Background: Exploring the characteristics of the most popular tweets during the outbreak of poliomyelitis in the Philippines in 2019 can guide health professionals in the competent use of social media for health education.
Objective: To determine the characteristics of popular tweets during the outbreak of poliomyelitis in the Philippines in 2019 according to source, content type, tone, sentiment, and topic.
Study Design: Cross-sectional.
Study Setting: Twitter.
Methods: To recover the tweets with keywords of the outbreak of poliomyelitis in the Philippines in 2019 from publicly available content, Tweet Binder was used to stream for tweets in English between September 14, 2019, to March 14, 2020. These were checked for redundancy and relevance. The tweets were classified according to source, content type, tone, sentiment, and topic. Descriptive statistics was used to analyze the data. The data was presented in tables and graphs.
Results: The top five most popular tweets during the outbreak were from news outlets and personal accounts. These were mostly news articles and blog posts. The popular tweets of the outbreak were from personal accounts followed by news outlets. A few health professionals were active on Twitter. News articles were the most commonly shared type of content. Most of the tweets had an unclear or neutral stand on vaccination. A few opinions against vaccination were noted. Majority of the tweets contained information rather than sentiments. The resurgence of poliomyelitis in the Philippines was the most discussed topic.
Conclusion: Popular tweets during the outbreak of poliomyelitis in the Philippines in 2019 had the following characteristics: (1) source–personal accounts, (2) content type–news articles, (3) tone–neutral or unclear towards vaccination, (4) sentiment–information, (5) topic–resurgence of poliomyelitis in the Philippines.
Keywords: Poliomyelitis, Outbreak, Vaccine, Social Media, Twitter
INTRODUCTION
The reluctance or refusal to vaccinate has been
threatening to undo the advancement made in the battle against
vaccine-preventable diseases.1-3 In 2019, the World Health
Organization listed vaccine hesitancy as the number seven threat to global
health.4 In the Philippines, this threat was made evident when the
Department of Health declared last September 19, 2019 an outbreak of
poliomyelitis–a debilitating and crippling viral illness
which has long been controlled in the country by vaccination since 2000–putting
numerous children at risk of lifelong paralysis. This declaration was triggered
by a confirmed case of polio in a three-year-old girl from Mindanao last
September 14, 2019.5-7 News of this outbreak resulted to an
explosion of online content in social media platforms such as Twitter.
The impact
of social media on vaccination should not be underestimated. Digital 2019 reported that 45% of the
global population were active social media users.8 Young adults aged
between 25 to 34 years were the most populous among active social media users.
Among adolescents aged 13 to 17 years, 3% of males and 4% of females were
frequent users. The same report said that 76 million Filipinos aged at least 13
years old were in social media. This comprised 71% of the national population.
Filipinos engaging in social media grew by 13% between 2018 to 2019. Twitter,
the fifth most active social media platform in the country after Facebook,
YouTube, Facebook Messenger, and Instagram, was reported to have 5.08 million
monthly active users. It has been a popular online social networking service
for collaborative journalism through the broadcast of tweets.9, 10
Twitter users were more likely to follow breaking news and viewed a greater mix
of news topics than Facebook users.11 It has been shown to quickly
disseminate news through the population, provide timely information to
witnesses and casualties of disasters, and aid the development of consensus
among its users.12, 13 Its concise lay-out has greatly simplified
the recovery of data for research: hashtags to index content, likes
to express agreement, and retweets to share content.14-17
While health care providers remain an important source
of accurate information, ostensible consensus in social media affects the
decision-making, the perception of harm, and the risk-management behaviors of
its users.18,19 The sharing of potentially harmful information in
social media continues to threaten the success of national vaccination
programs. This leaves children at even greater risk of developing
vaccine-preventable diseases. Exploring the characteristics of popular online
content on vaccination in social media is one approach in the study of public
opinion on immunization.20-22 An effective communication program
that champions for vaccination also requires the creation of educational
materials that take into account the communication patterns of users of social
media.23, 24 Health educators are more likely to enjoy the patronage
of a community when they utilize sources of information that cater to their
preferred sentiment and tone of speech. Eventually, with their acceptance comes
a higher degree of compliance.25
It is anticipated that this study will help transform
social media from a predominantly amateur forum of discussion to an effective
conduit for health education.26 When health care providers partake
in discussions on social media, their input accretes with the opinion of
laypeople towards a consensus, and their specialized contribution can mitigate
the effects of misleading information.19
Research
Objectives
General
Objectives
To determine
the characteristics of popular tweets during the outbreak of poliomyelitis in
the Philippines in 2019 according to source, content type, tone, sentiment and
topic.
Specific Objectives
1. To
determine the top five most popular tweets during the outbreak of poliomyelitis in the
Philippines in 2019 according to the number of likes
and retweets.
2. To
determine the frequency of the following attributes of
popular tweets during the outbreak of poliomyelitis in the Philippines in 2019:
A. Source
A.1. Government agency
A.2. Health professional
A.3. International organization
A.4. Journalist
A.5. News outlet
A.6. Non-governmental organization
A.7. Personal account
B. Content
type
B.1. News article
B.2. Blog post
B.3. Image
B.4. Other
B.5. No attached link
B.6. No access to attached link
C. Tone
C.1. Pro-vaccination
C.2. Anti-vaccination
C.3. Neutral/unclear
D. Sentiment
D.1. Information
D.2. Frustration
D.3. Humor/sarcasm
D.4. Concern
D.5. Others
E. Topic
Definition of Terms
Ethical Considerations
The study
commenced when the approval from the Cebu Institute of Medicine–Cebu Velez
General Hospital Institutional Review Board was obtained (Appendix 1). Strict
confidentiality was observed during the entire course of the research. The
author had no conflict of interest while conducting this research.
RESEARCH METHODOLOGY
Study Design
The study utilized a cross-sectional
design.
Study Setting
The study was done over Twitter – a
social media networking service which allows registered users to engage with
one another by the broadcast of tweets.
Study Population
Inclusion
Criteria: Tweets with the following attributes:
1. Scope–Tweets
on the 2019 Philippine poliomyelitis outbreak (posted from September 14, 2019,
to March 14, 2020, containing the keyword Philippines with any of these
keywords: poliomyelitis, polio, vaccine, or immunization)
2. Language–English
language
3. Popularity–native
retweet
count of 10
4.
Publication–publicly
available for recovery through Twitter’s Application Programming Interface
(API) by Tweet Binder
Exclusion Criteria:
1. Duplicates
and unintelligible content
2. Unrelated
content
Data Collection
The research received the approval of the Cebu
Institute of Medicine–Cebu Velez General Hospital Institutional Review Board. Tweet
Binder, a
third-party application service, was used to the recover the desired tweets
from publicly available content.30-32 It streamed for alphanumeric tweets
in English posted between September 14, 2019, to March 14, 2020. The tweets may
have any of these keywords: poliomyelitis,
polio, vaccine, immunization but
must always have the keyword Philippines.
The keywords were typed into the search query box while the dates and the
language were selected in drop-down lists in the online interface. This yielded
a list of all the tweets of interest with their usernames, account names, uniform
resource locators (URL) and the number of likes and retweets. This report was
downloaded as a Microsoft Excel Workbook. The report was manually examined for
completeness, clarity, and redundancy. Extra information in tweets, such as
links to web pages, were opened for content. Hashtags were retained with the
“#” symbol removed.
Three coders were trained for
content analysis. Each coder received a coding sheet containing 30 randomly
selected tweets. Each coding sheet had a unique ordinal position for the same
tweet. The assignment of the tweets in the coding sheets was exclusively known
to the author. Each coder appraised the tweets in the coding sheet by assigning
a number that corresponds to a specific choice for a certain attribute. Each
coder evaluated the tweets for these key attributes. The coders were not
allowed to discuss with each other.
Figure 1. Flowchart for Data Collection.
The coders were, thereafter, taught to identify and
code for topic. Topic referred to the themes defined by thematic analysis according to
the procedure outlined by Braun and Clark.29 While
discussions involving the author and the coders to develop the topics were
allowed, each coder returned to independently appraise the tweets for topics.
Interrater reliability was measured by Fleiss’ kappa (k). The study
ensued once the coders attained an acceptable level of consensus (k ≥
0.8) in all the key attributes.
During the pretest, the coders had a Fleiss’ kappa of 0.49 for relevance, 0.77 for
source, 0.31 for content type, 0.34 for tone, 0.35 for sentiment and 0.63 for
topic. Since the agreement between coders was poor (k < 0.8),
feedback was collected and issues in coding were clarified. With these changes,
the coders were retested for consensus with the following results: Fleiss’
kappa of 1.00 for relevance, 1.00 for source, 0.92 for content type, 0.95 for
tone, 0.95 sentiment and 1.00 for topic.
All tweets that had a retweet count of at least 10
times were identified from the report. The coders used the same procedure for
content and thematic analysis as learned from their training. The tweets were
appraised for relevance (Appendix 2). Tweets which were not relevant were
removed from the dataset. A copy of the remaining tweets was sorted according
to the number of likes and retweets. The remaining tweets were then evaluated
according to source, content type, tone, and sentiment (Appendix 3). The source
of the tweet was classified as follows: news
outlet, journalist, health professional, government agency, international
organization, non-governmental organization, and personal account.
The content type of the tweet was categorized as news article, blog post, image, other, no
attached link, and no access to attached link. The tone of the tweet was
classified into pro-vaccination,
anti-vaccination and neutral/unclear. The sentiment
of the tweet was recorded as information,
frustration, humor/sarcasm, concern, and
others. The tweets were only
considered for topic once all other attributes have been coded. The
coders returned the completed coding sheets with schemata on how the tweets
were evaluated (Appendix 4). The agreement of at least two of three coders
classified a tweet into categories within attributes. When all coders do not
agree, the tweet was removed. The resulting codebook included the following
variables: username, uniform resource locator (URL), tweet, number of likes,
number of retweets, source, content type, tone, sentiment, and topic (Appendix
5). This was in the format of a Microsoft Excel Workbook. This was stored in a
password-protected storage service and would be disposed after two years.
Descriptive statistics was used to determine the
frequency categories within each of the attributes. Proportions were used for
categorical variables. Inter-rater consensus was expressed in Fleiss’ kappa at 5% level of significance. Minitab
version 19.0 for Mac Mojave OS was used in the analysis of data and statistical
computations.
RESULTS
There were
13,944 publicly available tweets containing keywords of the polio outbreak made
between September 14, 2019, to March 14, 2020. Almost all of these tweets were
in English. Original content comprised 30.99% of the tweets while 69.01% were
retweets. The tweets were from 10,116 users each contributing an average of
1.38 tweets. Almost a quarter (23.12%) of these contributors each had 1,000 to
5,000 followers. The tweets were projected to reach 332,869,693 user accounts
and had the potential to be seen 724,653,931 times over Twitter. There were no
redundancies and unintelligible content in the report (Appendix 6).
A total of
259 tweets with the keywords of the outbreak were retweeted at least ten times.
These were evaluated for relevance. The coders had good consensus on relevance
(k=0.99;
p<0.00005). Forty-five (17.37%) of the tweets were removed for not
being relevant. The remaining 214 tweets (82.63%) were suitable for analysis
(Figure 2).
Four tweets
remained in the top five most popular tweets of the outbreak be it according to
likes or retweets. These were news articles and blog posts from news outlets
and personal accounts. They either informed the public of the outbreak or
expressed frustration over the resurgence of polio cases. These tweets have an
unclear or neutral stand on vaccination. Two tweets were not present in both
lists. Unlike the other four tweets, both were in favor of vaccination (Table
1-A and 1-B).
Figure 2. Flowchart on the Selection of the Study
Population.
Table 1-A. Top Five Most Popular
Tweets During the 2019 Philippine Poliomyelitis Outbreak Based on Likes.
Rank |
Account Name
and Tweet |
Likes |
1 |
“Polio remains in only 3 countries:
Pakistan, Afghanistan and Nigeria. There is a fourth now: Philippines. Polio
is so contagious that as long as a single child is infected with poliovirus,
children in all countries are at risk. Fuck you very much, Persida Acosta.
THIS IS ON YOU. https://t.co/3VmPziiDRp” |
12,555 |
2 |
#DefendPressFreedom #JunkTerrorBillNOW “We’re in deep shit: Dengue, African Swine Flu, now Polio. |
12,098 |
3 |
CNN Philippines “BREAKING: DOH confirms reemergence of
polio in the Philippines,19 years after the World Health Organization
declared the country free of the disease https://t.co/sDo55hwVGt
https://t.co/HZcMwtTU7G” |
5,185 |
4 |
ABS-CBN News “JUST IN: Department of Health confirms re-emergence of polio in the
Philippines, 19 years after declaring the country as polio free. | via
@raphbosano https://t.co/kYPuqDMHjY” |
3,038 |
5 |
TwoCityTrails “Singapore's SEA Games-bound team to get
polio vaccinations after 'increased' incidents of disease in the Philippines.
???????? #SEAGames2019 https://t.co/5dTZ5Dyabw” |
2, 639 |
Table 1-B. Top Five Most Popular
Tweets During the 2019 Philippine Poliomyelitis Outbreak Based on Retweets.
Rank |
Account Name
and Tweet |
Retweets |
1 |
Thérèse “Polio remains in only 3 countries:
Pakistan, Afghanistan and Nigeria. There is a fourth now: Philippines. Polio
is so contagious that as long as a single child is infected with poliovirus,
children in all countries are at risk. Fuck you very much, Persida Acosta.
THIS IS ON YOU. https://t.co/3VmPziiDRp” |
6,478 |
2 |
CNN Philippines “BREAKING: DOH confirms reemergence of polio in the Philippines,19
years after the World Health Organization declared the country free of the
disease https://t.co/sDo55hwVGt https://t.co/HZcMwtTU7G” |
5,093 |
3 |
#DefendPressFreedom #JunkTerrorBillNOW “We’re in deep shit: Dengue, African Swine
Flu, now Polio. |
5,055 |
4 |
World Health Organization Philippines “There is an outbreak of #polio in the Philippines. |
2,618 |
5 |
ABS-CBN News JUST IN: Department of Health confirms
re-emergence of polio in the Philippines, 19 years after declaring the
country as polio free. | via @raphbosano https://t.co/kYPuqDMHjY |
2,137 |
Personal accounts of individual users posted 78 popular tweets (36.45%) during the outbreak.
This was closely followed by news outlets which contributed 73 tweets (34.11%).
The involvement of other sources of tweets were as follows: international
organizations with 19 tweets (8.88%), non-governmental organizations with 17
tweets (7.94%), health professionals with 16 tweets (7.48%), journalists with
10 tweets (4.67%) and government with one tweet (0.47%) (Figure 3).
Figure 3. Sources of Popular Tweets of the 2019 Philippine Poliomyelitis Outbreak.
The most
common type of content shared during the outbreak were news articles in 111
tweets comprising 51.87%. Other types of content on Twitter during the outbreak
included 63 blog posts (29.44%), 34 images (15.89%) and two others (0.93%)–a
headline banner and an audio clip. Four tweets (1.87%) had broken links to
online content outside of Twitter (Figure 4).
Figure 4. Content Types of Popular Tweets of the 2019 Philippine Poliomyelitis Outbreak.
Most of
the tweets during the outbreak did not express a stand on vaccination or were
neutral to this preventive measure. This included 155 tweets (72.43%) with a
neutral or an unclear tone. There were 55 tweets (25.70%) which expressed
favorable opinions towards vaccination while four tweets (1.87%) had views
against vaccination due to the documentation of vaccine-derived polioviruses in affected
children (Figure 5).
Figure 5. Tones of Popular Tweets of the 2019 Philippine Poliomyelitis Outbreak.
With regards
to the sentiments of the tweets, 176 tweets (82.24%) conveyed information.
Fifteen tweets (7.01%) expressed concern towards anticipated needs and problems
while 11 tweets (5.14%) contained frustrations and expressed perceived
resistance in the prevention, control, and mitigation of the outbreak. Tweets
which heavily relied on context for proper interpretation included 12 humorous
and sarcastic tweets (5.61%) (Figure 6).
Figure 6. Sentiment of Popular Tweets of the 2019 Philippine Poliomyelitis Outbreak. N=214
Thematic
analysis of the tweets resulted in
five topics. Resurgence of poliomyelitis in the Philippines included 131
tweets (61.21%) on the existence of an outbreak. Disease susceptibility and
severity comprised seven tweets (3.27%) on the pathogenic nature,
presentation, diagnosis, and management of poliomyelitis. Regulation and
policy issues referred to the 23 tweets (10.75%) on how the Philippine
government responded. Non-government initiatives consisted of 21 tweets
(9.81%) on how individuals and organizations responded. Vaccine efficacy
had 32 tweets (14.95%) on how immunization can control the outbreak (Figure 7).
Figure 7. Topics of Popular Tweets of the 2019
Philippine Poliomyelitis Outbreak. N= 214
The coders made
consistent assessments in all five key attributes (k>0.8, p<
0.0005). The agreement in
the nominal assessments of the three coders was acceptable (Table 2).
Table 2. Validity of the Attributes of Relevant Popular Tweets.
Attribute |
Tweets Inspected |
Matched Responses |
% Agreement |
k |
p-value |
Relevance |
259 |
258 |
99.61 |
0.99 |
<
0.00005 |
Source |
214 |
209 |
97.66 |
0.98 |
< 0.00005 |
Content |
214 |
205 |
95.79 |
0.95 |
<
0.00005 |
Tone |
214 |
204 |
95.33 |
0.92 |
< 0.00005 |
Sentiment |
214 |
208 |
97.20 |
0.94 |
<
0.00005 |
Topic |
214 |
206 |
96.26 |
0.96 |
< 0.00005 |
Over-all |
1329 |
1290 |
97.07 |
0.97 |
<
0.00005 |
DISCUSSION
Users of social media exchange information through the
active publication of material (i.e., posting) or through the passive
consumption of content (i.e., browsing).34, 35 During emergency
events and other non-routine situations, Twitter functions as a broadcasting
medium. This skews social media activity to favor a small number of highly
active contributors that tend to disseminate information towards a far larger
aggregate of bystander receivers.11-13 The results of this study
show that the number and content of tweets on the polio outbreak was consistent
with other studies which described Twitter as a broadcasting medium.36-41
The heuristic processing of information can explain
the recovery of about 14,000 tweets with keywords of the outbreak from nearly
10,000 users. Most of these tweets were retweets.42-44 The methods
of this study rely on the fact that the repeated use of certain concepts
related to an outbreak lead to the frequent presence of the keywords on
Twitter.45-49 Using these keywords, users can search for similar
tweets. The immediate appraisal and almost instantaneous sharing of content
through retweets were the result of personal decisions. In choosing what to
share during the polio outbreak, users usually selected tweets with the
keywords.18, 19 The frequent use of these keywords explains why most
of the tweets recovered in the report were retweets and why retweets remain the
preferred measure on how popular a particular content is on Twitter.50-55 With
every retweet, the original tweet is perceived by other users as particularly
important. This translates to its virality–the creation of huge volumes
of retweets quickly.
In a study based on mathematical models, once a tweet
is retweeted, it gets retweeted again almost instantly on the second to the
fourth hop away from the source.9 It should then come as no surprise
that the tweets which contain the keywords of the polio outbreak were projected
to have been sent out to at least 300 million users (also known as reach)
and had the potential to be seen at least 700 million times (also known as impact)
over Twitter.10 A few studies showed that any tweet shared as
retweets reaches an average of 1,000 users irrespective of how many followers
the original author has. This suggests that the retweet of a single tweet from
users with at least 1,000 followers – which constitute almost a quarter of the
sources of the tweets in the report – can be very persuasive.50-55
Most of the top five popular tweets on the outbreak of
poliomyelitis in the Philippines in 2019 were news articles and blog posts from
news outlets and expressions of frustration on the resurgence of cases from
personal accounts of laypeople. The numerous likes and retweets which
frequently appear in the ongoing stream of posts are heuristic cues for
consensus.56,57 Since it can imply approval, retweeting suggests to
other users that the information contained within a tweet is worthy of mass
broadcast. Liking, in
contrast, provides a more personal and meaningful form of approval that is less
public. Hence, when users opt to retweet, their choice to disseminate
information is in a more conspicuous manner. As a nonverbal form of
interpersonal communication, retweeting and liking reflects the immense value
users attach to the most popular tweets.12 Also, these leading
tweets illustrate on how individual decisions in social media can align towards
a goal.40, 41 It is this choice to share a post with others that
makes social media a viable avenue for health education.58-60
In this research, personal accounts posted most
(36.45%) of the popular tweets on the outbreak of polio in the Philippines in
2019. This was followed by news outlets (34.11%), international organizations
(8.88%), non-governmental organizations (7.94%), health professionals (7.48%),
journalists (4.67%) and government agencies (0.47%). A study in Spain which did
a content and source analysis of popular tweets on diphtheria showed that news
outlets constituted 15% of the sources. Other sources were authors (10.8%) and
journalists (2.6%). There was no mention on how many of the tweets were from
personal accounts. None of the tweets came from healthcare professionals or
organizations.25 The popularity enjoyed by news outlets in both of
these outbreaks results from the active role they take in exceptional
situations.34, 35 For example, a typical broadcast of an outbreak
begins with the dissemination of its existence from news outlets in the form of
news articles based on government issuances.11-13, 35-41, 61 Since
these announcements are perceived to come from reliable sources, users of
social media use their personal accounts to validate and express their views.62
During outbreaks, some individuals and groups rise to the occasion as experts
in their field.12, 26 In this study it was observed that some
popular tweets came from these expert health professionals. It is worth
mentioning that they have created substantial impact because they were among
the top sources of popular tweets. However, a similar study on the measles
outbreak in the United States in 2015 concluded otherwise. They reported that
news organizations have a higher impact than articles from health professionals
and organizations in dispersing health-related material in social media.63
During the polio outbreak, the activity of a small but influential cohort of
health experts on Twitter help counter misinformation by producing highly
engaging content and by competitively excluding potentially harmful content
from public consumption.26, 34-35
This study found that the most common type of content
shared during the polio outbreak were news articles followed by blog posts,
images, headline banners and audio clips in decreasing order of frequency.
Regardless of the type of content, breaking news on disasters and tragedies get
shared a lot on Twitter.64 In both the diphtheria and poliomyelitis
outbreaks, news articles were the most popular type of content.25 These
news articles highlight pressing health issues in social media. Likewise, they
create substantial impressions that can initiate and sustain public discussion
in social media.65
The tone of the popular tweets during the polio
outbreak was also analyzed. This study noted that a substantial proportion of
the tweets were neutral or unclear towards vaccination (72.43%) and some of the
tweets were in support of immunization (25.70%). However, during the diphtheria
outbreak in Spain, most of the tweets were in support of immunization (58%) and
some of the tweets were neutral or unclear towards vaccination (42%).25
Since the most common type of content shared during the outbreak were news
articles, it is expected that the tone of the statements was neutral or
objective rather than opinionated.11 The tweets against vaccination
for polio were from accounts of individuals suspicious of the efficacy of the
vaccines. This suspicion came about because of the report that the polio virus
recovered from the affected children were vaccine derived.5-7 Tweets
from anti-vaccination groups were not noted among the popular tweets of the
polio outbreak. During the diphtheria outbreak in Spain, however,
anti-vaccination tweets were not reported. The marginal prominence of anti-vaccination
tweets during the polio outbreak, could also be the result of competition from
tweets that reassure and empower the public.25
Another key attribute analyzed in this study was the
sentiment of the tweets. Majority of the tweets of the polio outbreak were
factual (82.24%). Tweets with sentiments of concern (7.01%), humor/sarcasm
(5.61%) and frustration (5.14%) followed in decreasing order of frequency. In
contrast, tweets on a diphtheria outbreak in Spain were slightly led by the
emotions of frustration, humor/sarcasm, and concern (53%). This was then
closely followed by informative tweets (47%).25 Since most of the
popular tweets during the polio outbreak were news articles from news outlets,
emotional tweets from personal accounts did not gain as much attention. While
it is known that messages with negative emotions get more retweets on Twitter,
the need for information during outbreaks explain this level of publication and
consumption. 12, 66 This was also brought about by the peculiarities
of social interaction among Filipinos that has now extended to social media.5-8
The social norms and cultural practices of a group plays a substantial role in
dictating the social media activity of its members.20-25 For
example, in cultures that tolerate the expression of emotion in public, some
users resort to social media to air out their frustration.45 One
study in Netherlands showed that the reason why social media users expressed
frustration during an outbreak was because of the knowledge that the disease is
preventable.67 Furthermore, in a study in 2013 on the role of
emotional response on the virality of social media content also found out that
the use of humor and sarcasm garnered more attention, increased revenue or
eased the sharing of divisive views during discussions.68-70
The last key attribute was on the topic of popular
tweets. The resurgence of poliomyelitis in the Philippines was the most
discussed topic (61.21%) followed by vaccine efficacy (14.95%). The rest
of the tweets were on the topics regulation and policy issues (10.75%), non-government
initiatives (9.81%) and disease susceptibility and severity (3.27%).
The popularity of this topic stems from the fact that cases of poliomyelitis
have resurfaced after having been controlled in the Philippines since the year
2000. This incongruity has brought to the attention of the public the issue of
poor vaccine coverage despite the existence of a national vaccination program.5-7
During the diphtheria outbreak, the most popular topic was on criticisms
towards anti-vaccination groups (35%). The groups do not believe in the
effectiveness of immunization.25
This study cannot determine as to which kinds of
tweets would be more effective in changing one’s attitude and behavior towards
vaccination. One study in
2015 on the effect of scientific consensus on public support for vaccination
showed that statements of consensus on the safety and efficacy of vaccines
improved the parents’ perceptions on vaccine safety.19 Another study
in 2013 on the how parents decide on the vaccination of their children showed
that discouraging vaccination may be perceived as irresponsible and bad
parenting.71 However, these studies were not designed to observe for
changes in individual or public opinion over long periods of time. The
long-term effect of discussing vaccination on Twitter in the context of current
social norms remains to be seen.72
CONCLUSION
The top five most popular tweets during the outbreak
of poliomyelitis in the Philippines in 2019 were news articles and blog posts
from news outlets and personal accounts that expressed frustration on the
resurgence of cases. Popular tweets during the outbreak of poliomyelitis in the
Philippines in 2019 had the following characteristics: (1) source–personal
accounts, (2) content type–news articles, (3) tone–neutral or unclear towards
vaccination, (4) sentiment–information, (5) topic–resurgence of poliomyelitis in
the Philippines.
The dissemination of information during this outbreak
relied on the heuristic appraisal of messages in tweets and the rapid creation
of numerous retweets. The outbreak has solicited ideas of individual and community response.
It has prompted the expression of various sentiments and elicited the activity
of a few who are against vaccination. A few health professionals were able to
post highly engaging online content that was shared with many people in a brief
time. This worked to keep potentially harmful messages from gaining the
attention of passive users of Twitter.
RECOMMENDATIONS
The extensive reach and immense impact of
messages broadcasted in social media should not be disregarded. While health
professionals continue to provide facts and advice related to health, social
media has transformed the choices and actions of its users. The pragmatic use
of social media can help address the threat of vaccine hesitancy in the
country. Public health programs can utilize social media, such as Twitter, to
address misinformation in vaccination.
The use of social media has resulted to a community of users who share, obtain, and utilize information gathered online. In response to this momentous change in human interaction, the competent use of social media should now be in the skill set of contemporary health professionals. They should not shy away from Twitter simply because of unfamiliarity but should make the most out of this platform. Simply put, humans just found another way to talk about their health.
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