# Using Localized Twitter Activity for Red Tide Impact Assessment

A. Skripnikov,111Postal address: New College of Florida, Heiser Natural
Sciences Complex, Room E156, 500 College Dr, Sarasota, FL 34243. Email:
<EMAIL_ADDRESS>Tel. : 352-283-2960 N. Wagner  J. Shafer  M. Beck  E.
Sherwood  M. Burke  New College of Florida, Division of Natural Sciences, 500
College Dr, Sarasota, FL 34243, USA.  Science and Environment Council of
Southwest Florida, 1530 Dolphin Street, Suite 4, Sarasota, FL 34236, USA.
Tampa Bay Estuary Program, 263 13th Ave S, St. Petersburg, FL 33701, USA.

###### Abstract

Red tide blooms of the dinoflagellate Karenia brevis (K. brevis) produce toxic
coastal conditions that can impact marine organisms and human health, while
also affecting local economies. During the extreme Florida red tide event of
2017-2019, residents and visitors turned to social media platforms to both
receive disaster-related information and communicate their own sentiments and
experiences. This was the first major red tide event since the ubiquitous use
of social media, thus providing unique crowd-sourced reporting of red tide
impacts. We evaluated the spatial and temporal accuracy of red tide topic
activity on Twitter, taking tweet sentiments and user types (e.g. media,
citizens) into consideration, and compared tweet activity with reported red
tide conditions, such as K. brevis cell counts, levels of dead fish and
respiratory irritation on local beaches. The analysis was done on multiple
levels with respect to both locality (e.g. entire Gulf coast, county-level,
city-level, zip code tabulation areas) and temporal frequencies (e.g. daily,
every three days, weekly), resulting in strong correlations between local per-
capita Twitter activity and the actual red tide conditions observed in the
area. Moreover, an association was observed between proximity to the affected
coastal areas and per-capita counts for relevant tweets. Results show that
Twitter is a reliable proxy of the red tide’s local impacts and development
over time, which can potentially be used as one of the tools for more
efficient assessment and a more coordinated response to the disaster in real
time.

###### keywords:

Red tide, Sentiment analysis, Social media analytics, Twitter

##  1 INTRODUCTION

Red tide algal blooms associated with marine dinoflagellates are regular
occurrences in coastal environments where oceanographic conditions, nutrient
availability, and planktonic community interactions create conditions
favorable for growth. Increases in the frequency, duration and severity of
these events have occurred over the years (Tester and Steidinger, 1997,
Kirkpatrick et al., 2004, Glibert et al., 2005), stimulated by multiple
factors related to coastal land development and climate change (Glibert,
2020). Toxins (Lin et al., 1981, Poli et al., 1986) produced by these species
or degradation of water quality conditions with cell decomposition can have
multiple negative impacts on marine environments and human communities in
coastal areas. Large die-off events of marine organisms are commonly observed
with red tides (Sievers, 1969, Steidinger, 1996), including fish, marine
associated birds, turtles, and large mammals (e.g., cetaceans, sirenians).
Bioaccumulation of neurotoxins can further alter ecosystem trophic dynamics
(Pierce and Kirkpatrick, 2001). Coastal conditions for humans during ride
tides are also negatively impacted, as toxins are aerosolized out of the water
column contributing to respiratory or skin irritation that can lead to
hospitalizations (Backer et al., 2005, Fleming et al., 2005, Fleming et al.,
2007, Milian et al., 2007). Economic impacts are also observed, as tourism or
waterfront business revenues are reduced in areas affected by red tide.

Red tides from K. brevis have been observed on the southwest coast of Florida
for over a century. Similar to global trends in harmful algal bloom (HAB)
occurrences, these events have increased in severity, frequency, and
geographic extent in recent years. While blooms originate offshore on the West
Florida Shelf, onshore winds and currents can bring red tide blooms inshore to
beaches and estuaries along Florida’s Gulf Coast, where they may be
exacerbated by coastal nutrient sources. Extensive hydrologic modification of
southwest Florida has altered the quantity, location, and timing of nutrient
delivery from background conditions. Land use conversion of wetlands to
agriculture and urbanization of coastal areas has further altered drainage
patterns. The impacts of these changes on coastal water quality in southwest
Florida have been exacerbated by climate change, as seasonal temperature and
precipitation patterns have deviated from historical averages. Although no
single cause has been identified, the synergistic effects of multiple
stressors have likely contributed to significant red tide events. One of the
longest and most severe events to date persisted on the coast of southwest
Florida for 16 months from Fall of 2017 to early 2019.

Given the potential for large negative effects of red tide events on the
environment, economy and public health, additional datasets that can augment
conventional monitoring data could improve how local governments plan and
respond to these events. Local governments and regional planning councils rely
on regularly updated and routine monitoring data to respond appropriately to
environmental disasters. These data inform how future response is managed to
anticipated events, as well as how real-time response to ongoing events
changes relative to where impacts are most observed. For red tide events,
local governments may require data on where blooms are most severe to inform
clean-up events (e.g., in response to fish die-offs) or to issue public safety
announcements regarding beach closures or air quality advisories. Real-time
rapid assessment of in situ conditions can inform these decisions, whereas
more detailed information about oceanographic conditions and phytoplankton
cell counts that require lab processing can be used for forecasting where
bloom events are likely to occur in the future ([34], [54]). Moreover,
conducting keyword analysis of social media messages could help in determining
the issues of public’s utmost concern (e.g. is it economic impacts? health
consequences? general concern for the environment?), hence informing decisions
on the optimal distribution of the resources and funding (e.g. assisting
waterfront businesses, distributing protective equipment, organizing marine
debris cleanup, etc).

The recent bloom in southwest Florida was the first major red tide event since
broad public use of social media platforms, offering potentially unique
opportunities to assess complementary sources of information that can aid
management response to natural disaster events. During natural catastrophes
like floods, earthquakes, and hurricanes people have been turning to platforms
such as Twitter (Eismann et al., 2016, Kryvasheyeu et al., 2016, Mukkamala and
Beck, 2017, Zou et al., 2019), Facebook (Bird et al., 2012, Eismann et al.,
2016, Bhuvana and Aram, 2019, Jayasekara, 2019), Instagram (Sherchan et al.,
2017), Flickr (Liu et al., 2008, Preis et al., 2013, Chien et al., 2017) to
share their sentiments and experiences, making social media a valuable source
of information. There’s been a variety of aspects studied in cases of
utilizing social media within the context of disaster events, ranging from
collective behavior analysis (Liu et al., 2008, Eismann et al., 2016), to risk
and crisis communication (Veil et al., 2011, Bird et al., 2012), to relief
effort coordination (Gao et al., 2011, Purohit et al., 2014, Bhuvana and Aram,
2019), among others. Meanwhile, in this work we intend to focus on studying
the spatio-temporal correlations between localized social media activity and
disaster impacts to respective areas (Preis et al., 2013, Kryvasheyeu et al.,
2016, Chien et al., 2017, Chen et al., 2020), along with conducting keyword
and sentiment analysis for gauging public’s situational awareness during the
disaster (Verma et al., 2011, Yin et al., 2012, Mukkamala and Beck, 2017).
That would serve to provide an additional use case and partially confirm some
of the research done in Preis et al., 2013, Kryvasheyeu et al., 2016, when
user activity on Twitter and Flickr was shown to be a good proxy for
evaluating local damages during hurricane Sandy, or the study conducted in
Chen et al., 2020, where tweets containing certain keywords were shown to
correlate with road closures due to hurricane Harvey flooding.

Our study focused on Twitter - a “microblogging” service used by over 330
million people worldwide to share short strings of text or “tweets” to convey
opinions or ideas on any topic. Besides its efficiency of communication (due
to character restrictions imposed on tweet content), a key component of
Twitter is the ability to share or “retweet” information from other users such
that a single message can reach a much wider audience than the user’s
immediate followers. Tweets can also be used to assess a response in time
(i.e., how do people react after an event) that can be traced to a geographic
location if, for example, a tweet is spatially referenced by the user. Thus,
interpretation of tweet content across multiple users, relative numbers of
retweets, spatial, and temporal information can provide an assessment of topic
importance that can guide response and recovery efforts. The use of Twitter to
both gauge the impacts and inform disaster response has received some
attention in the recent literature. For example, links between Twitter
activity and localized storm damages (Kryvasheyeu et al., 2016) and community
response to storm events when power outages prevented use of conventional
communication outlets (Pourebrahim et al., 2019) have both been demonstrated
with an analysis of tweet data during hurricane Sandy. While storm events
unfold over days, red tide events can last several months and thus introduce a
significant temporal as well as spatial component to the impacts.

Here, we present a multi-level analysis of Twitter activity as it relates to
the Florida 2017-2019 red tide event. Our overall goal was to evaluate the use
of Twitter as a potential complement for data on real-time conditions as
measured by in situ observations (e.g., phytoplankton cell counts) or reported
conditions relevant to public health (e.g., beach conditions). The intent is
not to supplant existing methods for tracking status and trends of red tide
conditions, but to understand social responses to these events that may have
management implications for improving communication about red tide, both
general education and real time conditions, and for prioritizing prevention or
mitigation actions by local governments. The use of Twitter as a proxy for
real time conditions could also augment existing datasets by providing either
confirmatory, or in some cases anticipatory, information in the absence of in
situ data. For example, this may have value if regular monitoring programs for
tracking conditions do not exist or lab processing times prevent rapid
response to current conditions. Lastly, we analyzed sentiment and topical
nature of the tweets for the purposes of gauging public’s situational
awareness during the event, determining the issues of concern (environment,
health, economy, government), and potentially improving the accuracy of
Twitter metrics as proxies for local red tide conditions.

##  2 MATERIALS AND METHODS

###  2.1 Red tide local conditions data.

For local beach conditions data [31], such as dead fish and respiratory
irritation levels, we used daily reports from 12 main beaches, which included
six in Sarasota county (Manasota, Venice, Venice/North Jetty, Nokomis, Siesta
Key, Lido Key beaches), two in Manatee (Coquina, Manatee beaches), and four in
Pinellas (Pass-a-Grille, Treasure Island, St Pete, Clearwater beaches). When
accumulating the data by counties, the observed dead fish and respiratory
irritation levels were averaged across the beach pertaining to one county. On
city and zip code tabulation area (ZCTA) levels, each city and ZCTA were
either assigned the average reported conditions from the beaches nearby, or
assigned red tide impact of zero if they weren’t near any of those beaches.

K. brevis cell count data [35] was not as consistently available as the local
beach conditions data, taking on the form of samples collected sporadically
all across the southwest Florida coast, with the exact longitude and longitude
coordinates of sample locations provided (see Figure 8(A) in the Appendix). We
managed to define polygons on the map that allowed to assign all K. brevis
samples taken with that specific area to the nearest county, but doing it on
city- and ZCTA-levels was impractical, hence we stuck to county-level data for
K. brevis. Moreover, when summarizing K. brevis cell counts over a week or a
three-day period, we had to account for the fact that some counties had more
samples taken (e.g. Sarasota), and most samples generally showed 0-cell
counts. It meant that, if we were to simply take the mean cell count across
all samples, counties with larger number of samples could have their means
severely down-graded due to a huge number of 0-cell count samples, despite
having several other areas of really high cell counts. Instead, we evened the
playing field by only considering the five largest cell counts during that
period in that particular county.

###  2.2 Raw Twitter data collection.

The raw data for the 2017-2019 Florida red tide event was obtained via
Twitter’s Full Archive Premium search. First and foremost, to guarantee that
the tweet is pertinent to the red tide event, we searched for those that had
either ”red tide” or ”redtide” mentions in their content. Secondly, given that
the focus of the study was on evaluating local impacts of the red tide on the
five counties situated on Florida’s Gulf coast surrounding Tampa Bay and
Sarasota Bay (Pasco, Hillsborough, Pinellas, Manatee and Sarasota), we needed
tweets that also had an affiliation with that particular area. It was
accomplished by running search queries that match tweets to a location in two
different ways. First is called a ”place” match, which pertains to tweets that
were explicitly geo-tagged by the user as being sent from the location of
interest. Second is a ”geoprofile” match, corresponding to tweets from users
that had a relevant location specified in their profile description.
Geoprofile matches represent implicit association with the area of interest
because, unlike explicit geo-tags, they don’t guarantee that the tweet was
sent from the location specified in the user’s profile description.
Nonetheless, due to scarcity of place matches (only about 1.5% of tweets are
explicitly geo-tagged), geoprofile matches served to both considerably augment
the amount of potentially pertinent tweets, and to construct several
alternative Twitter metrics to be compared in terms of reflecting the actual
red tide impacts. Besides different location-matching criteria, tweets were
also subdivided by user account types (e.g. media, citizen, etc) to see if
Twitter activity by specific subgroups of users serves as a more reliable
proxy of the conditions. For the user privacy protection purposes we won’t be
releasing the exact criteria of determining account type assignment, bar the
fact that the account verification status was used to distinguish most citizen
accounts (unverified status) from those of public interest, e.g. media,
political figures, government agencies, visitor bureaus, that get the official
verification stamp (blue tick mark, signifying verified status) from Twitter.
Lastly, we only included tweets posted between May 15 of 2018 and May 15 of
2019, giving a sufficient window to witness both the anticipation period
(preceding the first coastal evidence of red tide in June 2018) and the
potential long-term after-effects (several months after the last observation
of red tide in January 2019 near Sarasota area) for the time window when red
tide was most active.

###  2.3 Twitter data cleaning.

To verify relevance of all the matched tweets, post-collection data cleaning
was performed. Presenting the biggest issue were the tweets containing phrases
like ”Red Tide Rick” and ”Red Tide Party”, referencing nicknames for Florida’s
then-governor Rick Scott and the Republican Party, respectively. Given that
2018 Florida red tide event happened to strongly overlap with the local
elections, such references could be found to match better with the dates of
political events (e.g. general election or campaign events) rather than the
actual local red tide impacts (see supplementary materials). To avoid
conflation of public’s concern and reports about local conditions with someone
using it as a political statement, we simply disposed of tweets that, outside
of political nicknames, contained no other mention of the actual red tide
event.

Having cleaned the overall tweet corpus, the loads that were obtained via
different location-matching criteria - explicit geo-tags and geoprofile
matches - were merged to avoid double-counting the tweets matched by more than
one criteria. In the meantime, if we encounter tweets that happened to be
matched to one area via explicit geo-tag and to a different area via
geoprofile (e.g. a tweet geo-tagged in Bradenton, while user’s profile
location says ”Sarasota”), we prioritize the place match in determining the
ultimate location affiliation of the tweet.

Another round of cleaning had to do with tweets being assigned as a geoprofile
match to Hillsborough county purely based on the user specifying ”Tampa Bay”
as their profile location, which could have easily pertained to Pinellas
county as well. Therefore, we reassign these tweets as geoprofile matches to
the greater Tampa Bay area, with the credit subsequently shared between
Hillsborough and Pinellas (see the details of credit-sharing scheme in Section
2.4).

Having completed all the cleaning stages, we ended up with a grand total of
18082 tweets geo-matched to our five counties of interest. Among these, 1295
were explicit geo-tags, while the other 16787 were matched solely based on
geoprofile location information. This tweet load included original tweets
(8841, $49\%$ of the load), replies (1784, $10\%$) and retweets (7457,
$41\%$).

###  2.4 Per-capita tweet counts and credit sharing among several counties.

Among other considerations, because the number of Twitter users can vary
greatly depending on the location, we considered metrics on per-capita basis,
normalizing by the population of that respective area. Tables 2-4 in the
Appendix point to per-capita local tweet counts presenting a much more
reliable picture of the actual red tide conditions in the area. Data from US
Census Bureau website [53] was used to obtain population estimates for our
five counties of interest in 2018. While we are aware that Twitter only allows
users of age 13 and older, the exact percentages of 13+ population weren’t
provided, and we simply decided to go with the overall population as a sound
proxy. Hillsborough county had nearly 1.5 million people (including the highly
populated Tampa metro area), Pinellas being second with 973 thousand, Pasco
third with 539 thousand, Sarasota - 426 thousand, and Manatee county was last
with about 400 thousand.

Credit-sharing for tweets matched to the general Tampa Bay area took place
commensurate to the relative twitter-eligible population sizes of Hillsborough
(60%) and Pinellas (40%) counties. So, if we had a 100 tweets matched to Tampa
Bay area, 60 of them are credited to Hillsborough, with the remaining 40 - to
Pinellas.

In the meantime, when working on city- and ZCTA-level data, we’ve also used US
Census Bureau data with 2018 population estimates for all the cities in the
Tampa Bay surrounding area. Moreover, we made sure to ”smooth” the populations
of smaller cities to be considered as a part of the metro area around the
closest large city, avoiding for such tiny towns as Bradenton Beach or Anna
Maria (with populations of under 1000) to be blown out of proportion as a
result of per-capita calculations. Hence, all cities belonging to the same
metro area were divided by the population of that metro area (e.g. in case of
Bradenton Beach, we divided its counts by the total cumulative populations of
all surrounding cities, such as Bradenton, West Bradenton, Palma Sola,
Bradenton Beach itself, etc), while ZCTA counts were divided by the population
of the metro area that it was associated with.

###  2.5 Sentiment analysis.

The main goal of sentiment analysis is to quantify the emotion expressed
within the text. Depending on whether the message is positive, negative or
neutral in nature, an appropriate numerical value is assigned to reflect that
sentiment. In the case of our data, monitoring temporal dynamics of the
sentiment for red tide related tweets in a given location might aid in
determining whether the area is currently being affected (”Siesta Key beach is
littered with dead fish, smell is horrible #redtide”), recovered from it (”Red
tide is gone from Madeira Beach”), or avoiding it altogether (”No red tide in
Clearwater!”). In particular, we will be looking at correlations between the
sentiment expressed by Twitter users and the actual red tide conditions
observed in the area at the time.

To assign sentiment values, we use the methodology implemented in $sentimentr$
package [46] from $R$ Statistical Software [45]. It relies on a combination of
pre-defined polarized word lexicon, vocabulary of valence-shifters, and
question weight. Polarized words (or phrases) are assigned a positive or
negative value, with magnitude representing the strength of expressed emotion
(e.g. ”bad” is $-0.75$, while ”disgusting” is $-1.0$; ”good” is $0.75$, while
”gorgeous” is 1). Valence-shifters are words (or phrases) that might change
either the direction or strength of the polarized words found in their
vicinity. That includes complete negation (e.g. ”not”, ”isn’t”, ”hasn’t” would
flip the sign of polarized words nearby), amplification/de-amplification (e.g.
”very”, ”strongly”, ”completely” would increase the magnitude of polarized
words nearby, while ”hardly”, ”barely” \- decrease it), adversative
conjunction (e.g. ”but”, ”however”, ”although” would increase the strength of
the following polarized words, while decreasing it for the preceding ones).
Moreover, the questions can be downweighted, due to expressing less conviction
as opposed to declarative statements (e.g. ”is red tide really bad there?” as
opposed to ”red tide is really bad there.”). In our case, we set question
weight at $0.25$, meaning that the total sentiment score of a question would
be divided by 4. Lastly, we also treated occurrences of multiple periods in a
row (e.g. ”this red tide…”, ”water is discolored…”) as conveying a negative
emotion (generally used to indicate exasperation, annoyance, frustration), so
we decreased the sentiment value by $0.15$ per each such occurrence in a
tweet.

In case of the red tide event, we needed to customize the polarized word
lexicon for it to be more applicable. As an example, we needed to dispose of
the word ”bloom” as a positive, given that in our context it pertains to algae
bloom - an occurrence that’s negative in relation to HAB events. We likewise
needed to associate words like ”impact” and ”affect” with the negative effects
the red tide had on the local environment, economy and health. Moreover,
occurrence of phrases such as ”low/medium/high levels”, ”low/medium/high
concentrations” exclusively pertained to the presence of K. brevis cell
counts, another negative event. On the other hand, all phrases pointing to the
absence of red tide were given a strong positive score (”no more red tide”,
”red tide is gone”, ”no signs of red tide” all at +1).

We couldn’t possibly verify the assigned sentiment scores for every single
tweet (total of over 10 thousand tweets with distinct contents), but we
randomly sampled a 1000 tweets (practice similar to Verma et al., 2011,
Mukkamala and Beck, 2017) and meticulously went through them in the best
attempt to put together the most appropriate vocabulary of polarized words,
valence-shifters and their respective assigned scores.

###  2.6 Relationship between Twitter activity and distance to impacted areas

Our analysis of Twitter activity and proximity to the impacted areas relied on
geo-coordinates provided by tweet’s spatial information (be it that of an
explicit geo-tag, or of user profile location) and that of K. brevis samples.
To define areas of high impact, we isolated the weeks containing high K.
brevis cell count ($>1,000,000$ cells/l) for at least one sampling location,
and computed minimum geodesic distances between tweet locations and closest
site of high K. brevis levels. To obtain tweet counts (or retweet proportions)
for each relevant week, we simply calculated totals of tweets (proportion of
retweets, respectively) associated with a unique Twitter-assigned pair of
coordinates. The plots observed on Figure 4 were obtained by accumulating
these Twitter metrics along with distances across all weeks of observing high
K. brevis cell counts. Analogous work was done when qualifying impacted areas
as locations with heavy dead fish levels and intense respiratory irritation,
yielding similar results.

##  3 RESULTS.

###  3.1 Cumulative Twitter activity on ”red tide” topic across metro areas.

One of the main goals was to study localized Twitter activity in five Florida
Gulf coast counties located in the Tampa Bay surrounding area: Pasco,
Hillsborough (including Tampa metro), Pinellas (Clearwater and St Petersburg
metro areas), Manatee (including Bradenton metro), and Sarasota (including
cities of Sarasota, Venice and Englewood). First, evaluating each area’s total
Twitter activity - throughout the entire duration of the Florida red tide
event, including the anticipation and after-effects - may be of interest to
identify potential correspondence to the actual observed local red tide
impacts, such as K. brevis cell counts, dead fish and respiratory irritation
levels. The tweet counts and red tide conditions for these counties relative
to their location along the coast can be observed on Figure 1.

A

B

Figure 1: Relative tweet counts and actual red tide conditions, accumulated by
each county during Florida red tide 2018 event. A: Gross (left) and per-capita
(per 100,000 population, right) totals, for explicitly geo-tagged tweets only
(blue) and all geo-matched tweets (including geoprofile matches as well). B:
Beach condition index for dead fish (orange) ranged from 0, ”None”, up to 2,
”Heavy”; beach condition index for respiratory irritation (brown) ranged from
0, ”None”, up to 3, ”Intense”.

A clear pattern of diminishing red tide impact moving northward from Sarasota
to Pasco is reflected on Figure 1(B) in decreasing number of K. brevis cell
counts, dead fish and respiratory irritation levels observed in the respective
areas. In the south, Sarasota and Bradenton were most impacted, whereas
impacts in Hillsborough and Pasco counties to the north were not as severe. In
contrast, if one were to rely on total tweet counts as the sole proxy for red
tide impacts for each metro area - ”Total” plot on Figure 1(A) - Hillsborough
would stand out as the most active region in terms of Twitter activity,
despite receiving minimal effects of red tide. That happens due to this
region, that includes Tampa, having by far the largest population, and we
alleviate this effect by employing per-capita tweet counts. Results can be
observed in ”Per-Capita” Twitter activity plot on Figure 1(A). We witness a
truer reflection of the actual red tide impact ranking for the five counties
of interest, with Sarasota showing the highest per-capita tweet counts, while
Hillsborough is ranked on par with Pasco with the lowest counts using this
adjustment. The explicit geo-tag counts (in blue, Figure 1(A), ”Per-Capita”),
which serve as a more reliable indicator of a person tweeting from the
location of interest, perfectly mirror the county rankings by actual red tide
impact. Unlike all geo-matched tweets (in purple, Figure 1(A), ”Per-Capita”),
considering explicit geo-tags only leads to Manatee clearly placing as the
second most active county on Twitter, while greatly reducing Hillsborough’s
Twitter activity, which reflects the severity of actual local conditions
reflected on Figure 1(B). Lastly, although explicitly geo-tagged tweets are
rare, they are much faster and easier to obtain in real time, due to Twitter’s
Full Premium Archive service allowing for a simpler set of filtering rules for
obtaining relevant geo-tags as opposed to filtering based on user’s geoprofile
information.

###  3.2 Weekly local Twitter dynamics compared to weekly local red tide
conditions.

On Figure 2(A) one may witness time series plots for weekly aggregates of K.
brevis cell counts, dead fish and respiratory levels in the entire Tampa Bay
surrounding area from May 2018 through May 2019. The five peaks observed
correspond to five peak red tide intensity impacts along the studied areas,
first one taking place in June (not as strong as others), second - in early
August (strongest impact), third - in mid-September, fourth - late
October/early November, and fifth - in early January. Figure 2(B) demonstrates
the corresponding weekly Twitter activity during the same period, represented
by total counts of tweets that were explicitly geo-tagged in the area (blue),
or also matched to it via geoprofile information (purple). One may clearly
notice strong temporal correspondence between local Twitter activity and the
red tide conditions, matching the second, third and fifth peaks, with a minor
bump corresponding to the first peak as well.

A

B

C

Figure 2: Weekly local red tide conditions and related tweet counts during the
2018 Florida event. A: Weekly red tide conditions. B: Total tweet counts, for
the entire Tampa Bay surrounding area. C: Weekly dead fish levels along with
weekly tweet counts, by county.

Witnessing strong general correlations between Twitter activity and red tide
conditions for the entire Tampa Bay surrounding area is promising, but in
reality the insights on a more localized scale (i.e. at the county-, city-
level) would be preferable for management and incident response. On Figure
2(C) we demonstrate the correspondence of our two Twitter metrics - per-capita
counts of explicitly geo-tagged and all geo-matched tweets - with the dead
fish levels, when broken down by county. In the most affected areas (Sarasota,
Manatee and Pinellas), considerable alignment of tweet counts with the periods
of high dead fish levels on local beaches is evident, including the minor bump
in Sarasota Twitter activity upon the initial sighting of red tide in June. On
the other hand, for the areas like Pasco and Hillsborough - that avoided most
of the impact - you might still witness tweets about red tide due to the
locals anticipating the red tide reaching their respective area, or simply
reporting on what’s happening in the neighboring counties. Nonetheless, it is
obvious that explicit geo-tags are less susceptible to that issue compared to
all geo-matches (that include the noisy geoprofile-matched tweets), which is
strongly exhibited on the example of Hillsborough county, leading to a
stronger overall correlation (0.79, as opposed to 0.72 for all geo-matches).

###  3.3 Multi-level spatiotemporal correlations of local Twitter activity
with red tide conditions.

In Section 3.2 we showed what happens if one studies correlations between
weekly Twitter activity and local red tide impacts on two localization levels
(entire Tampa Bay surrounding area and county-level). Here, we will study the
strength of such correlations on several more levels of locality, breaking our
data down by 57 primary cities, and subsequently by 166 zip code tabulation
areas. Figure 3(A) shows the aforementioned levels of localization in more
detail. Moreover, we experiment with other levels of temporal frequency by
looking at the data on a daily basis, or every three days, as opposed to just
every week.

A

B

C

Figure 3: Multi-level spatiotemporal correlations of local Twitter activity
with dead fish levels observed in the area. A: ZCTA-level boundary map, with
all ZCTAs belonging to the same county carrying the same main color. Shade of
ZCTA color represents a different primary city this ZCTA is associated with.
B: Heatmap-style matrix of spatiotemporal correlations for per-capita counts
of tweets explicitly geo-tagged in the area with local red tide conditions,
calculated on different levels of locality and time frequency. C: Same as B,
but for per-capita counts of all tweets geo-matched to the area, hence also
including those matched solely based on the user’s geoprofile information.

On Figures 3(B-C) you see the temporal correlations between Twitter activity
and dead fish levels observed on local beaches, and how they change depending
on the spatiotemporal levels considered. For the per-capita counts of both the
explicitly geo-tagged tweets and all geo-matched tweets, there’s an
unsurprising steady decrease in strength of correlations as we approach more
hyper-localized and higher frequency scales. For example, in case of weekly
data for explicit geo-tags, we first witness a tiny drop-off in correlations
when going from entire Tampa Bay area down to county-level dynamics (0.82 to
0.79), then a much stronger decrease on city-level (0.79 down to 0.54), and
further deterioration on ZCTA-level (0.54 down to 0.35). Analogous effect is
observed when increasing time frequency, e.g. see correlations of explicitly
geo-tagged tweets with dead fish levels on county-level go from 0.82 for
weekly data, down to 0.78 for three-day totals, to 0.68 for daily data.
Nonetheless, many of these correlations (especially on county-level,
regardless of time frequency) are still respectable enough to act as a solid
proxy for the actual red tide conditions in respective localities over time,
which itself presents an interesting research finding.

Comparing explicitly geo-tagged tweets with all geo-matched tweets in terms of
metric robustness across multiple spatiotemporal levels, explicit geo-tags
tend to outperform their counterpart virtually all across the board, in
addition to being the easier metric to obtain in real time. Same was shown for
respiratory irritation levels, as can be seen on Figure 7 in the Appendix.
Curiously, the only case where all geo-matches were preferred to explicit geo-
tags was county-level correlations of per-capita tweet counts with K. brevis
cell counts (see Figure 8 in the Appendix). As described in Section 2, K.
brevis samples were collected in a less consistent fashion (arbitrary
locations, unstable sampling frequency) than the data on dead fish and
respiratory irritation levels (robust daily reports from specific beaches),
likely making results for the latter - which showed explicit geo-tags as the
preferred proxy for local red tide impacts - more trustworthy.

In addition, we have attempted to focus on correlations of red tide conditions
with tweets from specific account types (e.g. only media, only citizens), and
also tried studying the delayed reactions and anticipation of red tide impacts
via lag and lead correlations, respectively (Spriggs et al., 1982, Dajcman,
2013). There was no evidence found of improved correlations in breaking tweets
down by account groupings, and it was shown that Twitter reacted to the
disaster in neither delayed nor anticipatory fashion. For more details on the
analysis, see supplementary materials.

###  3.4 Local Twitter dynamics based on proximity to impacted areas.

One of previous studies on social media use during a disaster event
(Kryvasheyeu et al., 2016) specifically investigated the relationship between
Twitter activity and proximity to areas of disaster impact. We carried out an
analogous analysis, measuring distances from tweet locations (cities) to
places experiencing red tide on that given week, and comparing local Twitter
activity based on proximity to the disaster. On Figure 4 you may witness the
logged per-capita counts of local tweets (linear regressions performed on
tweet counts as a function of distance warranted a log-transformation) plotted
against both the numerical (Figure 4(A)) and categorical (Close-Medium-Far,
Figure 4(B)) distance to the locations suffering from red tide during a
particular week.

A

B

C

Figure 4: City-level local Twitter activity and content originality as a
function of proximity to the red tide impacted areas. A: Scatterplot of logged
tweet counts against the numerical distance to the currently affected areas.
B: Box plots of logged tweet counts coming from locations that are at a close,
medium, or far distance from the currently affected areas. C: Proportion of
local retweets as a function of proximity to the red tide impacted areas.

In both analyses, we ended up with a statistically significant negative
relationship (p-values $<0.001$ for both), pointing to local Twitter activity
decreasing exponentially as we move further away from the disaster areas. This
finding corresponds to the effect observed in Kryvasheyeu et al., 2016, where
there was an exponential decrease in Twitter activity as one moved further
from locations impacted by hurricane Sandy, as long as it was within 300mi of
the disaster (beyond that the effect dissipated). While the hurricane Sandy
study was on a nationwide scale, including locations removed from disaster
impact by thousands of miles, our research focused on a relatively narrow
geographical area (only a handful of counties and metros surrounding Tampa
Bay). For a smaller scale like ours, categorizing the distance into three bins
- close ($<25$mi), medium ($25$-$50$mi), far ($>50$mi) from the red tide - led
to a much clearer representation of that negative relationship on Figure 4(B).
In particular, the $95\%$ confidence intervals for pairwise differences in
logged tweet counts for Medium-Close ($\in(-1.53,-0.38)$) and Far-Close
($\in(-2.09,-0.93)$) pointed to increasing statistical and practical
significance of the difference in tweet counts as we move further away from
the red tide-impacted locations.

Another interesting aspect - observed on Figure 4(C) - was the change in
originality of posts as the distance from the red tide increased. With
increasing distance from the impacted areas, an analogous increase in retweet
percentage among all red tide-related tweets was observed, meaning that users
located further away from the impact were more likely to retweet a post,
rather than produce an organic tweet describing their own opinion or
experience of the event. Despite red tides having much longer duration than
hurricanes, this insight was confirmed by work done in Kryvasheyeu et al.,
2016 on hurricane Sandy, where retweet fraction was shown to clearly increase
as location got further removed from the impact. That effect from hurricane
Sandy study appeared stronger in big part due to, yet again, the nationwide
scale of the respective study, with retweet percentages increasing much faster
for locations of $>500$mi away from the impact. Given our focus on a much
smaller area, with distances not exceeding 100mi, the magnitude of the
observed effect is very comparable to that of Kryvasheyeu et al., 2016 for a
similar distance range. This finding proceeds to reinforce the evidence for
potential benefits of accounting for the originality of the content when
evaluating local damage and coordinating relief efforts, be it a lengthy or a
short-lived disaster event. Granted, to fully confirm this finding, and
whether it applies to catastrophes like earthquakes or floods, similar
analysis would need to be carried out for other disasters over the last
decade.

###  3.5 Analyzing sentiments and topical nature of tweets.

Beyond simply using tweet counts as the way to summarize Twitter activity, we
conducted further study into content of the posts, analyzing expressed
sentiments and topical nature of the tweets related to red tide.

The goal of sentiment analysis was two-fold: to provide a Twitter metric of
total sentiment as an improved proxy for the actual local red tide conditions
- with negative values more likely pointing to the red tide affecting the area
– and evaluate public’s situational awareness in regards to the red tide. To
achieve the former, we treated tweets that were reporting on the presence
(absence) of the red tide in the area as negative (positive), regardless of
whether there was any emotion attached to it (see Section 2.5 for more
details), and subsequently utilized cumulative temporal sentiment scores as
the metric to gauge local red tide conditions. On the other hand, to study the
level of situational awareness, we conducted keyword analysis to see if the
public was acknowledging the event’s magnitude and discussing the main effects
and aspects of red tide.

A

B

Figure 5: Twitter sentiment analysis throughout the 2018 Florida red tide
event, by county. A: Word counts of ten most frequent positive and negative
terms encountered in tweets associated with respective locations. B: Weekly
dead fish levels observed, along with per-capita total weekly sentiment from
tweets geo-matched to each county.

On Figure 5(B) you may witness the correspondence between per-capita weekly
total sentiments for geo-matched tweets (mostly in red, due to prevalence of
negative sentiment) and dead fish levels for our five counties of interest.
There are several clear instances of high dead fish levels matching with the
strong negative sentiment in tweets posted that week, especially for the
affected areas of Sarasota, Pinellas and (to lesser extent) Manatee.
Additionally, on Figure 5(A) one may notice the county-level per-capita
frequencies of the ten most mentioned positive and negative words. The
increase in negative terms is evident when moving north (Pasco) to south
(Sarasota), with “dead” and “kill” being the most encountered words, likely
due to describing marine life casualties of red tide. Moreover, the word
”smell” shows a strong increase in usage when moving towards most affected
areas, likely resulting from high dead fish levels in such locations. Lastly,
robust usage of terms like “toxic”, “crisis”, “emergency” indicates public’s
situational awareness pertaining to both the underlying biological process
(toxins killing marine life) and severity of the event (one of the most
intense red tides in Florida history, see Karnauskas et al., 2019). This
finding partially updates and augments the study by Kuhar et al., 2009 on the
public’s perception of risks related to red tide, where it was concluded that
individuals didn’t appear to possess the most updated and adequate information
about Florida red tides and their impacts.

A

B

Figure 6: Multi-level spatiotemporal correlations of local Twitter sentiments
with dead fish levels observed in the area. A: Heatmap-style matrix of
spatiotemporal correlations for per-capita total sentiments of tweets
explicitly geo-tagged in the area with local red tide conditions, calculated
on different levels of locality and time frequency. B: Same as A, but for all
tweets geo-matched to the area.

With all of that said, tweet sentiments failed to outperform basic tweet count
metrics in regards to correlations with local red tide conditions. For
example, looking at the heatmaps of multi-level spatiotemporal correlations
for tweet sentiments with dead fish levels on Figure 6, virtually all
correlations were worse (in absolute value) than their tweet count analogs
(those shown on Figure 3(B-C)). Add on top the fact that tweet counts are much
easier to obtain than sentiment scores, count metrics appear as the best
choice for both efficiency and effectiveness from a pure impact assessment
standpoint. Regardless, it is still curious to note that for Twitter sentiment
metrics, as opposed to counts, all geo-matched sentiments consistently
outperform the explicit geo-tags, by a respectable margin. This aspect is
confirmed on the example of multi-level spatiotemporal correlations for tweet
sentiments with both respiratory irritation levels and K. brevis cell counts
observed locally (Figures 7 and 8 in the Appendix).

In addition to sentiment analysis, we considered categorizing tweet contents
by types of concerns that people expressed at the time of the disaster. Table
1 shows red tide related tweets to contain by far the largest number of unique
words and mentions corresponding to environmental concerns (talking about
beaches, fish, water, climate, pollution, etc). Health issues (citing
respiratory issues, toxicity of red tide, etc) place a distant second in
mentions, and economic concerns (discussing damages to local businesses)
third, with both topics being brought up consistently nonetheless. This
topical ranking falls in line with the results from Li et al., 2015), where
the newspaper coverage of a 2005-2006 K. brevis bloom was found to
predominantly focus on the associated environmental risks (80% of stories
mentioning those), followed by health concerns (48%), and economic impact was
covered the least (30%). In the meantime, local government was also mentioned
consistently, which was likely a function of both the public’s frustration
with the politicians, and the Florida 2018 general election, where the latter
heavily overlapped with this red tide event (see supplementary materials for
further discussion of this point).

Topic | Term Examples | # Unique Terms | # Mentions
---|---|---|---
Environment | beach, fish, water, climate, pollution | 48 | 27,430
Health | respiratory, toxic, sick, harmful | 34 | 8,267
Economy | business, tourist, restaurant, budget | 25 | 4,970
Government | governor, policy, vote, election | 43 | 3,357
Table 1: Most cited concerns on Twitter during Florida red tide event of
2017-2019, by category.

##  4 DISCUSSION

As one of the main conclusions to our study, we discovered that, during a
lengthy disaster event such as Florida red tide of 2017-2019, Twitter activity
can be used to gauge community perceptions of localized coastal impacts, in
both comprehensive and discrete spatiotemporal scales. One can consider
several distinct tweet metrics - counts or sentiments of tweets that were
either explicitly geo-tagged or geo-matched to the area in any way - for
improved representation of the relative damage to respective areas at each
moment in time.

When linking red tide impacts of several metro areas of interest on the
Florida Gulf coast to community perceptions, we used K. brevis cell counts
observed in local waters, along with dead fish and respiratory irritation
levels reported for local beaches. A clear hierarchy of red tide impacts from
spatial standpoint emerged, with southern counties (Sarasota, Manatee,
Pinellas) getting most affected, while locations to the north (Hillsborough,
Pasco) managed to avoid the disastrous effects. Per-capita counts of tweets
geo-matched to each respective area served as a great proxy for the local
damage received, improving on gross tweet totals. Stronger correlations were
obtained by only utilizing the explicitly geo-tagged tweets, which are much
more reliable than the tweets assigned to a metro area utilizing user
geoprofile location information alone (similar observation on lower
reliability of geoprofile matches was made in Chien et al., 2017).

While several studies have been done previously on the use of Twitter to both
gauge the impacts and inform disaster response (Kryvasheyeu et al., 2016,
Pourebrahim et al., 2019, Chen et al., 2020), most of those pertained to
instantaneous or short-lived disasters that typically don’t last longer than a
week. Red tide, on the other hand, can last several months at a time,
henceforth introducing a considerable temporal component alongside an already
existing spatial dimension. To the best of our knowledge, there has only been
one study conducted using Twitter data in regards to a similar disaster event
\- Mascareño et al., 2020, investigated Twitter dynamics throughout the
duration of the 2016 red tide event at Chiloe Island, Chile. With that said,
their work focused predominantly on studying social interrelationships across
multiple user groups and how they changed throughout the disaster event,
without putting as much emphasis on correlating localized Twitter activity
with the actual red tide impacts observed in the area.

In the specific case of 2017-2019 Florida red tide event, we witnessed algal
blooms with varying coastal intensities throughout the duration of roughly
eight consecutive months, warranting special attention to be paid to
understanding temporal dynamics of red tide development and coastal community
responses via Twitter activity over time. When looking at the entire Tampa Bay
surrounding area (Figure 2), strong correlations between weekly per-capita
tweet counts and corresponding local conditions were observed, with explicitly
geo-tagged tweets serving as the most reliable proxy, while also presenting
the most efficient metric due to simplicity of data collection in real time.

To draw a comparison with previous studies on social media usage during short-
lived disaster events, Florida red tide temporal dynamics are somewhat similar
to those of hurricanes, floods or earthquakes solely in the short windows
during peak impacts, where spikes in Twitter activity correspond to spikes in
respective conditions (dead fish and respiratory irritation levels, K. brevis
cell counts), as was shown on Figure 2. Nonetheless, there are still several
critical distinctions. First, even the duration of a single spike in red tide
conditions is much longer than that of instantaneous disaster impacts. To
observe this particular aspect, see daily dynamics of the red tide resurfacing
in Sarasota county during September of 2018 on Figure 9 in the Appendix, and
compare these to the timelines of hurricane Sandy (see Preis et al., 2013,
Kryvasheyeu et al., 2016) or hurricane Harvey flooding (see Zou et al., 2019,
Chen et al., 2020), where all the impacts took place over less than three-to-
four days. On a daily scale, temporal characteristics of instantaneous
disasters are much more straightforward, exhibiting clear-cut spikes, compared
to a more gradual and non-monotonous trajectory of a red tide. This confirms
the results seen on Figure 3, with Twitter activity serving as a much more
consistent proxy for longer-term (e.g. weekly, three-day) cumulative red tide
conditions as opposed to finer time frequencies (e.g. daily), although still
demonstrating respectable daily correlations at county level. Secondly, the
initial sighting of red tide in June - despite demonstrating sizeable dead
fish and respiratory irritation levels in Sarasota area - was accompanied by
only a slight increase in relevant tweets (see Figure 2), which is something
one would rarely expect from an onset of disasters like hurricanes, floods or
earthquakes. This likely goes to show the difference between catastrophes in
terms of damage severity specifically to the human population, where red
tides, while potentially posing huge threats to certain businesses and
carrying several negative health impacts, would not be as devastating as the
latter, which could lead to extreme property damage and numerous human
casualties.

Another interesting observation was that of tweet counts dissipating at faster
rates over time compared to the red tide intensity, which is evident on Figure
2, especially after the third peak in September. This may have to do with a
psychological phenomenon of ”compassion fatigue” (Jacobson, 2006, Adams et
al., 2008, Nakagawa et al., 2019), pointing to people, especially younger
adults, losing interest in the disaster event if a considerable amount of time
passes since its inception, regardless of whether the event is still
persisting by that point. Because of longer duration compared to catastrophes
like hurricanes, earthquakes, floods, compassion fatigue could have an even
stronger effect on lowered Twitter activity several weeks after the first
sighting of red tide in the area. With that said, this psychological phenomena
is most applicable to people not affected by the disaster directly, e.g.
living further away from the impacted areas. As opposed to the nationwide
scale of the study done in Kryvasheyeu et al., 2016, our research focused
specifically on the directly impacted locations, hence likely providing a
balancing counter-effect to that of compassion fatigue. All of that would
require a specific modeling adjustment when considering various Twitter
metrics and their reliability as a proxy for the actual local red tide
conditions, and is intended to be a subject of future research.

When studying the correlations on a county level, there was a slight
deterioration in strengths of correlations between red tide conditions and
Twitter activity in those more fine-grained areas. That is only natural, due
to some locations that didn’t experience the actual effects of red tide (e.g.
Hillsborough) still actively posting about it either in anticipation of the
potential impact, or simply reporting on the developments in the neighboring
counties. Despite that, utilizing weekly counts of tweets explicitly geo-
tagged in the area served as a great barometer of local beach conditions,
showing much less activity in the unaffected areas compared to using all geo-
matches, while retaining strong temporal correlations with spikes of red tide
in the impacted locations.

In an effort to obtain actionable insight on a more hyper-local scale (e.g.
city- or ZCTA-level) and finer temporal scale (e.g. daily instead of weekly),
we studied spatiotemporal correlations of per-capita Twitter activity with
local red tide conditions across several localization levels and time
frequencies. An expectedly steady decrease in correlation strength was
observed when going down to more fine-grained locality levels and higher time
frequencies, but there was still considerable correspondence for total and
county-level data across all temporal scales considered. City-level
correlations were respectable for per-capita explicit geo-tag counts if
monitored weekly or every three days. Posing curious research questions on
their own, these aspects may also point to Twitter as a crowd-sourcing tool to
augment the already existing practices of place-based monitoring for the
actual red tide conditions.

Another critical aspect of using local tweet counts was their strong
association with the proximity of tweet location to the areas being impacted
by red tide at the time. We demonstrated an exponential decrease in per-capita
Twitter activity (on city-level) as the distance to the affected locations
increased. Moreover, we also detected a decrease in originality of the posts,
meaning the higher percentage of retweets, the further one moved from the
affected locations. Looking solely at areas within a 100 miles of disaster
impacts (the range of our localized study), our results confirmed the findings
from Kryvasheyeu et al., 2016 for analogous distances.

Emotion expressed in the posted tweets might provide us with information that
basic tweet counts would not be able to convey. In particular, it could help
with better estimation of red tide’s arrival and departure from a particular
locale, manifesting in prevalence of negative and positive messaging
originating from the area, respectively. For those reasons we conducted
sentiment analysis and utilized weekly total tweet sentiment as an alternative
to weekly tweet counts. When using temporal sentiment as a metric to
approximate red tide development on a county level, we witnessed strong
(expectedly negative) correlations with the local red tide impacts, especially
when considering all geo-matched tweets. Moreover, polarized keyword analysis
was indicative of public’s high situational awareness, grasping the origins of
red tide and severity of the disaster. With that said, when looking at
correlations between tweet sentiments and local beach conditions on multiple
spatiotemporal levels, virtually none of them were better than those for tweet
count metrics. Considering the extra layer of complexity and uncertainty added
by estimating tweet sentiments (see Section 2.5) as opposed to simply
obtaining counts, it makes the latter a clear choice for most optimal, timely,
red tide impact assessment.

Besides sentiment analysis, categorization of tweet content by types of
specific concerns being expressed by people was also conducted. Do the locals
and tourists mostly cite environmental issues? Or do they primarily refer to
economic or health concerns? Ability to answer such questions can in turn have
management implications for improving communication about red tide, both
general education and real time conditions, and for prioritizing management
actions, such as marine debris cleanup, targeted assistance to waterfront
businesses and their employees, or distribution of personal protective
equipment (PPE). We showed that environmental issues are brought up the most,
with health and economic concerns cited considerably less, albeit still
constituting popular topics.

Among threats to validity we should mention that the collected localized
Twitter data is not to be fully relied upon as an exact representation of
general population living in respective areas. First, it clearly eliminates
anyone without a Twitter account, with the reasons ranging all the way from a
person being from low-income family without access to technology, to having
certain physical or mental disability, to simply a matter of preference to
communicate via other social media outlets (Facebook, Instagram, etc).
Moreover, in Mislove et al., 2011 it was shown that Twitter tends to
significantly underrepresent sparsely populated areas, while also skewing
male. These issues have been brought up in the literature before (Morstatter
et al., 2013), and while certain papers have worked on approaches to
circumvent them (Miranda Filho et al., 2015, Longley and Adnan, 2016), it
mostly remains a disclaimer warning about limitations of using Twitter data.
Lastly, to simplify the data collection and text analysis, in our particular
study we focused solely on tweets written in English, which automatically
excludes anyone with limited English proficiency or those simply preferring to
communicate in a different language. As studied in Hong et al., 2011, Twitter
is supremely diverse internationally from linguistic standpoint, and while
Twitter activity in Florida is likely to lean heavily English, according to
$worldpopulationreview.com$ Spanish is spoken by $21.8\%$ of Floridians, along
with Florida having $25.58\%$ of Hispanic population (6th largest in the
United States). Hence, our study could be augmented in the future by
accounting for tweets sent in Spanish, along with applying Spanish-specific
sentiment analysis techniques as discussed in Pla and Hurtado, 2014).

All-in-all, our analysis of over 18,000 tweets geo-matched to Florida’s Gulf
coast during the 2017-2019 red tide event showed very strong localized
correlations between red tide conditions and public response in terms of the
number and sentiment of tweets, along with the percentage of retweets. While
correlations gradually dissipated upon considering more localized scales and
higher time frequencies, they were still respectable for spatiotemporal levels
that would allow for a potentially timely response (e.g. county-level on daily
basis, or city-level every three days), in general making Twitter a solid
proxy of red tide impacts in the area. Provided how reliable the explicitly
geo-tagged tweets turned out to be for that purpose, and given their relative
ease of data collection (compared to user geoprofile matches, or some of the
physical disaster impact measurements), local governments and planning
agencies could integrate Twitter into their operations for a relatively low
fee and in a rather seamless fashion during disaster events. Creating software
pipelines to collect relevant tweets on a daily basis (using SQL [37] and any
scripting language), while also calculating counts, evaluating sentiments,
categorizing content by concerns being expressed (using R [45], Python [44]),
creating useful visualizations (using Tableau [51], for example) is a feasible
task to be carried out for informing response to the disaster as it unfolds.

For future work, we plan on conducting further analysis into different Twitter
user groups and individual accounts, to characterize social influencers and
types of messaging during red tide, in an attempt to identify strategies for
improved public communication. Which account types - media, political figures,
government bodies, regular citizens - and what kind of messages generate the
most activity on Twitter (via ‘likes’, retweets, replies)? Also, how is the
messaging and public’s Twitter reaction affected by any other major events
happening concurrently with the red tide period (e.g. political elections,
other environmental disasters, etc)? It will be falling more in line with the
research work done on public’s perception of red tide (Kuhar et al., 2009,
Nierenberg et al., 2010, Kirkpatrick et al., 2014) and social dynamics
throughout the event (Mascareño et al., 2020). Moreover, as an alternative to
relying solely on geo-tag and user geoprofile information, we plan on
utilizing tweets matched to a particular location via explicit mention of that
locale in the tweet content. This might potentially open doors for an even
better local impact assessment, along with the ability to gauge the level of
interest and study differences in messaging about the event across the nation.
Lastly, when conducting sentiment analysis, traditionally one treats tweet
replies similarly to the original posts or retweets - as a standalone message.
In the future, we could work on incorporating the contents of the original
tweet to provide richer context when evaluating sentiment expressed in the
reply message.

## SUPPLEMENTARY MATERIALS

$Supplement.pdf$ file contains supplementary materials referenced in the main
manuscript.

To promote additional research on social media applications to inform
management response to HAB events, as supplement to this article we provide
the complete library of tweets being analyzed in this study, the custom
lexicon used for sentiment analysis, and vocabulary of terms for concern types
(environment, economy, health, government).

Additionally, a more extensive set of data and source code can be found at
https://github.com/UsDAnDreS/Using-Localized-Twitter-Activity-for-Red-Tide-
Impact-Assessment.

## ACKNOWLEDGEMENTS

This project was supported in part through the Tampa Bay Environmental
Restoration Fund (TBERF) and a USEPA Region 4 Cooperative Agreement Grant to
the Tampa Bay Estuary Program (CE-00D89319-0). TBEP funding for this project
stems from USEPA Section 320 Grant Funds, and the TBEP’s local government
partners (Hillsborough, Manatee, Pasco, and Pinellas Counties; the Cities of
Clearwater, St. Petersburg, and Tampa; Tampa Bay Water; and the Southwest
Florida Water Management District) through contributions to the TBEP’s
operating budget. The Sunrise Rotary Foundation of Sarasota also supported
this project through a grant to the Science and Environment Council of
Southwest Florida. The authors are grateful to Dr. Tracy Fanara for providing
the tabular beach conditions data as reported on visitbeaches.org, Mote Marine
Laboratory’s Beach Conditions Reporting System [31].

##  5 APPENDIX

###  5.1 Tweet totals vs per-capita.

Red Tide Condition Counts of | Explicit Geo-Tags | All Geo-Matches
---|---|---
|  K. Brevis Cell Counts
---
Dead Fish Levels
Respiratory Irritation Levels
| Total | Per-Capita
---|---
0.47 | 0.50
0.73 | 0.79
0.63 | 0.72
| Total | Per-Capita
---|---
0.43 | 0.65
0.47 | 0.72
0.39 | 0.66
Table 2: Correlation of weekly red tide conditions with weekly counts of explicitly geo-tagged and all geo-matched tweets, total and per-capita, on the local county-level. For Tampa Bay surrounding area, including the following counties: Pasco, Hillsborough, Pinellas, Manatee, Sarasota. Red Tide Condition Counts of | Explicit Geo-Tags | All Geo-Matches
---|---|---
| K. Brevis Cell Counts
---
Dead Fish Levels
Respiratory Irritation Levels
| Total | Per-Capita
---|---
0.23 | 0.25
0.58 | 0.63
0.50 | 0.57
| Total | Per-Capita
---|---
0.22 | 0.34
0.39 | 0.60
0.32 | 0.55
Table 3: Correlation of daily red tide conditions with daily counts of explicitly geo-tagged and all geo-matched tweets, total and per-capita, on the local county-level. For Tampa Bay surrounding area, including the following counties: Pasco, Hillsborough, Pinellas, Manatee, Sarasota.  Red Tide Condition Counts of | Explicit Geo-Tags | All Geo-Matches
---|---|---
| Dead Fish Levels
---
Respiratory Irritation Levels
| Total | Per-Capita
---|---
0.39 | 0.54
0.35 | 0.48
| Total | Per-Capita
---|---
0.23 | 0.47
0.20 | 0.43
Table 4: Correlation of weekly red tide conditions with weekly counts of
explicitly geo-tagged and all geo-matched tweets, total and per-capita, on the
local city-level. We include 57 cities in the Tampa Bay surrounding area,
including the following counties: Pasco, Hillsborough, Pinellas, Manatee,
Sarasota.

###  5.2 Multi-Level spatiotemporal correlations of local Twitter activity and
with local respiratory irritation levels and K. brevis cell counts.

A

B

C

D

Figure 7: Multi-level spatiotemporal correlations of local Twitter activity
with respiratory irritation levels observed in the area. A: Heatmap-style
matrix of spatiotemporal correlations for per-capita counts of tweets
explicitly geo-tagged in the area with local red tide conditions, calculated
on different levels of locality and time frequency. B: Same as A, but for per-
capita counts of all tweets geo-matched to the area, hence also including
those matched by user’s geoprofile information. C: same as A, but for per-
capita total tweet sentiments of explicitly geo-tagged tweets. D: same as B,
but for per-capita total tweet sentiments of all tweets geo-matched to the
area.

A

B

C

D

E

Figure 8: Multi-level spatiotemporal correlations of local Twitter activity
with K. brevis levels observed in the area. A: Map of K. brevis sample
locations, with their county assignments. B-C: Heatmap-style matrices of
spatiotemporal correlations for per-capita counts of explicitly geo-tagged
tweets (B) and all tweets geo-matched to that area (C). D-E: Heatmap-style
matrices of spatiotemporal correlations for total sentiments of explicitly
geo-tagged tweets (D) and all tweets geo-matched to that area (E).

###  5.3 Example of daily red tide dynamics

Figure 9: Daily local red tide conditions and tweet counts in Sarasota during
September 2018. Local K. brevis cell count data was not available for every
single day, hence was excluded from the graphic.

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