Quantifying societal emotional resilience to natural disasters from geo-
located social media content

Krishna C. Bathina1*, Marijn ten Thij1,2,3, Johan Bollen1,

1 School of Informatics, Computing, and Engineering, Indiana University

2 Delft Institute of Applied Mathematics, Delft University of Technology

3 Department of Data Science and Knowledge Engineering, Maastricht University

* Corresponding author<EMAIL_ADDRESS>
## Abstract

Natural disasters can have devastating and long-lasting effects on a
community’s emotional well-being. These effects may be distributed unequally,
affecting some communities more profoundly and possibly over longer time
periods than others. Here, we analyze the effects of four major US hurricanes,
namely, Irma, Harvey, Florence, and Dorian on the emotional well-being of the
affected communities and regions. We show that a community’s emotional
response to a hurricane event can be measured from the content of social media
that its population posted before, during, and after the hurricane. For each
hurricane making landfall in the US, we observe a significant decrease in
sentiment in the affected areas before and during the hurricane followed by a
rapid return to pre-hurricane baseline, often within 1-2 weeks. However, some
communities exhibit markedly different rates of decline and return to previous
equilibrium levels. This points towards the possibility of measuring the
emotional resilience of communities from the dynamics of their online
emotional response.

## Introduction

Hurricanes are devastating to their affected communities. The US alone has had
over 30 hurricanes in the last 20 years, whose aggregated effects involving
destroyed infrastructure and loss of life are difficult to estimate. This is
all the more true for a hurricane’s social and psychological effects on well-
being. Some communities suffer long lasting and immeasurable damage to their
social fabric, even after extensive government and community intervention, and
reconstruction of infrastructure.

Much of the attention surrounding hurricanes has focused on physical issues
and logistics, such as preparing and protecting infrastructure, and the
prevention of loss of life, but the social and psychological consequences can
be equally relevant. For example, after Hurricane Katrina, New Orleans lost
53% of its population, leaving entire wardens depopulated [1]. In addition,
the disaster had a profound effect on the well-being of the New Orleans
community which was not fully captured by subsequent attempts to assess the
economic damage [2]. Whereas some areas did make a partial recovery and
benefited from the construction of new public service systems, others
struggled to return to their pre-hurricane situation.

The mental and psychological effects of natural disasters may play an
important role in the recovery of a community after a natural disaster. This
important facet of recovery is difficult to quantify other than painstakingly
conducting population surveys and interviews at regular intervals [30].
Unfortunately, this approach presents considerable logistic challenges with
respect to scale, costs, and accuracy.

Here, we look to social media as a potential source of complementary real-time
proxy data to model changes in geo-located, regional well-being. We measure
the collective emotional response and subsequent recovery of geographically
defined communities from large scale Twitter data, in the period before,
during, and after a hurricane to estimate its resilience to external shocks at
high temporal resolution.

We do so from the perspective that resilience is a characteristic of systems
that can resist external shocks, either by returning to their previous
equilibrium or by developing an adaptive response to the external driver. In
this work, we characterize the emotional resilience of a community as its
“time to recovery”, namely its ability to resist the negative effects of a
natural disaster and return to baseline sentiment afterwards. By measuring the
dynamics of changing community sentiment from social media before, during, and
after a natural disaster, we attempt to model the characteristics of its
emotional resilience.

Our work builds upon the foundation set by previous research about social
media and well-being. Jaidka et al. [31] used 1.53 billion geo-located tweets
in the United States and showed that word-level methods of sentiment analysis
provide good estimates for county-level subjective well-being when compared to
Gallup survey measures. Fan et al. [32] used Twitter timelines from 74,487
users and analyzed tweets before and after an explicit report of a positive or
negative emotion showing that reports of a positive emotion were preceded by a
sharp increase in sentiment followed by a shallow drop to normal levels while
reports of negative emotions showed the opposite pattern: a slow buildup of
negative sentient followed by a sharp drop and a slow return to baseline
levels. Valdez et al. [33] used an analogous method to study the effects of
the COVID-19 pandemic on sentiment in the US from January to April.

Our work also builds upon previous research in social media analysis and
community response. Bollen et al. performed sentiment analysis on Tweets from
2008 and found that changes in sentiment correlated with major and notable
events that year [43]. Bollen et al. and Yang et al. both studied Twitter data
and found sentiment to be correlated to stock market returns [44, 45].
Recently, there have also been many papers studying Twitter sentiment during
the COVID-19 pandemic, specifically focusing on the the effects of lockdowns
and restrictions [46, 47, 48, 49].

Much work has been done surrounding the use of online social media to study
hurricanes. Arnold et al. showed that the volume of tweets and hashtags
increase as a function of the intensity of hurricanes [36]. Stark et al.
showed that the frequency of tweets related to Hurricane Irma peaked as the
hurricane hit the region and that the level of concern varied by both region
and gender [38]. This was further developed by Neppalli et al., who showed
that the sentiment also varies by distance to the disaster [39, 40]. Zou et al
used tweets to highlight the disparity of social media usage during Hurricane
Harvey [37]. Communities with better social and geographical conditions were
shown to send more disaster related tweets.

Here we build upon these works by examining the emotional response of
geographical communities to hurricanes as an instance of natural disasters
that affect community well-being. We posit that hurricanes form a good case-
study to assess community recovery and resilience for the following reasons:

Unpredictability:

Hurricanes can vary unpredictably in terms of their specific magnitude and
geographic scope, making it difficult for communities to anticipate their
social and economic effects.

Synchronization:

Hurricanes affect all residents of a community in a geographic location at a
discrete point in time, allowing analysis of sentiment dynamics centered on a
particular point in time.

In this work, hurricanes serve as natural experiments of how natural disasters
may drive changes in the well-being of disparate communities. In fact, the
well-being of entire communities can be observed before, during, and after
hurricanes in order to study and model the community’s resilience to
significant natural disasters. To this end, we examine the emotional response
of four geographically distinct communities on social media over three
Atlantic hurricane seasons in the context of 4 hurricanes: Irma (Florida),
Harvey (Texas), Florence (Carolinas), and Dorian (Carolinas, Florida, and
Alabama). We downloaded geo-located tweets from the Hurricane Formation
(tropical cyclogenesis) to Hurricane Dissipation (tropical cyclolysis) for
each hurricane. We define “US Landfall” as the day when the hurricane makes
landfall in the region of interest. We also define the following time periods:

Before:

Hurricane formation to 5 days before US Landfall

During:

5 days before US Landfall to 5 days after US Landfall

After:

5 day after US Landfall to Hurricane Dissipation

As shown in Fig 1, after recording the hurricane location and time period, we
use the Observatory on Social Media (OSoMe) [26] and the Twitter API to
download tweets that were posted in the affected region. We then extract the
valence of the resulting sets of geo-located tweets using a sentiment analysis
tool to study the pattern of changing Twitter sentiment during the hurricane.
Next, we bootstrapped the calculated sentiment and then built exponential
fits. Finally, to discover terms that are characteristic to the changes in
sentiment, we study the frequency of VADER lexicon terms before, during, and
after the hurricane landfall.

We analyzed changes in Twitter sentiment in the regions affected by the set of
selected hurricanes, including 2 control regions that were not directly
affected by a hurricane or where the specific hurricane did not make landfall.
We modeled the trend of changing sentiments before, during, and after each
respective hurricane. Our results indicate that in all cases, Twitter
sentiment in each affected region slowly decreases as the hurricane approaches
indicating widespread anticipation in the respective populations, a large drop
right after US Landfall reflecting the effects of the hurricane making
landfall, and a rapid returns to previous levels over the course of about 1-2
weeks. Our results indicate that Twitter sentiment does reflect changes in
population subjective well-being as a result of a discrete negative event and
that the rate of change over time reflects community-specific response rates
which may be indicative of emotional community resilience.

Fig 1: An outline of our methodology. A) We retrieve bounding box coordinates
of the US Landfall of 4 major hurricanes in 5 regions. B) We then download the
geo-located tweets from the affected region during the hurricane using the
Twitter API. C) Using sentiment analysis tools, we quantify the valence of the
tweets for. D) We bootstrap the sentiment and then build exponential fits. E)
Finally, we look at the change in frequency of VADER lexicon terms before,
during, and after the hurricane landfall.

### Background on chosen hurricanes

Hurricane Irma, originating from Cape Verde, was the first Category 5
hurricane in 2017. In September, the hurricane formed in the Caribbeans and
hit Puerto Rico and Florida. Because of advanced warning systems, many of the
Caribbean islands were prepared for evacuations. Florida declared a state-of-
emergency causing the evacuation of 6.5 million Floridians and a reroute of
all inbound flights. Approximately 73% of the state was left without power and
65,000 structures were destroyed [3, 4]. The damage was estimated to be at 50
billion dollars with 84 deaths [5]. Hurricane Harvey was a Category 4
hurricane that formed near Barbados in August 2017. It moved northwest towards
Mexico and hit lower east Texas. It tied Hurricane Katrina for causing the
most economic damage, evaluated at 125 billion dollars [6]. An early warning
storm watch, multiple state-of-emergencies, and mandatory evacuations were
declared to minimize loss but over 100,000 homes and vehicles were still
destroyed [7]. There were electricity outages across the path of the hurricane
and 103 deaths in Texas alone.

Hurricane Florence, originating from Cape Verde, hit the Carolinas in
September 2018. It was the first major hurricane of the 2018 season causing a
state-of-emergency, an overnight curfew, and mandatory evacuations as rains
reached a record of 34 inches in North Carolina. The deluge caused large
sections of the interstate to close down and many homes were completely
flooded. There were over 50 confirmed deaths and estimated damages of over 24
billion dollars [8, 9].

Hurricane Dorian formed near Senegal in August 2019 [10]. The hurricane moved
west and missed a majority of the Caribbean islands. Unfortunately, it was one
of the most destructive storms to hit the Bahamas. The slow moving nature of
the hurricane caused immense damage, leveling a majority of islands it came in
contact with. The hurricane then moved north of Florida and landed in the
Carolinas on September 5th with winds up to 90 mph [11]. Both Florida and the
Carolinas were under a state-of-emergency due to flooding and high winds [12,
13]. Both areas had 3 deaths total but the Carolinas had over 160,000
buildings with no power [14, 15, 16].

Before the hurricane hit, the president of the United States Donald Trump
stated that “Alabama, will most likely be hit (much) harder than anticipated”
on his personal Twitter account and released a press release with the same
information on the White House official Twitter account. This was refuted by
the National Weather Service and the hurricane did not affect Alabama, but the
official statement by President Trump was never refuted [17, 18, 19].

Fig 2 shows the trajectories of each hurricane as well as the geographic
bounding boxes corresponding to the affected areas from which tweets were
sampled. Note that we analyze Hurricane Dorian’s effect on 3 distinct regions
as a control: (1) Florida which it skimmed but where it did not make landfall,
(2) Alabama which it did not affect at all, but where, due to misinformation
on social media, some of the public may have believed they could be affected,
and (3) the Carolinas where the hurricane did make landfall.

Fig 2: The Trajectories of the Hurricanes and the Bounding Box of the Tweet
Samples. The east coast of the United States gets hit with many yearly
hurricanes because the warmth of the water alongside the coast provides
thermal energy to incoming hurricanes. Hurricane Harvey came up from the south
and hit south-east Texas, especially the Houston metropolitan area. Hurricane
Irma also came up from the south but landed in Florida. Hurricane Florence
came across the east coast and landed in the Carolinas. Hurricane Dorian
skimmed Florida but also made landfall in the Carolinas. Alabama was not
affected by Dorian even though an official memo from President Trump claimed
they would get hit.

## Data and methods

### Data

We use social media for this analysis because of its pivotal position in
public discourse. According to the Pew Research Center, 84% of US adults aged
18-29 used social media in 2021. Among those older than 65 years, 45% report
that they are social media users. Even though Facebook has the most users (69%
of US adults), we specifically use Twitter due to its microblogging nature; it
is most commonly used to discuss daily routine and reporting news [42] while
still representing 23% of all social media users. This is particularly
important for our analysis as we are interested in people’s evolving reports
of their personal state in the context of a hurricane.

We collected geo-located Twitter data that was posted between 2016 to 2019
from (OSoMe), which provides a 10% random sample of all daily tweets posted
worldwide. Here we only retained tweets whose geo-location data matched the
bounding boxes corresponding to the respective hurricane-affected regions
shown in Fig 2. The S1 Table shows a summary of the raw data. In our
subsequent analyses, we excluded all retweets and non-English tweets, as
identified by the Twitter API that provides this information. The number of
analyzed Tweets for each hurricane and region are shown in Table 1. A list of
Tweet IDs for tweets used in this analysis can be found at
https://github.com/kbathina/resilience-to-natural-disasters. All data from
OSoMe and the Twitter API were collected in accordance with their respective
terms and conditions.

Fig 3 shows the weekly and daily frequency of filtered tweets. The regions
affected by hurricanes Irma, Harvey, and Dorian in the Carolinas show an
increase in activity around their US Landfalls, indicating that user activity
increased during the hurricane and that Twitter may thus be suitable to gauge
geo-located hurricane-related communications.

Table 1: Hurricane Data

Hurricane | Location | Number of Tweets
---|---|---
Irma (2017) | Florida | 823537
Harvey (2017) | Houston | 511229
Florence (2018) | Carolinas | 500332
Dorian (2019) | Carolinas | 7921
Dorian (2019) | Florida | 20646
Dorian (2019) | Alabama | 2760

The name, year, and location of each hurricane followed by the raw number of
tweets downloaded from OSoMe.

Fig 3: Daily tweet frequencies for Hurricanes Irma, Harvey, Florence, and
Dorian. All dates are rescaled to the number of days since the US Landfall the
area. In the case of Hurricane Dorian in Alabama, US Landfall is when
President Trump predicted the hurricane would hit.

### Sentiment analysis

Sentiment analysis refers to a collection of supervised or unsupervised
natural language processing technique that maps the content of a text to a
rating or classification of its sentiment or emotions.

To assess the sensitivity of our analysis to the choice of sentiment analysis
tool, we subject each tweet to two different sentiment analysis tools. First,
we use the Valence Aware Dictionary for sEntiment Reasoner (VADER), a tool
highly suitable for Twitter content [20] using a large lexicon that was
developed through crowd-sourcing sentiment rating. VADER matches the terms in
a tweet to an extensive lexicon of 7,517 terms, applying a set of grammatical
rules that acknowledge common use of punctuation, capitalization, modifiers,
and negation that can affect a tweet’s sentiment, e.g. “I am extremely NOT
pleased!!!!”. VADER returns a Tweet’s Valence rating on a scale from -1
(negative valence) to +1 (positive valence). Because VADER does not natively
differentiate between the absence of a match and neutral (zero) sentiment, we
modified VADER to output 0 for neutral sentiment and “NULL” for no matching
words.

Second, we compare VADER sentiment values for each tweet with those generated
by the Linguistic Inquiry and Word Count (LIWC) [28] tool. Using a predefined
dictionary, LIWC returns a positive (LWPR) and negative (LWNR) word
ratio—i.e., the number of positive or negative words divided by the total
number of words in the tweets. Besides calculating different values, LIWC and
VADER use different sentiment lexicons. In an earlier large-scale bench-
marking test for a random sample of tweets, VADER was shown to outperform LIWC
in both macro-F1 score (57.89 vs, 35.95) and coverage (82.20% vs. 61.82%)
respectively [29].

Hurricanes invoke bleak imagery and are generally spoken about in a negative
context. Any significant changes in sentiment could therefore be an artifact
of Twitter language shifting towards the topic of hurricanes (i.e., greater
prevalence of hurricane-related words) and sentiment analysis reflecting their
inherently negative sentiment. However, we checked to see if a set of
hurricane-related words from our analysis, such as “hurricane”, “tornado”,
“storm”, “rain”, “deluge”, and “flood”, exist in the VADER or LIWC dictionary.
Both dictionaries did not contain any of these words, so our sentiment results
are likely not skewed by the presence of topical hurricane-related words in
the Twitter data set.

### VADER lexicon usage

To characterize the emotional response of a region to a hurricane, we look for
the VADER lexicon words, i.e. words with a valence loading, that are most
characteristic of changes in sentiment during a hurricane. In particular, we
look for VADER lexicon terms with the largest change in frequency, positive or
negative, during the hurricane relative to their frequency of usage before and
after.

We first grouped the tweets of each region into three time periods (before,
during, and after the hurricane) as defined in the Introduction. We then
tokenized each tweet and removed words that are not included in the VADER
lexicon because we are only interested in words that affect our observation of
tweet valence. Next, we calculated the TF-IDF of each word during each time
period, expressing how specific the word is to the time period. We then
z-score normalize the TF-IDF values of each word using the mean and standard
deviation across the 3 time periods, resulting in a $3\times 1$ vector of
normalized TF-IDF scores for each word. As a result, each word’s vector
contains a before, during, and after z-score normalized TF-IDF score that
expresses the degree to which the TF-IDF value of the term for a period
deviates from the 2 other periods, i.e. how well does the term characterize a
particular time period relative to the 2 other periods.

Using the words as labels and the $3\times 1$ TF-IDF vectors as features, we
ran a k-means clustering with $k=2$ clusters. We observe that for each region
the same two clusters of patterns emerge; a concave shape (down, up, down)
representing an increase in the z-score normalized TF-IDF scores during the
hurricane and a convex shape (up, down, up) representing a decrease in the
normalized TF-IDF scores during the hurricane.

For each region and hurricane, we have a set of TF-IDF vectors of words
grouped into these two clusters (up during the hurricane vs. down). In order
to determine if there was a change in the importance of VADER words during the
hurricane, we compare the rank order of the words between time periods using
the z-score normalized TF-IDF value. One such comparison is the Kendall rank
correlation coefficient ($\tau$), which is a measure of correlation, or
similarity, between two rankings. A high correlation corresponds to a nearly
similar ranking order while a low correlation represents a dissimilar ranking.

### Bootstrapping and null-model

The volume of tweets and the Twitter user sample changes with time. These
differences can lead to variance and estimation errors between time windows,
complicating comparisons of sentiment over time. Therefore we bootstrap our
estimates of sentiment values to obtain 95% confidence intervals of the mean
sentiment at a given time as follows. Given a timeline of tweets split into
$t_{k}$ time units with $N_{k}$ tweets each, $\mathcal{O}(t_{k})$ is the set
of observed sentiment z-scores at time $t_{k}$ for tweets that have a
calculated sentiment (see subsection “Data”). To calculate if a day $k$ is
statistically significantly high or low, we first bootstrap by calculating the
mean sentiment of a random sample with replacement of $\mathcal{O}(d_{k})$ of
size $N_{k}$. This bootstrapping process is repeated $B=10,000$ times for
every day,
$\bar{\mathcal{O}}\text{*}_{1}(d_{k}),\bar{\mathcal{O}}\text{*}_{2}(d_{k}),...,\bar{\mathcal{O}}\text{*}_{B}(d_{k})$.

To compare observed sentiment against a random sample of tweets, we also build
a daily null-model, $\mathcal{N}(d_{k})$, by only randomly sampling (with
replacement) from tweets in $\mathcal{O}(d_{k-28})$ to $\mathcal{O}(d_{k-1})$
and at the same day of the week as $d_{k}$. The null-model is also
bootstrapped $B$ times per day,
$\bar{\mathcal{N}}\text{*}_{1}(d_{k}),\bar{\mathcal{N}}\text{*}_{2}(d_{k}),...,\bar{\mathcal{N}}\text{*}_{B}(d_{k})$.

Since the null-model indicates the expected sentiment for a random sample of
tweets, we compare observed sentiment values to this null-model to gauge their
significance. We do this by first calculating the 2.5th, 50th, and 97.5th
percentile of the bootstrapped values of each day for both the observed data
and the null model. Days in which the 97.5th percentile of the observed
bootstrapped sentiment is less than the 2.5th percentile of the null model
bootstrap indicates a statistically significant decrease in sentiment.
Similarly, days in which the 2.5th percentile of the observed bootstrapped
sentiment is higher than the 97.5th percentile of the null model bootstrap
indicates a statistically significant increase in sentiment.

### Valence fits

To estimate the rate of sentiment decay and recovery surrounding each
hurricane, we fit several models to each region’s sentiment over time. We
grouped Twitter sentiment (each tweet has one valence value) in 12 hour
windows and then calculated the mean tweet valence value for each window. We
then fit 2 curves; one descending curve ending at the minimum sentiment and
one ascending curve starting from the minimum sentiment onwards [32]. The sum
of squared errors (SSE) for each model are shown in the S6 table. The
exponential fit in the form $E=ae^{bt}+c$ where $t$ is the time in days, $E$
is the expected sentiment, and $e$ is the natural exponential had the lowest
SSE.

## Results

We first look at the sentiment and significant changes over the period under
consideration (2017 to 2019) when Hurricanes Harvey, Irma, Florence, and
Dorian affected the US. Fig 4 shows the z-score normalized VADER sentiment
(subtracting the mean and divided by the standard deviation of the time series
for that period) for each region. In each instance of US landfall, the lowest
VADER sentiment (z-scores) was observed during the hurricane. Note that in the
case of Hurricane Dorian, only the Carolinas showed a decrease in VADER
sentiment as the only region to be directly affected, contrary to Alabama and
Florida that served as controls in this analysis. These observations support
the notion that hurricanes are associated with changes in collective sentiment
in the affected regions and that these changes are contingent on the hurricane
making landfall in the given region.

Fig 4: Z-score normalized sentiment of tweets in each region. For each
affected region, the sentiment was the lowest during the hurricane. In the
case of Hurricane Dorian, only the Carolina showed a large drop. Alabama and
Florida, on the other hand, showed very little changes in sentiment throughout
the whole time period.

To determine whether statistically significant changes in sentiment occurred
at a given point in time relative to when the hurricane made landfall, in Fig
5 we plot VADER sentiment changes relative to a null-model of randomly
selected tweets (see Materials and Methods) for each daily time window. The
null-model we use compensates for seasonal effects in our data by taking a
random sample of tweets from the period shortly before the time point itself.
This comparison reveals statistically significant daily sentiment changes
relative to when the respective hurricane made US landfall. For Hurricanes
Florence, Harvey, and Irma, we observe a large drop in sentiment that occurred
within the first 4 days of landfall. Hurricane Dorian in the Carolinas, on the
other hand, shows a large drop only one day before US Landfall. In each of
these regions, VADER sentiment decreased significantly while the LWNR
increased significantly (S5 Fig). The LWPR (S4 Fig) showed no pattern of
significant change in the affected areas and the control groups.

Fig 5: Bootstrapped VADER sentiment around each hurricane and location. For
each day, we calculated the 2.5th, 50th, and 97.5th percentile of the
bootstrapped sentiments in both the original data and the null model. The bold
line is the median of the observed data and the null model while the shaded
areas represent the 95% confidence interval. Any day in which the confidence
intervals does not intersect represents a statistically significant change in
sentiment. In all locations with landfall, the overall sentiment was lower
when compared to the previous month. This was followed by 2-3 days of a
sustained depressed sentiment. After about 8 days, the sentiment was
statistically higher due to the emotional resilience of the community.

After the sustained negative sentiment (about 8 days after landfall), the
VADER sentiment increases significantly, whereas the LIWC negative word ratio
(LWNR) decreases significantly, indicating a comparably more positive
sentiment. We observed a significant increase in the LIWC positive word ratio
(LWPR) for Hurricane Irma only. One may speculate this indicates a potentially
greater emotional resilience of this community, assuming that the ability to
return to previous levels after an exogenous shock indeed corresponds to
community resilience. As expected, there were few significant changes in
Florida and Alabama before, during, and after Hurricane Dorian since physical
damage was limited in those states.

We also calculated the magnitude of sentiment change using the daily, observed
z-score medians. The fits and sum of squared errors (SSE) for each hurricane
using VADER are shown in Table 2 and the plots are shown in Fig 6.

Table 2: Exponential Fits of VADER Sentiment for Hurricanes Irma, Harvey, and Florence Hurricane | Descending | Ascending |  |
---|---|---|---|---
| a | b | c | a | b | c | SSE | Half-life (Days)
Florence | -0.34 | 0.26 | 0.10 | -12.99 | -1. | 0.11 | 1.45 | 0.5
Harvey | -0.11 | 0.62 | -0.01 | -7.22 | -0.77 | -0. | 1.28 | 0.9
Irma | -0.18 | 0.80 | -0.06 | -1.3 | -0.64 | -0.06 | 0.72 | 1.3
Alabama | / | / | / | / | / | / | / |
Florida | / | / | / | / | / | / | / |
Carolina | -2.68 | 1. | 0.06 | -0.66 | -0.95 | 0.07 | 10.38 | 0.7
Fig 6: Exponential Fits of VADER Sentiment and Change in Sentiment. The first
plot shows every 12 hour grouped VADER data. With this, we fit two exponential
function to each affected area using. The exponential form is $E=ae^{bt}+c$
where $t$ is the time in days, $E$ is the expected data, and $e$ is the
natural exponential.

Fig 6 and in Table 2, the Carolinas were the only region to have the minimum
sentiment one day before the hurricane made landfall, as opposed to the other
regions, in which the lowest sentiment was one to four days after. Fig 4 also
shows that the Carolinas had the largest decrease in sentiment; almost double
the other affected regions. However, the Carolinas also bounced back to normal
in almost 2-3 days, indicative of high emotional resilience. Krause et al.
found it took 16 months for an individual to mentally recuperate from the
stressors of natural disasters [30]. One theory to explain the differences in
recovery time with our results is that two different types of thinking are
being measured [56]. The quick resilience can be explained by an immediate and
innate relief (Type 1 thinking) immediately after the primary disaster of the
hurricane. The longer resilience can be explained by a logical and more
analytical thinking (Type 2 thinking) involved in fully realizing the long-
and short term effects of the hurricane. The pattern for the other affected
regions were very similar, except with a much smaller impact. We don’t show
fits for Florida and Alabama as there was no significant sentiment change near
the hurricane event, even though Dorian skimmed by the Florida coast and many
may have been worried about the possibility of it making landfall in that
area.

Finally, we looked at all words with sentiment loadings, i.e. words that exist
in the VADER lexicon, that were used in tweets posted from the respective
communities during the hurricane event, and compared them to words before and
after the hurricane. Fig 7 shows the Kendall rank correlation coefficient
between time periods in each cluster. There is a high rank correlation between
VADER words before and after the hurricane, thus showing a very little
difference in rank order, i.e. the importance of the valence words are similar
in the two time period before and after the hurricane. The rank correlation
when comparing with words during the hurricane are 8-9x times lower,
indicating a large difference between the rankings.

Fig 7: Rank order correlation of VADER words by TF-IDF The Kendall rank
correlation coefficient is a measure of correlation between two orderings. A
high correlation between orderings of TF-IDF values of VADER terms from two
time periods indicates a similar ranking of the VADER terms between the two
time periods. The correlation between the before and after orderings are
almost 1, indicating a near perfect similarity in the word rankings. On the
other hand, all of the calculated correlations involving the during orderings
are almost 8-9x less, indicating that the hurricane had an effect on the usage
of VADER terms in tweets.

The S2 and S3 Figs show the top 5 valence words in tweets with the largest
z-score normalized TF-IDF score in each cluster and time period. Before and
after the hurricane, the most important words were the same in each region, as
shown in Fig Figure S2. For example, Hurricane Florence had the same exact
word list in the convex cluster before and after. During the hurricane, on the
other hand, “warning” became the most important word. The concave cluster has
the same group of words in all of time period. The decrease in TF-IDF scores
of the top 5 words during the hurricane can be attributed to an increase in
the use of the word “warning” from the convex cluster. A similar pattern can
be seen in Hurricanes Harvey and Irma as well; the concave clusters have the
exact same order of importance across all time periods and the decrease in
importance during the hurricane can be attributed to an increase in frequency
of words such as “help” and “safe” in the convex cluster. In Hurricane Dorian,
Florida and Georgia showed very little difference in the top 5 most important
words. In the Carolinas, on the other hand, “warning” and “safe” increased in
importance during the hurricane while words such as “great”, “love”, and
“happy” decreased.

## Discussion

Using geo-located social media, we show a statistically significant decrease
in community sentiment during four hurricanes, even though a set of common
hurricane related words had no effect on our analysis. Using exponential fits,
we also show a significant emotional resilience about 1-2 weeks after the US
Landfall, bringing the sentiment back to its baseline value. We also show an
increase in the importance of “danger” and “safety” related words that are not
directly hurricane-related.

Our data sources and analysis has many advantages over traditional survey and
interview methodology. Surveys can be impractical for live monitoring public
sentiment, in particular during a crisis situation such as a hurricane making
landfall. They can furthermore be biased by factors related to social
expectations and social conformity bias. Pluralities of the US population are
now active on social media platforms, yielding data that is recorded at high
temporal resolution [35], allowing systems to monitor changes in public
sentiment in approximately real-time during major events and even natural
disasters.

There are some limitations to this approach that should be addressed. First,
we use aggregate Twitter sentiment a a proxy of evolving community sentiment
because tweets tend to be unprompted and real-time. The Twitter population is
not a representative, but rather self-selected sample of the United States
population [34] and a sub-sample that is active on this particular social
media platform (Twitter vs Facebook, Reddit, etc), complicating efforts to
generalize from this analysis. Second, hurricanes and other extreme weather
can lead to power outages, sometimes lasting for days. Consequently, social
media and general internet usage in some users can decrease during those
times, also leading to a reliance on more traditional sources of information
such as TV and radio [51, 50]. However, we believe that internet and social
media usage during natural disasters is more nuanced when taking into account
the ”Digital Divide” [53]. As mentioned above, the Twitter population is not a
representative sample of the world. For example, young adults tend to use
social media more often than older adults [35]. Kent and Capello showed that
during the Horsethief Canyon Fire in September 2012, younger people had
disproportionately more fire related Twitter posts [52]. Xiao et al. showed
that during Hurricane Sandy, the number of related tweets increased in mildly
damaged places and decreased in more affected areas [54]. They also found that
young, male, or more educated populations are more likely to have more tweets.
When looking at income disparity and tweet frequency, they found a reversed
U-shaped pattern and hypothesized that an increase in wealth can indicate
better access to technology and information. But, after a certain point, the
extremely wealthy are less motivated to use social media, potentially because
they are less affected by the natural disaster. Furthermore, Wang et al found
similar results, showing that communities with higher social vulnerabilities
were underrepresented even before Hurricane Sandy made landfall, indicating
less social media engagement and the potential to be left behind during
disaster recovery due to low visibility [55]. We acknowledge the Digital
Divide as a limitation in our analysis as we are not able to account for these
population differences. Finally, in this work, we specifically focus on major
hurricanes that made landfall at different locations in the US, affecting
different communities. However, our results may not generalize to other
natural disasters such as floods or forest fires which may have specific
effects on public sentiment and community resilience.

These limitations suggest avenues for further research. Platforms such
Facebook, Reddit, and Weibo can provide detailed real-time data on public
sentiment that would allow generalization beyond specific social media
platforms and their communities. Combining such data with attention-level
data, e.g. Google searches, and surveys has been shown to lead to greater
accuracy of observations [41], and may similarly support the analysis of
community resilience with respect to the short and long term effects of
hurricanes. Natural disasters occur throughout the world, where many of
Twitter’s users reside. Multi-lingual sentiment analyses tool could therefore
be useful for studying the effects of natural disaster, including but not
limited to hurricanes, from a global perspective. This is particularly
relevant since countries can differ significantly with respect to
demographics, population density, government intervention strategies, and
infrastructure. Finally, a natural extension of our work is to include more
natural disasters, such as tornadoes, floods, and earthquakes. The differences
in the accuracy of forecasting between these natural disasters could lead to
interesting insights of how uncertainty affects the public’s emotional
response to the particular disaster as well as their collective resilience.

## Supporting information

#### Table S1

Data parameters from Twitter. The first two columns are the name, year, and
location of the hurricane. The next two are the bounding box from which the
geo-located tweets were sampled from. The following three date columns are the
dates from which the tweets were sampled and the US Landfall respectively. In
the case of Alabama, the US Landfall represents when the official announcement
was made by the White House.

#### Figure S2

Top 5 VADER words by normalized TF-IDF scores before, during, and after each
hurricane. The first cluster showed more caution related words with a higher
TF-IDF during the hurricane than before or after, such as “warning’, “safe”,
and “help”. In contrast, the second cluster had very similar words across all
time periods. The drop in TF-IDF of these similar words in the second cluster
can be attributed to the increase in caution related words in the first
cluster.

#### Figure S3

Top 5 VADER words by TF-IDF before, during, and after Hurricane Dorian for
each region. For Florida and Alabama, the words in both clusters are similar
across each time period. The Carolinas, on the other hand, show an increase in
caution related words throughout (seen in cluster 1) and thus a decrease in
the commonly said words (seen in cluster 2).

#### Figure S5

Bootstrapped LIWC negative word ratio (LWNR) around the hit date for each
hurricane and location. For each day, we calculated the 2.5th, 50th, and
97.5th percentile of the bootstrapped LWNR in both the original data and the
null model. The bold line is the median of the observed data and the null
model while the shaded areas represent the 95% confidence interval. Any day in
which the confidence intervals does not intersect represents a statistically
significant change in the LWNR. Hurricanes Florence, Harvey, and Irma caused a
significant increase in the LWNR starting at landfall while the LWNR in Dorian
(Carolinas) dropped 2 days beforehand.

#### Figure S4

Bootstrapped LIWC positive word ratio (LWPR) around the hit date for each
hurricane and location. For each day, we calculated the 2.5th, 50th, and
97.5th percentile of the bootstrapped sentiments in both the original data and
the null model. The bold line is the median of the observed data and the null
model while the shaded areas represent the 95% confidence interval. Any day in
which the confidence intervals does not intersect represents a statistically
significant change in sentiment. In all locations with landfall, the positive
word ratio had no significant change with the exception of Hurricane Irma 7
days after landfall.

#### Table S6

Equation Fits of VADER scores We tested 4 different models in order to find
the best fits. In all cases except Dorian in the Carolinas, the exponential
fit had the lowest sum of square errors (SSE).

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