognitions in public education campaigns [37–39], and
their use has been shown to increase people’s intentions
to take action to avoid harm from a variety of environ-
mental health hazards [40–42]. Although the application
of Protection Motivation eory constructs in pub-
lic health messaging on Twitter has not been studied
directly, previous studies analyzing communications
authored by public health agencies have found that
“action” Tweets, which may bolster self-ecacy beliefs,
and Tweets specically referencing hazard severity, gar-
ner higher engagement from users [17, 43].
Institutional health communications that primarily
serve to inform users about a hazard also have an impor-
tant function on Twitter [44], particularly during wildre
events [45]. Best practices in health risk communication
point to using numeric information to promote accurate
perceptions of risks, as people seem to prefer receiving
risk information that contains numbers (especially the
highly numerate) [46–48], or that contain numbers in
combination with verbal labels [49, 50]. ey also nd
messages with numbers more useful. In one study, partic-
ipants found websites with numeric information clearer
and more useful than a site without it, and they were
also more motivated to use the information [51]. is
preference for numbers may be partly attributed to the
imprecision of risk information when expressed verbally,
for example, words like “signicant” may lead to varying
interpretations among individuals [52].
Additionally, numerical risk information may increase
intentions to act to reduce environmental risks [53], as
it increases other health-protective intentions [54, 55].
Health risks attributable to poor air quality are gener-
ally communicated by referencing a specic hazard cat-
egory from the US Environmental Protection Agency’s
Air Quality Index (AQI), which helps the public evalu-
ate how hazardous the air is on a scale from 0 (good) to
500+ (hazardous) [56]. Importantly, using interpretative
labels to denote dierent categories of risk on a scale has
been found to aid people’s health decision making and
their interpretation of numeric information [57]. Some
research also suggests health-based AQI risk labels like
those used in the US (e.g., unhealthy, hazardous, etc.) are
more eective at motivating protective action intentions
than non-health-based air quality risk labels (e.g., poor,
polluted, etc.) [58]. However, to our knowledge, only one
previous study has explored how institutions or ocials
inform the public about air quality health risks on Twitter
during wildre smoke events [10].
Institutional use of social media like Twitter cen-
ters around engaging users and building (virtual)
communities. It is generally viewed as an important
way to promote public participation in health promo-
tion and decision making [59]. Some evidence suggests
that encountering Tweets about community-building
or social practices like care and compassion can inu-
ence the adoption of prosocial behaviors [60]. Further,
communications encouraging dialogue between com-
munity members and public engagement may increase
trust and other favorable perceptions of institutions and
government organizations [61], which have been found
to increase people’s intentions to adhere to public health
recommendations during events like natural disasters
[62]. Despite this, health agencies appear to author fewer
Tweets that focus on “community”, relative to the “action”
and “information” functions [30, 31].
e literature reviewed above suggests that extensive
research points towards how to craft eective social
media messages about a variety of health risks. However,
our understanding of the actual communication practices
of governments—especially as they pertain to wildre
smoke—remains limited. is study aimed to: [1] ana-
lyze temporal Tweeting patterns in relation to potential
wildre smoke exposure in Washington and Oregon, and;
[2] evaluate and compare institutions’ use of three best
practices for communicating on social media. Based on
our ndings, we generated practical recommendations
for public health practitioners and researchers communi-
cating about wildre smoke risks on social media. Each
Tweet in our dataset was coded for language that could
encourage action-taking, its use of risk information, and
for language that could promote community-building.
e research questions we sought to answer were:
1. Do temporal Tweeting patterns about wildre and
smoke align with daily average AQI values?
2. Do institutional Tweets about wildres and smoke
apply social media communication best practices,
which:
a) Encourage the adoption of protective actions for
reducing smoke exposure based on predictions
from Protection Motivation eory?
b) Inform users about the health risks associated
with smoke exposure by leveraging verbal cues,
numeric information, and AQI risk labels?
c) Promote community-building through references
to social interactions and/or social behaviors?
3. How does the use of health messaging across these
dimensions compare by institutional characteristics
including type, regional scale, and location?
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Slavik et al. BMC Public Health (2024) 24:379
Methods
Tweet retrieval
An initial scoping review of online information sources
identied key government institutions and agencies
serving as ocial disseminators of air quality informa-
tion to citizens residing in Oregon and Washington.
Relevant institutions included national and state-level
environmental and health agencies, as well as local health
departments. Using the Twitter interface to manually
search the names of these government organizations, 34
Twitter accounts were identied from which to draw the
sample of Tweets from (See Supplemental Table 1). is
study did not include Tweets from organizations related
to wildre emergency response, such as local re depart-
ments, as these accounts tended to disseminate informa-
tion about re spread and evacuation notices as opposed
to health-related information about wildres and air
quality. Of note, Twitter was renamed “X” in July 2023
several months after data collection for this study had
taken place.
Twitter data was downloaded in January 2023 from the
34 Twitter accounts selected for this study using a Twit-
ter Application Programming Interface (API) accessed
through R using the ‘rtweet’ package [63]. An R script
was developed to download the maximum number of
Tweets from each account (i.e., the most recent 3,200
Tweets) permitted for account-specic searches required
by Twitter’s API. e download yielded a dataset of
85,406 Tweets authored by the 34 accounts of inter-
est published between April 2009 and January 2023. We
limited our analysis to include only Tweets authored
between January 1st, 2022, and December 31st, 2022, as
this represented the most complete dataset; all but two
Twitter accounts (out of 34) ‘Tweeted’ during this time,
resulting in 24,430 Tweets authored by 32 accounts.
We further restricted our analysis to Tweets about
wildres and smoke, based on whether they contained
the keywords “smoke” or “re”, and excluded any Tweets
containing the words “tobacco”, “cigarette” or “second-
hand”. Tweets that did not contain any text (e.g., con-
tained an image only), would have also been screened
out at this stage. is step resulted in a sample of 1,879
Tweets. Next, two coders worked together to screen out
any Tweets not about wildres or smoke that remained
in the dataset. e two coders engaged in discussions,
deliberating on any coding discrepancies between indi-
vidual Tweets until they reached a consensus. Tweets
screened out at this stage included, for example, those
discussing air pollution from household replaces or
wood stoves, or air pollution due to reworks (N = 593).
Wood-heating in homes is common across many regions
of the US Pacic Northwest during the winter months
and can account for the majority of smoke produced
(and the PM2.5 recorded) during that season [64]. Tweets
that referenced air pollution due to forest management
res (e.g., prescribed forest burns) were retained in our
analysis, yielding a nal dataset—authored by 30 dierent
accounts—of 1,287 Tweets about wildres and smoke.
Twitter account classication
e 30 Twitter accounts that authored the Tweets in our
dataset were classied based on three categorical vari-
ables. First, an account’s location was either classied
as Washington (WA) (N = 20 accounts, N = 830 Tweets),
Oregon (OR) (N = 7 accounts, N = 323 Tweets) or USA-
based (N = 3 accounts, N = 134 Tweets) based on whether
the institution was located in one of the two states or
belonged to an account attached to a national agency
(e.g., the US EPA’s Oce of Air and Radiation), respec-
tively. Second, an account was classied by its regional
scale as either local (N = 21 accounts, N = 384 Tweets) or
as a state-level or national-level account (N = 9 accounts,
N = 903 Tweets) based on whether the account served
citizens from a county, or citizens across an entire state
(or multiple states), respectively. For example, Twitter
accounts classied as local would have included local
public health departments (e.g., Seattle and King Coun-
ty’s @KCPubHealth account), while Twitter accounts
classied as regional would have included both state
agencies (e.g., Oregon’s Department of Environmental
Quality @OregonDEQ) and national agencies (e.g., US
EPA’s AirNow Program @AIRNow). Lastly, accounts
were classied as either environmental (N = 10 accounts,
N = 882 Tweets) or health (N = 20 accounts, N = 405
Tweets) based on their institutional mandate.
AQI data retrieval and analysis
is study used daily AQI values for PM2.5 to indicate
potential wildre smoke exposure. Exposure to PM2.5
from wildre smoke has been associated with increased
incidences of all-cause mortality and respiratory mor-
bidity, including exacerbations to asthma and COPD,
pneumonia and bronchitis [65]. Further, Burke et al. pre-
viously found that wildre smoke caused more than 75%
of exceedances in daily PM2.5 concentrations in Wash-
ington and Oregon between 2020 and 2022 [66]. us,
PM2.5 is frequently used as an indicator of wildre smoke
exposure. Daily air quality summary statistics for PM2.5
in Oregon and Washington states were downloaded from
the US EPA for the year 2022 to examine temporal trends
in both states’ levels of wildre smoke [67]. e data is
based on the EPA’s Air Quality System, which leverages
air quality measurements for various criteria pollutants
from multiple state monitoring sites and undergoes val-
idation. Daily maximum AQI values (ranging from 0 to
500 based on the US AQI) were aggregated by state and
by month.
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Slavik et al. BMC Public Health (2024) 24:379
Protection motivation theory scoring and analysis of
tweets
We searched for keywords associated with each of the
four dimensions of Protection Motivation eory [36] to
devise a total score out of 7 for each Tweet (see Table1).
e rst dimension, “severity”, was assessed using three
sub-dimensions. For the rst sub-dimension, “conse-
quences”, a Tweet would receive a score of 1 if it men-
tioned any health consequence or health eect associated
with wildre smoke exposure (e.g., asthma, coughing,
etc.). For the second sub-dimension, “threat”, a Tweet
would receive a score of 1 if it mentioned words associ-
ated with wildre smoke threats to health (e.g., PM2.5,
pollution, etc.). For the third sub-dimension, “magni-
tude”, a Tweet would receive a score of 1 if it mentioned
how severe the threat to health was (e.g., severe, seri-
ous, etc.). us, a Tweet could receive a maximum score
of 3 for the “severity” Protection Motivation eory
dimension if it used messaging satisfying all three of the
sub-dimensions.
e second Protection Motivation eory dimension,
“likelihood”, was assessed using two sub-dimensions.
For the rst sub-dimension, “probability”, a Tweet would
receive a score of 1 if it mentioned how likely the threat
to health was (e.g., uncertain, predicted, etc.). For the
second sub-dimension, “vulnerability”, a Tweet would
receive a score of 1 if it mentioned any vulnerable groups
more likely to experience health consequences from
smoke exposure (e.g., children, pregnant people, etc.).
us, a Tweet could receive a maximum score of 2 for the
“likelihood” Protection Motivation eory dimension if it
used messaging satisfying both of sub-dimensions.
For the third Protection Motivation eory dimen-
sion, “mitigation”, a Tweet would receive a score of 1 if it
mentioned any mitigation measures from smoke expo-
sure (e.g., air purier, staying indoors, etc.). Lastly, for
the fourth Protection Motivation eory dimension,
“self-ecacy”, a keyword search for the terms “protect”,
“safe” and “can” was carried out; Tweets containing one
or more of these words were read to examine the context
surrounding the use of the words and whether they were
used to describe people’s ability to protect themselves,
stay safe or execute some specic action (e.g., “Learn how
you can protect your health from wildre smoke…”). e
“self-ecacy” dimension of the Protection Motivation
eory score was also scored as a binary variable out of 1,
denoting a presence or absence of self-ecacy language.
Protection Motivation eory scores were created for
each Tweet by tallying up the presence of each Protection
Motivation eory dimension/sub-dimension. We fol-
lowed a similar approach to one adopted by Zhang et al.,
who also linked keywords from Tweets to specic con-
structs of a health behavior theory in order to examine
the usage of these constructs in health-related Twitter
discussions [68]. In this study, each Tweet obtained a
Protection Motivation eory score ranging from 0 (no
Protection Motivation eory dimensions present) to 7
(all Protection Motivation eory dimensions and sub-
dimensions present).
Risk information scoring and analysis of tweets
In addition to Protection Motivation eory scores,
Tweets were also coded based on the presence or absence
of dierent types of risk information and specically
whether: (i) verbal cues were used; (ii) numeric infor-
mation was used; and/or (iii) AQI risk labels were refer-
enced. Table1 highlights which words were used to create
a verbal risk information score, where the presence of any
single word would lead to a score of 1 for that category. A
Tweet received a score of 1 in the numeric category if it
contained a number (i.e., any Arabic integer) in reference
to a risk quantity relevant to wildres and/or smoke, for
example, describing a percent likelihood of re spread or
the number of acres burning. Finally, a Tweet received a
score of 1 for the AQI risk label category if it referenced
any one of the six AQI hazard categories commonly used
to quantify risks from poor air quality in the US.
Assessment and analysis of tweets about community-
building
Further, this study used a dictionary-based approach with
Linguistic Inquiry and Word Count software (LIWC-22)
to assess linguistic dierences in word use about com-
munity-building across the Twitter accounts studied.
Boyd et al. provides an overview of the development of
the LIWC dictionary and the reliability and validity of
the various dimensions the software generates [69]. In
essence, each LIWC dimension is composed of a select
list of dictionary words that have been found to capture
its meaning based on an extensive text corpus of approxi-
mately 31million words from various sources (including
thousands of Tweets). We selected two dimensions from
LIWC-22 relevant to community-building, and ran all
Tweets in our sample through the software to give each
Tweet two LIWC scores, representing percentages of
total words within a text for two dimensions of interest:
“social behavior” and “prosocial behavior”. Prior research
has found that the social dimensions LIWC uses are a
reliable indicator of social connections and closeness
[70]. e “social behavior” dimension captures words
associated with social interactions and includes terms
like “said” and “share”; e “prosocial behavior” dimen-
sion captures a subset of words from the “social behav-
ior” category, but specically targets terms associated
with social behaviors that benet society or promote car-
ing about others, for example, “care” and “thank” [69].
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Slavik et al. BMC Public Health (2024) 24:379
Statistical analysis
Descriptive statistics were performed for the Protection
Motivation eory scores and the individual dimensions
used to describe the presence/absence of these scores
across dierent groupings of interest (i.e., by institution
type [environmental vs. health], regional scale [local vs.
state-level or national-level], and location [OR vs. WA
vs. USA]). Descriptive statistics were also performed to
describe the presence/absence of dierent risk language
in the Tweets, as well as dierences in the LIWC dimen-
sions, across institution type, regional scale and location.
Models were constructed to test dierences between
institution types, regional scales, and locations on the
outcomes of interest including the Protection Motiva-
tion eory score, risk language, and LIWC score. For
the Protection Motivation eory and LIWC scores, pre-
liminary analyses revealed potential violations of homo-
geneity of variance and normality of residuals. erefore,
non-parametric statistical tests were employed for the
Protection Motivation eory and LIWC models. To
test for dierences between institution type and regional
scale, Mann-Whitney Wilcoxon tests—the non-para-
metric equivalent of independent samples t-tests—were
used. e results of these models were accompanied by
Vargha and Delaney’s ‘A’ as an eect size measure, for
which benchmarks recommended by the developers were
provided [71].
For dierences across the three locations, the Krus-
kal-Wallis test (i.e., non-parametric one-way analysis
of variance) was used, accompanied by post-hoc tests
to compare each location using Dunn’s method and the
Benjamini-Hochberg p-value adjustment for multiple
comparisons [72, 73]. Epsilon-squared (E2) was reported
as an eect size measure for the Kruskal-Wallis test. As
the outcomes were binary for the models of risk lan-
guage, logistic regression models were employed. Stan-
dard logistic regression results with odds ratios as an
indicator of eect size were reported. For the model of
location, post-hoc pairwise comparison results were
reported based on the marginal means, again using the
Benjamini-Hochberg p-value adjustment for multiple
comparisons.
Results
Temporal patterns in institutional tweeting and potential
smoke exposure
is study examined whether temporal trends in insti-
tutional Tweeting about wildre smoke corresponded to
changes in daily AQI values (i.e., an indicator for poten-
tial population exposure to wildre smoke). Results in
Fig.1 show that values for maximum daily AQI (averaged
by month) were highest in September and October 2022
and AQI levels were generally higher in Oregon than in
Washington state. Across all accounts, 44% of all Tweets
in our sample were authored prior to that year’s wild-
re smoke season during the period January to August
2022, while 54% of all Tweets were authored during
the two-month peak in smoke levels (during the period
September-October 2022) (data not displayed). Both in
Washington and also in Oregon, each Twitter account
Table 1 Keyword search strategies for Protection Motivation Theory and risk language scoring by dimension/sub-dimension
Variables for keyword search Keywords
Protection Motivation Theory dimensions Sub-dimensions (if
present)
Severity Consequences Asthma; breath*; cardiovascular; chest pain; condition; cough;
damag*; disease; eect; eye; headache; health problem; heart;
illness; lung; respirat*; throat; wheez*
Threat Air quality; AQI; degraded; hazard; partic*; PM; pollut*; risk; threat
Magnitude Danger*; extreme; harm*; serious; severe; unhealthy
Likelihood Probability Canǂ; chance; could; expect; forecast; frequent; likel*; may;
might; possib*; potential; predict; probab*; uncertain; will
Vulnerability Asthma; cardiovascular; child; elderly; kid; old; pregnant; respira-
tory; sensitive; worker
Mitigation NA HEPA; indoor; inside; mask; MERV; monitor; outdoor; outside;
purier; respirator; sensors; shelter; space; window
Self-ecacy NA Canǂ; protect; safe
Risk information dimensions
Verbal cues NA Big; decline; decrease; elevated; fewer; heavy; high; increase;
large; less; low; many; more; most; reduce; rise; rising; small; tiny
Numeric information NA [number]; %; percent*
Air Quality Index Risk Labels NA Good; hazardous; moderate; unhealthy†
Note: *Wild card search queries wer e carried out on cert ain words to capture groups of s imilar words with diere nt endings
ǂTwo possible uses of the word ‘can’ existed in this search and we re treated as dierent key words, rst ‘can’ as a verb for possibility (i.e., can be sm oky), and second
‘can’ as a verb for ab ility (i.e., you can take t his action to protect yo urself)
†The word ‘unhea lthy’ appears in three o f the six Air Quality Ind ex risk labels: Unhealthy f or sensitive groups, Unh ealthy, Very unh ealthy
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Slavik et al. BMC Public Health (2024) 24:379
authored on average 18 wildre-smoke Tweets during the
period prior to peak smoke levels, which corresponded to
44% and 39% of each state’s annual total Tweets, respec-
tively. Each USA-based account authored on average 24
wildre-smoke Tweets during the period of time preced-
ing peak wildre smoke levels (54% of their annual total
Tweets). During the two-month peak period of wildre
smoke, accounts in Washington each authored on aver-
age 22 Tweets, while accounts in Oregon appeared to
take a more responsive approach and authored an aver-
age of 27 Tweets each (54% vs. 58% of their annual total
Tweets, respectively). Accounts across the USA each
authored an average of 20 Tweets during the peak wild-
re smoke period (44% of their annual total Tweets).
Use of tweets promoting adoption of protective actions
from wildre-smoke exposure
is study explored whether institutional Tweets about
wildre and smoke encouraged the adoption of protec-
tive actions to mitigate smoke exposure by applying
constructs from Protection Motivation eory. Results
indicate that no Tweet authored by the accounts in the
dataset contained all seven Protection Motivation e-
ory dimensions/sub-dimensions studied (data not dis-
played). However, most Tweets contained messaging
satisfying at least one of the seven Protection Motivation
eory dimensions (N = 1054, 82%). Only 15 Tweets used
messaging consistent with each of the four Protection
Motivation eory cognitions (i.e., severity, likelihood,
mitigation, self-ecacy) by satisfying at least one of three
sub-dimensions for “severity”, one of two sub-dimensions
for “likelihood”, as well as the “mitigation” and “self-e-
cacy” dimensions.
Table2 summarizes the use of Protection Motivation
eory dimensions in the sample of Tweets studied. e
most frequently used “severity” sub-dimension in Tweets
Table 2 Frequency of use of Protection Motivation Theory
health messaging in Tweet sample (N = 1,287 Tweets).
Percentages add up to more than 100% because a Tweet could
be represented in each dimension and sub-dimension
Protection Mo-
tivation Theory
dimension
Sub-dimension (if
present)
Frequency % of
total
Tweets
Severity Consequences 87 7
Threat 615 48
Magnitude 220 17
Likelihood Probability 595 46
Vulnerability 123 10
Mitigation NA 178 14
Self-ecacy NA 305 24
Fig. 1 Summed frequencies of wildre and smoke Tweets authored by institutional accounts (left) and maximum daily AQI values (for PM2.5) (right) in
2022 aggregated by account location and month. Data sources: Twitter and US EPA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 8 of 16
Slavik et al. BMC Public Health (2024) 24:379
was “threat” (N = 615, 48%), followed by “magnitude”
(N = 220, 17%). Only 2% of Tweets (N = 26) used all three
sub-dimensions of the “severity” dimension and 56% of
Tweets contained at least one “severity” sub-dimension
(N = 716) (data not displayed).
e most frequently used “likelihood” sub-dimension
in Tweets was “probability” (N = 595, 46%) (Table2). 10%
of Tweets analyzed contained information about vulnera-
ble populations (N = 123); e most frequently mentioned
terms pertaining to the “vulnerability” sub-dimension
were “sensitive” (N = 77), “asthma” (N = 27), “child” or
“kids” (N = 30), and “elderly” or “old” (N = 25). Less fre-
quently mentioned terms for the “vulnerability” sub-
dimension were “pregnant” (N = 9), “respiratory” (N = 8),
“worker” (N = 1) and “cardiovascular” (N = 1). Only 7% of
Tweets (N = 88) used both “likelihood” sub-dimensions in
their health messaging (data not displayed).
14% of Tweets used messaging to satisfy the “mitiga-
tion” Protection Motivation eory dimension and dis-
cussed measures to mitigate exposure to wildre smoke
(N = 178) (Table2). Terms pertaining to staying indoors
(“indoor” N = 61, “inside” N = 14, “shelter” N = 13) or lim-
iting outdoor time (“outside” N = 34, “outdoor” N = 23)
were most frequently mentioned. e term “monitor”
was also used (N = 46). Other mitigation measures refer-
enced in the Tweets included the terms “mask” (N = 20),
“window” (N = 18), “HEPA” or “purier” (N = 12). Nearly
a quarter of Tweets used health messaging that satised
the “self-ecacy” Protection Motivation eory dimen-
sion, which referred to Twitter users’ capacity to execute
a smoke-protective behavior (N = 305, 24%).
is study also examined how the application of Pro-
tection Motivation eory in Tweets varied by institu-
tional characteristics. No signicant dierences emerged
between institutional use of Protection Motivation e-
ory dimensions across environmental vs. health insti-
tutions (W = 177,206, p = 0.815, Vargha and Delaney
A = 0.50) (Table 3). However, a signicant dierence
existed between regional scales; local accounts that serve
county-level populations used Protection Motivation
eory health messaging more frequently in their Tweets
compared to state- or national-level accounts that serve
larger populations (W = 200,098, p < 0.001, Vargha and
Delaney A = 0.58).
Tweets authored by Twitter accounts located in Ore-
gon, Washington, and across the USA signicantly
diered in their Protection Motivation eory scores (X2
(2, N = 1287) = 17.17, p < 0.001, E2 = 0.013) (Table4). Post-
hoc comparisons revealed that accounts located in Wash-
ington used health messaging with content aligned with
Protection Motivation eory more frequently in Tweets
compared to accounts located in Oregon (p < 0.001) and
accounts across the USA (p = 0.053), which did not dier
from each other (p = 0.552).
Use of tweets containing risk information
We further investigated whether institutional Tweets
about wildre and smoke informed people about the
health risks associated with smoke exposure through use
of verbal risk cues, numeric risk information and AQI
risk labels. ree hundred and sixteen Tweets (25% of
total sample) included risk information containing some
verbal cues in their health messaging (e.g., “Central WA
has experienced fewer smoky days than normal…”). e
term “large” (N = 22) was the most frequently used ver-
bal cue for risk information; it quantied the size of res
contributing to wildre smoke. Just sixty-four Tweets
(5%) contained numeric risk language (e.g., “An AIR
QUALITY ALERT is in place for another 48 hours…”).
Two hundred and thirteen Tweets (17%) explicitly used
risk language referring to one or more of the AQI risk
labels (e.g., “Air quality has reached the level of unhealthy
for sensitive groups across most of #Seattle. Smoke is com-
ing from #BoltCreekFire near Skykomish”).
is study also explored how accounts’ use of the three
types of risk information varied by institutional charac-
teristics. ere was no signicant dierence between
environmental and health institutions and their use
of verbal risk language (OR = 1.13, p = 0.360) (Table 5).
Table 3 Mean Protection Motivation Theory scores by institution
type and regional scale
Protection Moti-
vation Theory
score (standard
error)
Vargha and
Delaney’s A
(eect size)
Wp
Institution
type
Environmental 1.24 (0.04) 0.50
(negligible)
177,206 0.815
Health 1.30 (0.06)
Regional scale
State or national 1.53 (0.04) 0.58 (small) 200,098 < 0.001
Local 1.92 (0.07)
Table 4 Mean Protection Motivation Theory scores by location with post hoc pairwise comparisons
Protection Motivation Theory score (standard error) Ε2χ2 pDunn’s post hoc
USA OR WA
1.51 (0.10) 1.46 (0.07) 1.75 (0.04) 0.013 17.17 < 0.001 USA vs. OR
USA vs. WA*
WA vs. OR***
Note: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.0 01
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 9 of 16
Slavik et al. BMC Public Health (2024) 24:379
ere was also no signicant dierence between environ-
mental and health institutions’ use of numeric risk lan-
guage, (OR = 1.07, p = 0.812) nor in their use of AQI risk
labels, (OR = 0.78, p = 0.145). Local accounts did not dif-
fer in their use of verbal risk language when compared
to accounts at the state- or national-level (OR = 0.84,
p = 0.218) (Table 5). However, accounts at a state- or
national-level scale were more likely than local accounts
to provide numeric risk information (OR = 1.89, p = 0.05)
and less likely to provide AQI risk labels (OR = 0.55,
p < 0.001).
ere was signicant variance predicted by the loca-
tions of accounts’ Tweets and their use of verbal risk
language (X2 (2, N = 1287) = 10.775, p = 0.005). In terms
of predicted probabilities, accounts in Washington had
a 27.3% probability of using verbal risk language, com-
pared to a 20.4% probability in Oregon accounts and
a probability of 17.2% in USA-based accounts (Fig. 2).
Pairwise comparisons revealed that Tweets from Wash-
ington-based accounts used signicantly more verbal
risk language than both Oregon (p = 0.024) and USA-
based accounts (p = 0.024), but Tweets from Oregon
and USA-based accounts did not dier signicantly
(p = 0.422).
For numeric risk language, none of the USA-based
accounts included numeric risk information. erefore,
this model was simplied to compare only Oregon and
Washington. ere was a signicant eect of location
such that a greater percentage of Tweets authored by
Washington-based accounts included numeric risk lan-
guage (6.9%) than Tweets authored by Oregon-based
accounts (2.2%), (OR = 3.33, p = 0.003) (Fig .2).
All three locations (OR, WA, USA) were included in the
AQI risk information model. ere was signicant vari-
ance predicted by the dierent locations for references
to AQI risk labels, (X2 (2, N = 1287) = 80.27, p < 0.001).
Tweets authored by Washington-based accounts had a
22.9% probability of referencing AQI risk labels, accounts
in Oregon had a 5.6% probability and the USA accounts
had a probability of 3.7% (Fig.2). Pairwise comparisons
revealed that Tweets from Washington-based accounts
contained signicantly more AQI risk labels than both
Oregon-based accounts (p < 0.001) and USA accounts
Table 5 Counts and likelihood of Tweets containing the three types of risk information studied (verbal cues, numeric information, and
AQI risk labels) by institution type and regional scale
Verbal cues (e.g.,
“large”)
Odds ratio
(95% C.I.)
Numeric informa-
tion (e.g., “[#]”)
Odds ratio
(95% C.I.)
AQI risk labels (e.g.,
“unhealthy”)
Odds
ratio
(95% C.I.)
Institution type Yes None Yes None Yes None
Environmental 210 627 1.13
(0.86, 1.48)
43 839 1.07
(0.61, 1.80)
155 727 0.78
(0.56, 1.08)
Health (ref) 106 299 21 384 58 347
Regional scale Yes None Yes None Ye s None
State or national 213 690 0.84
(0.64, 1.11)
52 851 1.89
(1.03, 3.76)
126 777 0.55
(0.41, 0.75)
Local (ref) 103 281 12 372 87 297
Fig. 2 Probability of Tweets containing each of the three types of risk information studied (verbal cues, numeric information, and AQI risk labels) by loca-
tion. Error bars reect 95% condence intervals
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Slavik et al. BMC Public Health (2024) 24:379
(p < 0.001); Tweets from Oregon and USA accounts did
not dier (p = 0.415).
Use of tweets promoting community-building
is research explored institutions’ use of messaging in
Tweets to promote community-building through refer-
ences to social interactions and social behaviors. Across
our sample of Tweets, 1.70% of the words in each Tweet,
on average, were words associated with social behaviors
(data not displayed). e Tweet with the highest propor-
tion of social behavior words was authored by a local
health department located in Washington state, in which
15.38% of the Tweet constituted social process words.
Across the whole sample, on average, fewer than 1% of
words per Tweet constituted words related to the pro-
social behavior dimension (N = 0.52%). Out of the 1,287
total Tweets, 611 contained at least one social behavior
word (47%), and 236 contained at least one prosocial
behavior word (18%).
is study also examined how the use of Tweets pro-
moting community-building varied by institutional
characteristics. e LIWC analysis indicated that the
use of social behavioral words diered signicantly
between environmental and health institutions (p = 0.024)
(Table 6); words associated with this dimension were
used more frequently by health institutions compared to
environmental institutions. However, the use of words
associated specically with prosocial behaviors (p = 0.464)
did not signicantly dier across institution types. When
examining community language use by regional scale, no
signicant dierences were found between local versus
state- or national-level institutions’ use of social behav-
ioral (p = 0.443) or prosocial (p = 0.574) words.
As summarized in Table 7, use of words related to
social behaviors signicantly diered by location (X2 (2,
N = 1287) = 91.26, p < 0.001) (Table7). Institutional Twit-
ter accounts based in Oregon, on average, authored
Tweets with higher percentages of words associated with
social behaviors (2.47%) relative to Tweets authored by
accounts located across the USA (2.17%, p = 0.043) and
accounts based in Washington state (1.33%, p < 0.001).
Accounts based in Washington authored Tweets contain-
ing a signicantly smaller proportion of words associated
with social behaviors compared to USA-based Twitter
accounts (p < 0.001). e use of words associated with
prosocial behaviors did not dier signicantly between
account locations (X2 (2, N = 1287) = 0.32, p = 0.851) and
none of the pairwise comparisons were signicant.
Discussion
Wildre smoke is and likely will continue to be a major
cause of air pollution in Oregon and Washington [4, 66].
In these regions, environmental and public health agen-
cies are advised to communicate to the public about air
quality issues, provide advice on strategies to limit expo-
sure to wildre smoke, and generally view public educa-
tion as an important part of their institutional mandate
[14, 29, 74]. is research evaluated wildre and smoke
communications authored by Oregon, Washington and
USA-based accounts on the social media platform Twit-
ter and examined how health messaging varied by insti-
tutions. First, we explored temporal Tweeting patterns
Table 6 Mean percentage of words per Tweet referring to community-building by institution type and by regional scale. Means
are expressed as the percentage of total words within a Tweet that are words associated with a given LIWC category of community
language
LIWC category Mean word percentage
(standard error)
Vargha and Delaney’s A
(eect size)
Wp
Environmental Health
Social behavior 1.60 (0.07) 1.92 (0.12) 0.46 (negligible) 165,702 0.024
Prosocial behavior 0.52 (0.04) 0.52 (0.07) 0.51 (negligible) 181,668 0.464
State or national Local
Social behavior 1.66 (0.07) 1.80 (0.12) 0.51 (negligible) 177,700 0.443
Prosocial behavior 0.53 (0.04) 0.51 (0.07) 0.49 (negligible) 171,059 0.574
Table 7 Mean percentage of words per Tweet referring to community language by location. Means are expressed as the percentage
of total words within a Tweet that are words associated with a given LIWC category of community language
LIWC category Mean word percentage (standard error) Ε2χ2 pDunn’s Post
hoc
USA OR WA
Social behavior 2.17 (0.22) 2.47 (0.13) 1.33 (0.07) 0.071 91.25 < 0.001 USA vs. OR*
USA vs. WA***
WA vs. OR***
Prosocial behavior 0.57 (0.12) 0.60 (0.08) 0.48 (0.04) 0.0003 0.32 0.851 USA vs. OR
USA vs. WA
WA vs. OR
Note: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.0 01
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Slavik et al. BMC Public Health (2024) 24:379
in relation to potential smoke exposure. We found that
more than half of all Tweets authored by accounts in Ore-
gon and Washington were Tweeted during peak smoke
levels (58% and 54% of annual total Tweets, respectively).
ese results indicated that institutions in Oregon and
Washington took a responsive approach to communi-
cate to citizens about wildre smoke when the risk of
exposure was highest. In comparison, the three USA-
based accounts Tweeted a smaller proportion of Tweets
during peak smoke levels (44% of annual total Tweets).
is result may be attributed to the fact that these three
accounts represent national agencies that communicate
about air quality issues across the USA, and thus, one
might expect these accounts to Tweet less frequently in
response to specic wildre and smoke events occur-
ring in a particular region. Still, the ndings from this
analysis indicate that communicators in Oregon and
Washington are generally following the US EPA’s recom-
mendations around communicating about wildre smoke
even during wildre o-seasons [29]; in both states, each
Twitter account authored, on average, the same number
of Tweets (18 Tweets) during the period prior to peak
smoke levels.
We also examined whether institutions followed best
practices for communicating on social media by leverag-
ing three message functions: (i) encouraging the adoption
of smoke-protective actions; (ii) informing the public
about health risks using verbal cues, numeric informa-
tion and AQI risk labels; and (iii) promoting commu-
nity-building. Use of these message functions has been
associated with advancing numerous public health goals.
For example, messaging encouraging the adoption of
protective actions and community-building can lead to
changes to behavior that help mitigate health risks asso-
ciated with exposure to environmental hazards [41, 42,
62], and leveraging numeric, verbal, and AQI risk infor-
mation appears to aid individuals’ health decision mak-
ing and understanding of risks [49, 57]. Our ndings
indicated that institutional accounts used Twitter to pro-
mote smoke-related behavior change (as indexed through
dimensions of Protection Motivation eory) more often
than they used it to disseminate wildre smoke risk infor-
mation or promote community-building.
Specically, the majority of Tweets we analyzed (82%)
included some form of protection motivation con-
structs—messaging that has been found to generate
greater willingness to adopt actions to reduce exposure
to hazards [42]. However, less than half of the Tweets in
our sample used language associated with community-
building (47%), and only a quarter reported any kind of
risk information (25% verbal, 5% numeric, 17% AQI).
Furthermore, we found dierences in institutions’ use
of action-, information- and community-based mes-
saging across account types, highlighting the challenges
for institutions to communicate about wildre smoke
consistently.
Individuals are increasingly turning to online sources
for information about wildre smoke [23, 24, 27]. Given
the limited research evaluating ocial online wildre
smoke communications [10, 12, 45], our study addressed
an important knowledge gap by shedding light on how
institutions communicate about wildre smoke to highly
exposed populations through social media. is research
also oers lessons for how institutions can improve
health messaging about wildre smoke in the future by
leveraging messaging that is evidence-based, timely and
theory-driven. As exposures to environmental risks like
wildre smoke become more frequent and more intense
with climate change [1], reducing the burden of disease
attributable to these hazards will require that health o-
cials not only eectively leverage popular communica-
tion channels and distribute relevant communications
rapidly, but also get the message right. We make four
recommendations.
1. Institutional messaging requires more integration with
protection motivation theory constructs
Individuals and communities can adopt several measures
to minimize and mitigate exposure to wildre smoke,
for example, by using respirators and at-home air puri-
ers, reducing time outdoors during smoke events, and
upgrading building ventilation [75]. Since applying con-
structs from Protection Motivation eory in health
communications has previously been found to encourage
action-taking [42], the high frequency of Tweets in our
sample containing Protection Motivation eory messag-
ing indicates that institutions appeared to use language
promoting behavior change to reduce the threat of smoke
exposure and health harms. is was the case especially
among local accounts serving county-level populations,
which used this type of messaging more frequently in
their Tweets compared to state-level or national-level
accounts that serve larger populations. is nding may
reect local health departments being better positioned
to implement health promotion interventions and drive
behavior change at the community level compared to
state and national institutions [31], for example, in orga-
nizing clean air spaces during smoke events and encour-
aging local community members to access them.
Out of the seven Protection Motivation eory con-
structs we studied, “threat” and “probability” were most
used in Tweets, appearing in nearly half of all Tweets
in our sample (48% and 46%, respectively). us, insti-
tutions appeared to be eective at leveraging certain
elements from the Protection Motivation eory to
describe the hazard that smoke poses and the probability
of individuals encountering it.
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Slavik et al. BMC Public Health (2024) 24:379
Only 15 Tweets used messaging to satisfy at least some
elements from each of the four main Protection Motiva-
tion eory cognitions (i.e., severity, likelihood, mitiga-
tion, self-ecacy). Consistent with research by Marfori
et al. [27], only 7% of Tweets discussed the health conse-
quences of smoke exposure, which may have impacts on
individuals’ abilities to make informed decisions about
smoke risks and taking appropriate precautions. Further,
the small proportion of total Tweets discussing popula-
tions vulnerable to smoke-related health eects and spe-
cic mitigation measures (10% and 14%, respectively)
represents an important area where health messaging
could improve—especially since individuals in vulner-
able groups seem to seek-out information about wildre
smoke more frequently and could benet from tailored
messaging [6, 12]. Moreover, increasing people’s per-
ceived vulnerability to wildre risks, and providing them
with actionable solutions to reduce smoke exposure,
appear to be important motivators for undertaking risk-
mitigating actions [39–41].
Our analysis also found that terms instructing people
to stay indoors or limit time outdoors were more fre-
quently mentioned than terms related to other mitigation
measures like air purier use or ventilation systems to
improve air quality. is nding was consistent with Van
Deventer and colleagues’ research on health messaging in
Washington state during a 2018 smoke event [10], sug-
gesting that ocials instructed individuals to stay inside
without necessarily providing guidance on how to make
indoor spaces safer. Since the prevalence of residential
heating, ventilation, and air conditioning systems in cit-
ies like Seattle, Washington and Portland, Oregon con-
sistently rank lower than many other major US cities
[76], and smoke particles can approach 70–100% of the
outdoor concentrations in homes without air condition-
ing [29], staying indoors may not always oer people
substantial health benets and may not be feasible for
everyone.
2. Combined numeric information, verbal cues, and AQI
risk labels should be leveraged in communications about
wildre smoke
e percentage of Tweets in our sample that informed
users about how much wildre smoke risk was pres-
ent using verbal and numeric risk terms (25% and 5%,
respectively) was smaller than the percentage of Tweets
containing Protection Motivation eory constructs
described above. is nding presents two challenges to
risk communicators in Oregon and Washington. First,
messaging promoting behavior change appears to be
most eective when supplied with information about
levels of risk [42], suggesting the frequency of “action”
and “information” Tweets should be more similar. Sec-
ond, since many people appear to prefer to receive risk
information containing numbers alone or numbers with
verbal cues [49, 50]. e large discrepancy between ver-
bal and numeric risk information reported by institutions
in our sample should be addressed by institutions going
forward.
Nearly a fth of Tweets in our sample referenced AQI
risk labels to inform audiences about how clean or pol-
luted the air was. As prior research has indicated that
risk labels may improve individuals’ health-related judg-
ments and even improve use of numeric information in
decisions [57, 77], the use of AQI risk labels in health
messaging on wildre smoke should be continued. Our
results indicated that local organizations were more likely
to reference AQI risk labels (e.g., “hazardous”) compared
to state-level or national-level accounts. is nding can
be explained by the fact that AQI information is obtained
using local monitors to reect hazard levels, and thus,
Twitter accounts representing individual counties more
often report local AQI levels while state-level or national-
level accounts likely report broader regional trends in
air quality. Since the public generally expects to have
access to locally-relevant information about smoke levels
in their community [11, 12] and tends to report higher
levels of trust in local sources of information [14], local
accounts should continue to report local AQI risks in
their health messaging when disseminating information
about smoke risks. On the other hand, state- or national-
level accounts could play a role in re-directing their
online audiences to follow local accounts for risk infor-
mation [31].
3. Engaging, bi-directional smoke communications are
needed
Our study examined whether institutions encouraged
community-building through references to social inter-
actions and/or social behaviors, which may have impli-
cations for encouraging public participation in health
promotion and trust in government institutions [60, 61].
On average, less than 2% of words in each Tweet in our
sample constituted community language. Nearly half the
Tweets in our sample (47%) contained at least one word
associated with community-building. is proportion
of Tweets containing community language is relatively
higher than the 22–35% reported in other studies exam-
ining public health Twitter communications; however,
previous studies relied on manual thematic coding of
Tweets, and consequently, direct comparisons are di-
cult to draw [30, 31]. To assess community language use
across various accounts, we used the percentage of words
per Tweet containing social behavior words as a more
conservative metric summarizing how much each Tweet
met the community language criteria, rather than only
relying on the presence or absence of themes in a text.
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Page 13 of 16
Slavik et al. BMC Public Health (2024) 24:379
Our analysis of community-building messaging by
institution did not reveal any signicant dierences
in how various accounts used language about proso-
cial behavior. However, it did reveal that terms associ-
ated with any social behavior appeared more in Tweets
authored by health institutions compared to environ-
mental institutions. is nding could be explained by
the fact that health institutions, focused on community
health, appear to leverage more human-centered mes-
saging compared to environmental institutions, which
primarily prioritize environmental protection. Since citi-
zens with less knowledge about wildre risks appear to
rely more on community cues as motivation for engaging
in wildre-protective behaviors [41], our nding suggests
environmental institutions may benet from the use of
more community language to promote the uptake of AQI
messaging. e public’s desire for smoke messaging that
engages community members [26] further demonstrates
that this is an important message function that ocials
should prioritize.
4. Take advantage of smoke “o-seasons” to implement
a proactive approach to wildre and smoke health
messaging
Large-scale wildres (and wildre smoke) are not unprec-
edented in the US Pacic Northwest, and both Oregon
and Washington have amassed considerable experience
managing public health risks attributable to this hazard
[78]. In fact, our analyses indicated that, on average, pop-
ulations in both states appeared to experience similar lev-
els of smoke exposure in 2022 based on maximum daily
AQI values, which was also corroborated by research
from Burke et al. [66]. Yet, our study suggests that institu-
tions in both states took slightly dierent approaches to
communicate about wildre and smoke on social media.
Accounts based in Washington Tweeted a higher per-
centage of annual total Tweets (44% vs. 39% for Oregon-
based accounts) between January-August, suggesting
these accounts may have taken a more proactive commu-
nication approach prior to the two-month peak period
in wildre smoke compared to Oregon-based accounts.
Proactive communication is viewed as critical for help-
ing households prepare for wildre smoke and eectively
mitigate health harms associated with smoke exposure
[29]. Washington-based accounts also appeared to pri-
oritize wildre and smoke messaging that encouraged
the adoption of protective actions and informed people
about risks using verbal, numeric and AQI formats. On
the other hand, accounts in Oregon seemed to prioritize
messaging that focused more on community-building
by using more community language per Tweet. ese
dierences in communication strategies could be inten-
tionally tailored to their respective populations, or they
may simply reect dierences in resources (e.g., trained
communications sta) allocated to communications and
smoke information dissemination. Future research could
oer signicant insights into these state-by-state dier-
ences by conducting interviews with communications
sta and personnel to gather more information.
Practical implications for public health
e ndings of this study can provide numerous lessons
to public health practitioners and researchers regard-
ing communication strategies for environmental health
hazards like wildres and smoke. First, our work points
to the importance of proactive and sustained communi-
cations informing the public about wildres and smoke
before they are likely to encounter these hazards. Timely
communications are key for educating and empowering
individuals to make informed decisions about protect-
ing themselves from harmful exposures. is research
also demonstrates how theories of health promotion
like the Protection Motivation eory can be leveraged
to craft more eective messages for behavior change.
Additionally, we highlight the need for communicators
to employ numeric information, verbal cues, and AQI
risk labels, to help individuals quantify and compare risk
levels across time and space and mitigate impacts on
health. Finally, we call attention to the need for commu-
nications to increase engagement with communities and
for social media to generate more interaction between
public health stakeholders and citizens. Although this
paper focuses on health messaging related to wildre and
smoke, insights can be drawn for communicating risks
associated with exposure to many other environmental
hazards.
Limitations
is research’s main limitations stem from its reliance
on Twitter data. In this study, we analyzed Tweets
by various governmental institutions in the US that
Tweeted in 2022; however, the public likely receives
information from numerous other sources (both
offline and online), and likely also encounters wildfire
and smoke communications on Twitter from accounts
that were not captured in our sample. Although simi-
larities likely exist between what an agency Tweets
and what they communicate on other channels, our
research cannot draw conclusions about all wildfire
and smoke communications that the public may view
in the US Pacific Northwest; research on other infor-
mation sources is needed.
It is also important to note that not all individu-
als use Twitter, and its future as a key governmen-
tal communication platform remains uncertain as
it has transitioned into a new platform called “X”.
Our results may not reflect the current social media
landscape and our analysis only captured a limited
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 14 of 16
Slavik et al. BMC Public Health (2024) 24:379
number of accounts that Tweeted about wildfire and
smoke events in the Pacific Northwest region of the
US during the study period (2022). Thus, this study
examines smoke communication practices on Twitter
at a particular snapshot in place and time and future
research should be extended to other regions and time
periods. We could have used a portion of the total
dataset of 85,406 Tweets published between April
2009 and December 31st 2021 and compared these
to the January 1st 2022 through December 31st 2022
Tweets that were selected and analyzed for this study
to measure changes in messaging from the earlier
time period to the time period we selected. However,
since earlier time periods did not contain a complete
Tweeting history from some accounts, we selected
only Tweets from a single year (i.e., 2022) in order to
conduct a more in-depth analysis of wildfire smoke
communications.
Additionally, we relied on select keywords to score
institutions’ use of Protection Motivation Theory
dimensions and risk language. Although this approach
was informed by prior literature and based on an initial
scan of the Tweets, the list of keywords selected likely
did not capture an exhaustive list of words that could
be associated with the dimensions (or sub-dimensions)
scored. We also did not quantify the public’s engage-
ment to Tweets in this research, nor did we measure
real behavior change in response to health messaging.
Future work could expand on this research consider-
ably by measuring the effectiveness of wildfire smoke
communications on behavioral intentions through
experimental research. More research is also needed to
compare the impacts of communications disseminated
during smoke off-seasons versus during peak smoke
levels on citizens’ smoke preparedness. Such research
on the effectiveness of proactive versus reactive health
communications in motivating behavior change would
guide health officials in determining the most strategic
approach to safeguarding public health during wildfire
smoke events. Still, our work offers valuable insights
that public health stakeholders can apply to other
means of communication that dominate the media
landscape going forward.
Conclusion
Reducing population exposure to wildfire smoke is a
major public health challenge in the US Pacific North-
west and effective health messaging is needed to edu-
cate the public about smoke-related health risks and
how they can mitigate them. This research analyzed
Twitter communications about wildfires and smoke
from 2022 authored by institutional public health and
environmental accounts in Washington and Oregon.
This study compared Tweeting patterns over time, in
connection with potential wildfire smoke exposure,
and evaluated communications based on whether
they encouraged the adoption of smoke-protective
actions, informed the public about health risks, and
promoted community-building. Overall, we found
that institutional Tweeting generally coincided with
rising wildfire smoke levels, suggesting institutions
tailored their messaging in response to potential popu-
lation exposures to wildfire smoke. This research also
found that accounts mostly used Twitter to promote
smoke-related behavior change and used it less for the
purposes of disseminating wildfire smoke risk infor-
mation or promoting community-building. This study
fills an important knowledge gap around institutional
communication practices and social media health
messaging about wildfire smoke to highly exposed
populations.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12889-024-17907-1.
Supplementary Material 1: Supplemental Table 1. List of Twitter ac-
counts used to construct Tweet sample for analysis, their characteristics,
and the number of Tweets they generated in the year 2022
Acknowledgements
We express our sincere gratitude to J Connor Darlington for his invaluable
contribution in assisting with Tweet data collection and providing study
materials, crucial to the successful execution of this research. We would also
like to thank the reviewers for their comments and suggestions, which greatly
improved this work.
Author contributions
CES led the study’s conceptualization, data collection, and data analysis
strategy and took a lead role writing the manuscript. DC carried out data
analyses, prepared all manuscript gures and assisted with manuscript
writing. ASC assisted with the study’s data analysis strategy and assisted
with manuscript writing. Both CES and ASC collaborated on Tweet coding
to generate the Tweet study sample. NB assisted with data analyses and
manuscript writing. EP assisted with manuscript writing and provided project
guidance.
Funding
This research did not receive any funding.
Data availability
The datasets used and/or analyzed during the current study were obtained
from Twitter (now ‘X’) using an API, thus, they are not publicly available but are
available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 15 of 16
Slavik et al. BMC Public Health (2024) 24:379
Author details
1School of Journalism and Communication, University of Oregon, 1715
Franklin Boulevard, Eugene, OR 97403, USA
2Center for Science Communication Research, University of Oregon,
Eugene, OR, USA
3Department of Psychology, University of Oregon, Eugene, OR, USA
Received: 10 November 2023 / Accepted: 27 January 2024
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