Table 3.
Summary characteristics of studies that reported the degree of polarization of views / content regarding COVID-19 vaccination on social networks.
| Reference | Country | Study period | Study content | Social media | Main results |
|---|---|---|---|---|---|
| Alam et al., 2021188 | NR | December 21,2020 to July21,2021 | Public posts | Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. | |
| Ali et al., 2021189 | USA | February to March of 2021 | Public posts | Among the February tweets, about 40% were mature positive, 21.15% were mature negative, and 33.15% were mature neutral tweets. On the other hand, in March 2021, 35.30% were mature positive, 22.09% were mature negative, and 36.40% were mature neutral tweets. It can be observed that, both among February and March tweets, positive sentiment was higher than negative sentiment among the mature users. Additionally, the percentage of neutral sentiment increased among the mature users from February to March. | |
| Al-Zaman et al., 202194 | Bangladesh | 8 March to 2 December 2020 | 10,000 most popular Facebook posts with the highest interactions on the vaccine issue | The results show that Facebook users prioritize more vaccine-related news links (71.22%) over other content. The declining interactions on the issue suggest that interaction growth mainly depends on positive news on the vaccine. Finally, users’ reaction to the vaccine issue is dominantly positive, though they may show a highly negative attitude toward vaccine misinformation. | |
| Ansari &Khan 2021190 | NR | May 15, 2021, to June 25, 2021, | Public posts | Overall global findings of COVID-19 vaccination sentiment analysis suggested 1.23% strong positive sentiments and 6.43% strong negative sentiments. According to gender based COVID-19 sentiment analysis male very strong positive sentiment is 0.22% and very strong negative sentiment is 0.33% while in case of female gender the very strong positive sentiment is 0.00% and very strong negative sentiment is 0.81%. | |
| Basch et al., 202187 | NA | NA | Videos | Tik-Tok | The number of videos discouraging the vaccine was 38, while of those encouraging the vaccine was 36. Videos encouraging a vaccine garnered over 50% of the total cumulative views and just around 50% of the total likes, while the opposite accounted for 39.6% of the total cumulative views, 44.3% of likes, and 47.4% of comments. |
| Baumel et al., 202289 | NR | NR | Videos using the search function under both “#Pfizer” and “#Moderna,” the 100 “most liked” videos under each “hashtag” were chosen for analysis. | TikTok | According to the comments, there were 20.6% positive sentiments towards Pfizer and 56.8% positive sentiments towards Moderna. 35.2% neutral sentiments towards Pfizer and 14.4% neutral sentiments towards Moderna. 44.2% negative sentiments towards Pfizer and 28.8% negative sentiments towards Moderna |
| Biswas et al., 2022185 | Kingdom of Saudi Arabia, Egypt, United Arab Emirates, Jordan, and Qatar | 01 August 2009 to 31 December 2019 (T1) and 01 January 2020 to 15 February 2021(T2). Users who posted at least one tweet in both periods were included in the study data | Public posts | In T1, 48.05% of tweets were positive, and 16.47% of tweets were negative. In T2, 43.03% of tweets were positive, and 20.56% of tweets were negative. Among the Twitter users, the sentiment of 15.92% users shifted towards positive, and the sentiment of 17.90% users shifted towards negative. Public sentiment that have shifted towards positive may be due to the hope of vaccine efficacy, whereas public sentiment that have shifted towards negative may be due to the concerns related to vaccine side effects and misinformation. | |
| Carrasco-Polaino et al., 2021191 | NR | Data was obtained during the first four days after efficacy data were announced for each of the four vaccines that had made their results public before November 30, 2020 (Pfizer, November 9; Sputnik V, November 11; Moderna, November 16; Oxford-AstraZeneca, November 23) | 49,776 interactions by 25,692 Twitter users, of which 2970 were original tweets. | The polarity analysis tool used indicated that the average overall sentiment towards all COVID-19 vaccines was moderately favorable or positive (M = 0.11; SD=0.19). When polarity or sentiment towards each vaccine was analyzed, the results showed that polarity values could be grouped into three levels with Pfizer (M = 0.16; SD=0.198) and Moderna (M = 0.16; SD=0.19) as the vaccines with the highest positive values. A second group was found within the set of three vaccines of Chinese origin (M = 0.13; SD=0.17) and the Oxford vaccine (M = 0.12; SD=0.18) with lower mean relative positive sentiment values for the Russian Sputnik V vaccine (M = 0.098; SD=0.19). The analysis revealed that these differences were statistically significant (p<0.001). | |
| Gao et al., 2021192 | China | December 25, 2020 to January 7, 2021 | Weibo posts with the key search word “COVID-19 vaccines” | Sina Weibo | Both positive and negative emotions increased among the public after the official announcement. “Good” was the most increased positive emotion and indicated great public appreciation for the production capacity and free vaccination. “Fear” was the significantly increased negative emotion and reflected the public concern about the safety of the vaccines. |
| Gao et al., 2021193 | China | 23 to 26 July 2021 | Weibo posts with the hashtag #most of the confirmed cases in Nanjing had been vaccinated | Sina Weibo | 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine's effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. |
| Gori et al., 2021194 | Italy | October 2020-January 2021 | Public posts | Based on the annotated tweets, 29.6% of the 2538 unique users as anti-Vax and 12.1% as pro-Vax were identified, with a strong disagreement in annotation in 7.1% of the tweets. | |
| Hernandez-Garcia et al., 202188 | 63.6% of the videos originated from Mexico and the USA | February 2021 | Videos | YouTube | A total of 118 videos were analyzed; the media created 57.6% of the videos. Positive tone was observed in 53.4%. The most discussed topics were target groups for vaccination (38.9%) and safety (43.2%). A significantly smaller number of likes was obtained in videos of media compared to those created by health professionals (p = 0.004). Videos made by health professionals, compared to those of media, showed a greater positive tone (OR = 3.09). Hoaxes/conspiracy theories were identified in 1.7% of the videos. |
| Herrera-Peco et al., 202156 | NA | December 2020 | Public posts written in Spanish language, under the hashtag #yonomevacuno | The anti-COVID-19 vaccine stream accounted for a total of 31.05% of the tweets, followed by the group of tweets that did not express any specific opinion (28.85%), with conspiracy theory tweets (16.97%) being the third main tendency. There is the existence of a series of tweets from users in favor of the COVID-19 vaccine (4.15%). | |
| Huangfu et al., 2022186 | NR | December 14, 2020, to April 30, 2021 | Public posts | Overall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). | |
| Jahanbin et al., 202190 | NA | December 2020 | Public posts | The result of this study showed that 591,053 tweet (52%) were in positive sentiment, 382,431 (34%) neutral sentiment and 152,653(14%) negative sentiment about vaccine of COVID-19. | |
| Jin et al., 2020200 | NA | January-July 2020 | Public videos | YouTube | Coronavirus video content was divided into three subgroups of public-health intervention-related videos: individual interventions, government interventions, and medical interventions, as well as seven video title narratives. Over time, engagement for the intervention video subgroups has increased whereas the diffusion for other non-intervention videos has decreased suggesting that information about COVID-19 interventions has become more popular as the pandemic develops. Engagement is lowest overall on medical intervention videos, which may be due to vaccine and treatment development as a topic being downgraded quickly from YouTube's search results. |
| Karami et al., 202196 | USA | November 2020 and February 2021 | Public posts | The negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. The vaccination in the U.S. was started on 14 December 2020. This result indicates that U.S. public sentiment has become less negative during the two months after starting the vaccination. In total, 33.64% and 66.36% of tweets were negative and non-negative, respectively. | |
| Melton et al., 2021195 | NR | Dec 1, 2020, to May 15, 2021 | 13 Reddit communities consisted of 1401 posts and 10,240 comments (11,641 in total) focusing on the COVID-19 vaccine | The polarity analysis found that 56.68% of the posts measured positive, 27.69% were negative, and 15.63% neutral. The subjectivity analysis reported 73.15% of the comments measured in between [0.25, 0.75] and considered "neutrally subjective", 18.13% were reported to be minimally subjective (less than 0.25) while the remaining 8.72% were highly subjective (greater than 0.75). Public sentiment in Reddit communities is overall positive regarding discussions about the Covid-19 vaccine or experiences with taking the vaccine, keywords and topics were detected that indicate some hesitancy amongst these users. | |
| Mir and Gul 2021196 | NR | NR | Tweets with hashtags “covid19vaccine” and “coronavirusvaccine” | Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments. | |
| Nezhad and Deihimi 202195 | Iran | April 1, 2021 and September 30, 2021 | Tweets, mentioning COVIran Barekat (the homegrown vaccine), Pfizer/BioNTech, AstraZeneca/Oxford, Moderna, and Sinopharm (imported vaccines) | The positive sentiments towards foreign vaccines accounted for 43% of tweets (n = 173,048), followed by the negative sentiments for 45% and the neutral sentiments for 12%, respectively. On the other hand, the positive sentiments towards the homegrown vaccine accounted for 40% of the tweets (n = 160,335), followed by the negative sentiments for 40% and the neutral sentiments for 20%. | |
| Pratama et al., 202193 | Indonesia | NR | Public posts | The results of this study imply that the sentiment in the form of negative opinions is very large, namely 75%, positive opinions 20% and neutral opinions 5%. | |
| Praveen et al., 202191 | India | Across different months of the year 2020 | Public posts | 47% of social media posts discussing vaccines were in a neutral tone, and nearly 17% of the social media posts discussing the COVID-19 vaccine were in a negative tone. Fear of health and allergic reactions towards the vaccine are the two prominent issues that concern Indian citizens regarding the COVID-19 vaccine. | |
| Roe et al., 202198 | UK | 1 July 2021 and 21 July 2021 | 137,781 tweets being specifically related to COVID and Online questionnaire distributed through email and social media platforms of Twitter and Facebook | The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. Through questionnaire analysis it has been found that most of the participants (85.7%) had previously accepted all vaccines they had been offered), 73.8% were not concerned about receiving a COVID-19 vaccination, 17.1% were slightly concerned, 4.3% were very concerned and 4.3% stated that they were impartial. | |
| Sv et al., 202192 | India | March and April 2021 | Public posts | Tweets with positive sentiments about the side effects of the vaccine were 33.6% (n = 63,848 tweets). Tweets with negative sentiments recorded for 21.3% (n = 40,633 tweets). It is an encouraging sign that, even while posting about the side effects of the COVID-19 vaccine, nearly 78.5% of the tweets were with either neutral or positive sentiments. It can also be concluded that the positive sentiments towards the side effects of the COVID-19 vaccine increased to a greater extent from the 2nd week of April (when the total COVID-19 cases began to see a drastic increase). | |
| Yoder et al., 2021197 | NR | January 12, 2020- February 28, 2021 | First fifty accounts with the following terms: fertility doctor, fertility, OBGYN, infertility, TTC, VAX and IVF. | Twitter and Instagram | Sentiments toward the VAX were largely positive for all groups (Physician 90.3%, Individual 71.4%, Fertility Center 70%), or neutral (Physician 9.7%, Individual 28.6%, Fertility Center 30%), with no negative posts identified. Trends in mentions and sentiments were similar on both Instagram and Twitter platforms. |
| Yousefinaghani et al., 2021198 | NR | January 2020 to January 2021 | Public posts | The neutral category accounted for the 41% of the tweets, followed by the positive category accounting for 34% and negative category accounting for 25%. The negative sentiments were related to a range of concerns, but the majority usually focused on vaccine development being time-consuming, doubts in vaccine safety or reaction to governments, political figures and manufacturers. On the other hand, positive tweets were usually about scientific breakthroughs, medical advice and spreading hope. | |
| Zhang et al., 2021199 | China | October 18, 2020, and May 15, 2021 | Public posts | The positivity toward COVID-19 vaccines in China tends to fluctuate over time in the range of 45.7% to 77.0% and is intuitively correlated with public health events. In terms of gender, males were more positive (70.0% of the time) than females. | |
| Zhang et al., 2022187 | NR | June 2020 to July 2021 | Public posts | The whole population's attentiveness toward vaccines was strongly correlated (Pearson r = 0.9512) with official COVID-19 statistics, including confirmed cases and deaths. The attentiveness ratios toward vaccines of organizations were higher than that of individuals at all the time points, with the OR ranging from 1.44 (95% CI 1.28–1.61) to 2.01 (95% CI 1.70–2.39). |
Abbreviations: NR – not reported; NA – not applicable; OR – odds ratio.