Content Analysis of Social Media on Indonesia Vaccination Covid-19 Policy

The purpose of this study is to determine the types of social media platforms used by proponents and opponents of vaccination. This research employs a qualitative approach, analyzing social media hashtag data with Q-DAS (Qualitative Data Analysis Software) and Nvivo 12Plus. This study finds that: First, social media was used to spread both sides' narratives and content. Second, social media relation tends to be quite strong, but the pro-side is stronger than the contra-side. Third, the narration on both sides uses hashtags and a single word to spread the influence. They were used for vaccination issues on two sides of the issue. This research limitation, like this study concentrating exclusively on social media data, excluded digital data like the phenomenon on social media only. The recommendation for the following research is: Try to understand the social movements opposing or promoting vaccination in Indonesia and compare them to other Asian countries.


Introduction
In 2020, the whole of the world was affected by the Covid-19 Pandemic; this pandemic was caused by SARS-COV-2 that the first pandemic spread in Wuhan, China (Gates, 2020;Agustino & Wicaksana 2020;Darmastuti et al, 2020;Supriatna, 2020)). In 2021 the scholar tries to find out from this pandemic, starts producing the vaccine, and some countries making plans to vaccination to make herd immunity (Abo & Smith, 2020;Schaffer Deroo et al., 2020). Social media also trend about the pandemic of Covid-19, especially on the social media activism of Vaccine (Pearce et al., 2019). This research tries to know what kind the Social Media responds to the Pro-Contra vaccination program in Indonesia. This research focused on social media's role in the program of vaccination in Indonesia.
Social media can have the positive and negative impact on society, make the society become more mobilize and organize on using social media platform (Miladi, 2016), another side social media giving impact to the democracy make them a more rising voice to criticize the social problem (McCabe & Harris, 2020;Ortuoste, 2015). The country that affects Covid-19 Pandemic deals with health policy like Iran using two steps policy: a. testing new passengers from China (Iranian and Foreign) and placing quarantine facilities for two weeks b. allocation of a particular budget for such items as required services, staff, medical supplies (Raoofi et al., 2020). Impact using social media in case of the pandemic is netizen focusing on the government's health policy. From the response appear the health activism Della Porta & Diani on the Subramanian talks that It can include social movements that have a global or dispersed base, and better understand activist and non-targeted efforts, scholars have constructed theories about how they affect political and social outcomes (Charalambous, 2019;Subramanian & Weare, 2020).
Vaccine, seen as a Policy in the condition of the pandemic, is an urgent and strategic step to reduce the  Pandemic; on the other side, the government should guarantee that the vaccine is practical benefit the citizen (Kaldy, 2015;Phelan, 2020). There some point about policymakers needs see three aspects: (1) interference to planning, (2) interference connected to public notification, (3) financial accessibility (Banerjee, 2020;Ruan et al., 2015). On the other hand, public Policy can see three approaches: Policy as activity of calculation of frequency, Policy sees diffusion of the solve issue with adoption and realization. Policy as a hope for a social solution (Macková, 2016;Zhang et al., 2020). Social media become massive media that rising voice of the people, beside that social media, also spread the narration in through the internet users, social media also can coordinate the mass become movement into the response of the social problem (Burgess et al., 2017;Lehti & Kallio, 2017;Smith & Colvin, 2016).
The problem of the Covid-19 pandemic is the growth of infections in Indonesia. The patient grew until 15 March 2021more than 1 million cases of infection. The data for the cases infected is in Figure 1and Table 1.

Figure 1. Covid-19 Vaccination
Source: Katadata.co.id This research provides that social media split into two side narration. The proof vaccine program and the contra with the vaccine program, besides that social media used to spared both narrations, and the narration still relate to another hashtag with the same goals. In this research, there is some limitation in this research only focused on social media narration of pro-contra of the Policy, without knowing the framing strategy used in social media and digital media.
This research seeks to answer the three following questions, there are: 1. What kind the social media content that spread about the social media about vaccination issue.
2. What kind the social media hashtags relation in the case of the vaccination issue. 3. What kind of narration spread in case of the vaccination issue.

Social Media and Public Policy
Based on reach and accessibility, social media has surpassed traditional media. Digital media outlets such as social media must also be taken into consideration. Any candidate attempting to sway a target group's beliefs should rely on a mix of data and analysis. (Ruan et al., 2015). Horowitz on Kaldy suggests that in a political context, several principles guide the development of social media strategies, such as (Kaldy, 2015): 1. "Previous social media policies" apply 2. Social media practices in politics, including legislative, administration, and policy-making, must be sensible and educational. 3. Responsibilities include allowing discussion of where the building is built, including the number of posts created. Moreover, demonstrate the effects of new public policies, such as the fact that social media can raise awareness of social Policy; the massive number of users shows that it is a highly effective medium to reach out to the public. The second benefit of social media is that it enables society to more quickly and affordably get the information they need (Ortuoste, 2015). Therefore, on the other hand, on social media platforms such as Twitter and Facebook, the use of online activism is essential (Subramanian & Weare, 2020). The underlying traits of any media outlet should not be neglected, such as social media, which anyone can use to discuss and speak for themselves, in terms of social media, people can post whatever they want, but it is not always socially ethical (Gentile, 2016;Macková, 2016;Smith & Colvin, 2016).

Health Aspect as parts of Public Policy
Health care is critical in the Covid-19 Pandemic situation. Everybody needs to have health care. A highly effective course of action would be taken against the Covid-19 plague (Banerjee, 2020;Warwick-Giles et al., 2016). Consider China's vaccination program as an example: The three major areas of public health that should be given attention to are healthcare, work safety, and community health. There should be a link between management and policy at the start of the health sector. Third, it is essential for society and the government to be aware of the health problem. Finally, the issue of accessibility and affordability requires action through several options (Carpenter & Lawler, 2019;Ruan et al., 2015). More policies and regulations should be put to control the pandemic as it is nearing a standstill. To control this widespread infection, countries and regions will need to use policy induction, regulation, on-the-job training, and natural abilities (Raoofi et al., 2020). Other countries should also help heal one another, as the WHO said, regardless of the policies, statutes, and ways of life of their respective governments. States need to provide regional disparities to exchange information and collaborate to combat an urgent global problem (Charalambous, 2019;Dighe et al., 2020;Gates, 2020;Liu et al., 2020;Sohrabi et al., 2020).

Social Media and Rising Voice
There have been marked changes in the use of social media over the years as a vehicle for participating in public discourse. In this case, social media was used to carry out a response campaign in Indonesia to protect children against Indonesian flu vaccination. Previous protests have changed to become more digital with the advancement of social media. "Society has been changed by making individuals empowered to speak their minds and make themselves heard through social media. Similarly, people engage in social media conversations through hashtags to create trends. There have been numerous movements and protests carried out through social media platforms, such as the Arab Spring (Miladi, 2016). There has been an increasing opportunity for Arab activists to communicate and interact with one another since the onset of the Arab spring. When the traditional media outlets in these three countries should have been facing turmoil, this has leapfrogged ahead of them, establishing itself as a new media leader. In both countries, however, authoritarian governments have already forgotten about the floods of data and information and the erosion of human rights, civil and economic freedoms brought on by authoritarian regimes. Demonstrators and detractors of the area now depend on Facebook, Twitter, and YouTube for information for free (Miladi, 2016). The most commonly used tactic on social media is to use hashtags for content and to use your voice to become a trending topic on social media. Recent expressions such as #BlackLivesMurdersMatter, #BlackGirls, and #SayHerName have raised fair concerns about the democratic practice of media outlets and impartiality issues (Fischer, 2016). While social media is highly prevalent today, there is also an argument that protestors may derive some benefit from its use.
Furthermore, digital media gives these companies the advantage of concealing their critics because of its digital nature, enabling them to measure their reputation. As a result of protestors moving on to other online solution and social networks, various companies have been taking away the opportunity to offer real solutions to real problems (Uldam, 2018). Nonetheless, another difficulty faces us on the left, and this is known as Anti-Vaccine. An ongoing fight against issues in the public eye will help foster hot-theism and slacktivism. A Slacktivist explanation: It may be that people who use popular media platforms cannot engage in the following discussions because they are wary of getting scammed in future projects. Not only is it visually attractive, but also relevant to people's interests in the context of social media was also described in the "context of discussions" (Park & Rim, 2020).

Methods
This research uses a qualitative approach, which uses social media data. In this case, it is using descriptive way to deliver the value or the result. This research also uses Q-DAS (Qualitative Data Analysis Software) to analyze social media data (Kholid et al., 2015;Rahmat & Purnomo, 2020;Setiawan & Nurmandi, 2020). In this case, the social media data specified in hashtags basis, which defines two sides accepting the vaccine policy and contra with the vaccine policy. Using Nvivo 12Plus as a tool of analysis, this research uses three thoughtful analyses: Chart analysis, Cluster analysis, and Word cloud analysis. This research limits the social media data used and the narration and content of social media to know what kind of narration and content spread in the social media case of vaccination program in Indonesia. The social media data used is hashtag data, which is obtained on January 15, 2021. The description of hashtags in table 2 follows. #vaksinasidimulai Shows the Vaccination program started 3.
#vaksinasimelawanpandemi Shows Vaccination program to survive this pandemic 4.
#VaksinBiarTenang This hashtag shows that vaccination make sure to save everyone 6.
#vaksinuntukkita Describe the vaccination benefits to the society Contra Vaccination 1.
#antivaksin Shows the resist the vaccination policy 3.
#TolakDivaksinSinovac Showing the rejection of vaccination policy 4.
#TOLAKVAKSIN Express to refuse the vaccine 5.
#tolakvaksin Express to refuse the vaccine 6.
#TolakVaksin Express to refuse the vaccine 7.
#truenormal Show the true normal condition without vaccine

Result and Discussion
These parts try to find out what kind of social media is used in the case of the vaccination issue. These parts are twosector actors (pro-vaccine) and (Contra Vaccine) with three analysis parts: chart analysis, cluster analysis, and word cloud analysis.

Pro-Vaccination Policy
These parts try to know social media tendency in case of vaccination policy issue. These parts using three analysis, there is Chart analysis to know social media topic or content that related to the vaccination program in Indonesia, Cluster analysis to know social media relation of those hashtags in the social media talks of Vaccination program in Indonesia, and word cloud analysis to know narrative spread in the social media about the vaccination issue in Indonesia.

The analysis parts in down below. Social Media Content about Vaccination Policy Support
This section attempts to know the content that spread in social media about pro-vaccination in this parts using social media data that define in nine hashtags there are: #Vaksin, #vaksinasidimulai, #vaksinasimelawanpandemi, #VkasinasiNasional, #VaksinBiarTenang, #VaksinCovid19, #VaksinHalal, #VaksinSinovac, #vaksinuntukkita. This section usies three nodes. There are Covid, Sinovac, and Vaksinasi.  In this case, we can see that social media content, in this case, there are three nodes of social media content: Covid, Sinovac, and Vaksinasi. In nodes of Covid, the highest pole is #vaksinasimelawanpandemi with 38,99%, in the second pole is #VaksinBiarTenang is 35,09%, the third place is #Vaksin is 33,29%. Nodes of Sinovac the highest pole is #VaksinHalal with 47,23%. On the second pole is #VaksinSinovac with 33,66%. In third place is #Vaksin with a result of 31,79%. Nodes of Vaksinasi the highest pole is #VaksinasiNasional with a result of 68,57%.
The second pole is #VaksinasiNasional, with a result of 68,57%. The third pole is #vaksinasidimulai with 59,47%. From the result's total result, the Highest result is the nodes of is vaksinasi with 51,62%. The second pole of the total result is nodes of Covid with 30,67%. Third place is the nodes of Sinovac with 17,71%. From this case, we can see that social media was used to raise the topic of the vaccination program.

Social Media Relation on Vaccination Policy issue Support
This analysis parts using Cluster analysis to know what kind the social media relation. The analysis uses a 0,5 lower limit and one upper limit. In this case, social media is used as a platform to determine the relationship between those hashtags on social media. This scale-based analysis from -1,0, and 1 as a reference in the upper limit and lower limit. The analysis is below.  Table 4.

Social Media Relation about Vaccination Program
From the data, we can see that the relation of those hashtags we can see in 0,5 lower limit and the one upper limit. the Highest relation is #VaksinSinovac and #VaksinCovid19 with 0,809096 score. The second pole of relation is #VaksinCovid19 with #Vaksin, and the result is a 0,781341 score. The third place is relation #vaksinuntukkita with #VaksinCovid19, and the relation score is 0,756944. Fourth place is a relation of #VaksinCovid19 and #vaksinasimelawanpandemi, with the relation score is 0,750323. The Fifth place is the relation between #VaksinSinovac with #Vaksin, and the relation score is 0,749446. In this case, we can see that the relationship appears and quite strong because of the relationship we can see in the 0,5 point lower limit and 1 point upper limit.

Narrative Spread in Social Media Support of Vaccination Program
Using a word cloud analysis with the word more ap in the analysis figure is 100 famous words about the vaccination policy in Indonesia. The analysis parts in figure 4 and  In this case, we can see the narration that the spread is dominated by single word to show the support for the Covid-19 vaccination program, word such as Vaksin, Vaksinasi, Covid, and Pertama the top ten in the social media Twitter. The highest word is Vaksin, with a count is 18869. On the other hand, besides using single words, there are also hashtags on social media like #vaksinasidimulai, #jokowidivaksin, #vaksinhalal, and #vaksin. In this case, the narration spared a single domination word and the hashtags used to promote the vaccination program.

Tendency and actor on social media
This section tries to find out what kind of social media tendency and actor happened on social media. This part using charts analysis to explain what kind of tendency and actor happened in social media. Explanation in figure 5 and table 6 follows. The data shows that the tendency of vaccine as parts of human rights the dominance from government institution with 31,71%, tendency vaccine as a necessity dominated by an individual with 37,84%. Tendency vaccine as solution dominance by the government institution. Its shows that the pro of vaccine dominance from the government institution and individual and minor from an interest group and media. There are three different points that we can see from the analysis section; there are three points that we can see:

Figure 5. Tendency and Actor on the Social Media
1. From this point, we can see the tendency of social media used to promoted the vaccination program (Acosta Arcarazo, 2016;Burke & Şen, 2018;Sow, 2016;Uldam, 2018;Zanghellini, 2017). They were using single words and hashtags, and making the narration spread on social media can impact the netizen to participate in the vaccination program. The social media hashtags also show the relation that quite strong to look and indicated that those hashtags are connected with the same goals to promote the vaccination program (Checker, 2017;Fernandez-Wulff & Yap, 2020;Kende, 2016;Murthy, 2018;Soares, 2018;Sorell, 2015).
From the analysis point, we can draw that social media's groove to support the policies can define in three aspects: first, social media topics or content define in three dominant topics like Covid, Sinovac, and Vaksinasi. The result is Highest result is the nodes of is vaksinasi with 51,62%. The second pole of the total result is nodes of Covid with 30,67%. Third place is the nodes of Sinovac with 17,71%. From this case, we can see that social media used to raise the topic of the vaccination program. Second, social media has the relation; in this case, social media relation is quite strong that we can see the relation with lower limit is 0,5, and the upper limit is 1 point. With the Highest relation is #VaksinSinovac and #VaksinCovid19 with 0,809096 score. Third, the social media narrative that spared dominated with the single words, besides that the hashtags also appear in social media talk with the highest word is Vaksin with 18869. The actor dominance by a government institution and individual and minor from media and interest group.

Contra Vaccination Policy
These parts try to find out what kind of social media is used in contra vaccination policy in Indonesia. These parts try to know what kind of social media is used to contrapose vaccination policy. This part defines in three analyses, Chart analysis, Cluster analysis, and Word cloud analysis. The chart analysis tries to capture The content that appears in social media phenomenon rejection of Covid-19 vaccine policies. Cluster analysis to know the relation of the hashtags in social media about the rejection of vaccination program and word cloud analysis can be used to know what kind of social media narration spread in rejection of vaccination policies.

Content analysis case of rejection of Vaccination Policy
This section using charts analysis using three nodes to classified the post on the hashtags. In this case, using seven hashtags that relevant to the rejection of vaccination policy in social media. The analysis in figure 6 and table 7 follows.  From the analysis part, we can see that the nodes of antivaccine are many with results with 100% there are #antivaksin, #TolakDivaksinSinovac, #TOLAKVAKSIN, #tolakvaksin, and #TolakVaksin.
The others are #antivaccine with 57,14% and #truenormal with 25%. The highest nodes of people are # antivaccine with 15,3%, and the other hashtags are 0%. Nodes of protest the highest is #truenormal with 75% and second pole is # antivaccine with 27,55%; the other hashtags are 0%. from this case, we can see that dominant hashtags content focused on the antivaccine nodes with 100% reach, proves the content of rejection more concentrate to shows the nodes antivaccine rather than another node. in this case social media used to make the narration and content of rejection of vaccine to spread in social media. The result of nodes dominant antivaccine with 62,81%, People with 12,4%, and Protest with 24,7%.

Relation of Hashtags case of rejection Vaccination Policy
Using cluster analysis using lower limit criteria is 0 point, and the upper limit is 1 point. The upper limit and lower limit are from scale 1,0 and -1 on the cluster analysis toll in Nvivo 12Plus. One point means the relationship is strong, 0 means there are possibilities to see the relation, and -1 means there is no relation. The social media hashtags that are related to this case are seven hashtags. The analytical parts in figure 7 and table 8 follow. The data talks that social media relation in rejection of the vaccination program in Indonesia tend to be quite firm but not strong enough. Because the whole of the social media relation appears in the 0 lower limits, with 1 point upper limit, in this case, we can see that social media relation of rejecting the vaccination policy with the highest reach on the 1,00 point there are #tolakvaksin with #TOLAKVAKSIN. #TolakVaksin with #TOLAKVAKSIN. and #TolakVaksin with #tolakvaksin. Besides that, there are also appear another relation like the fourth place is #antivaksin and # antivaccine with 0,586503 points. In fifth place is #truenormal and #TOLAKVAKSIN with 0,419083. The sixth pole is the hashtags #truenormal and #tolakvaksin, with the result being 0,419083. Seven poles are the relation of hashtags is #truenormal and #TolakVaksin with 0,419083 points. On the eight-pole is #TOLAKVAKSIN and # antivaccine, with the result is 0,326247 points. The nine poles are #tolakvaksin and # antivaccine with 0,326247 points; on the ten places, there are the hashtags #TolakVaksin, and # antivaccine, with the result is 0,326247 point. From the analysis, we can see that social media relation is quite strong but simultaneously the highest relation with represented with 1,00 point.

Narration Spread in Social Media to Reject Vaccination Policy
This research uses word cloud analysis to know what kind of narrative spread in the social media, in case using 100 words famous on social media twitter about the rejection vaccination program in case of the Indonesia trends. Using social media trends is essential to know what kind of narrative gained traction on social media trends. Analysis on figure 9 and table 10.  From the 100 words, there social media popular word the highest count is #antivaccine with 175 counts, the Second pole is the word of people with 39 counts. The third place is #antivaxxer with 29 counts; the fourth is #covid19 with 29 counts. Five poles are the vaccine with 26 counts, in sixth place is shut with 20 counts, seven places is #vaccine with 19 counts. Eight places are anti with 18 counts; nine places are protesters with 18 counts; ten poles are #covid with 17 counts. The analysis shows that social media talks dominated by using hashtags as word popular in Indonesia's vaccination rejection. It is proven with five hashtags in the big ten most popular word: # antivaccine, #antivaxxer, #covid19, #vaccine, and #covid. The narration also uses the single word specific to show rejection vaccination protest, like People, Vaccine, Shut, Anti and Protesters. In this case, we can see that social media uses hashtags and single words that dominated the famous word of rejection of the policies (Albrecht & Citro, 2020;Fischer, 2016;Gogul, 2020;Rivas, 2016).

Tendency and actor on social media
This section tries to find out what kind of social media tendency and actor happened on social media. These parts using charts analysis to explain what kind of tendency and actor happened in social media. The explanation in figure 10 and table 11 follows.  Besides that, the narration also using the single word specific to show rejection vaccination protest, like People, Vaccine, Shut, Anti and Protesters 4. The data shows that the tendency antivaccine protest dominates the individual actor with 60% and Group 40%. Tendency Covid Hoax there are no dominated with Group and Individual actor get 50%. The tendency of vaccine ineffective is dominated 67,57% and group with 32,43%. From this part, we can see that the social media actor of contra is dominated by individual actors and minors from the group actor.

Conclusion
In the analysis part, there is some difference between pro and contra. On social media, there are three points that stand out as comparisons between provaccination and anti-vaccination policies: 1. Social media content, in this case, the pro-vaccination Policy more evenly on every node, for example, if we take a look at the total nodes. The highest result is the nodes of is vaksinasi with 51,62%. The second pole of the total result is nodes of Covid with 30,67%. Third place is the nodes of Sinovac with 17,71%. From this case, we can see that social media used to raise the topic of the vaccination program. The contraside is more dominant in one node (antivaccine). The dominant nodes are antivaccine with 62,81%, rather than People with 12,4% and Protest with 24,7%. Its shows that social media is also used to show the rejection of the vaccination policy. 2. The result of relation in social media hashtags the pro of vaccination is quite substantial because the relationship can be seen on the 0,5 lower limit. It is also quite strong on the contra-side but not strong enough because the relationship can be seen on 0 lower limits. 3. Both sides, pro, and contra use hashtags and a single word to share the narration on social media. Both sides dominated using hashtags to promote the narration and using a single word to represented the voice. 4. Some different actors dominated when the pro-vaccine dominated from the government institution. On the other side, the dominant actor of contra is from the individual actor. From the four-point, we can see that social media used in two sides to promote the narration and content (Albrecht & Banerjee, D. (2020