Of the 2517 respondents, 2417 were included in the analysis based on the inclusion/exclusion criteria. The age of participants showed 446 (18.5%) people aged 18–24, 715 (29.6%) people aged 25–34, 591 (24.5%) people aged 35–44, 394 (16.3%) people aged 45–54, 189 (7.8%) people aged 55–64 and 82 (3.4%) people over the age of 65. Females accounted for 80.1% of the respondents (n = 1937 people) and males accounted for 18.8% (n = 454 people). Respondents were predominantly North American with 70.3% (n = 1700) from the USA and 12.9% (n = 312) from other North American Countries. The remaining were divided into 7.4% (n = 180) Australia/Oceanic, 7.3% (n = 176) European, 0.9% (n = 22) Asian, 0.6% (n = 15) African, 0.5% (n = 11) South American, and 1 respondent from Antarctica. The education levels showed that 0.1% (n = 2) had no formal schooling, 0.4% (n = 10) completed elementary school (grade level 1–8), 21.2% (n = 509) completed high school (grade level 9–12/13), 22.3% (n = 540) completed an Associates (2 year) degree, 34.5% (n = 833) completed a Bachelor (4 year) degree, 13.9% (n = 336) completed a Master’s degree, and 7.7% (n = 187) completed a Professional degree (PhD, MD, DC, DO, etc.). Individuals identified themselves as Lower class socioeconomic status (SES) comprised 9.8% (n = 238) of the population, 82.2% (n = 1987) as Middle class SES, and 7.9% (n = 192) as Upper class SES.
Facebook is the most commonly used social media type in the population with 69.8% (n = 1688), followed by Twitter 15.6% (n = 378), Instagram 12.9% (n = 311), then other forms of social media 1.7% (n = 40). Other forms of social media were identified as Snapchat, Tumblr, Reddit, Pinterest, or using all platforms equally. Most people 47.9% (n = 1158) claimed to only spend 0–2 h on social media daily, followed by 40.9% (n = 989) using 3–4 h, 8.4% (n = 204) using 5–6 h, 1.5% (n = 36) using 7–8 h, and 1.2% (n = 30) using social media for over 9 h. Most respondents 92.7% (n = 2240) have seen posts on social media about vaccines and only 7.3% (n = 177) have not. These posts influence 5.4% (n = 130) of users to think vaccines are worse than previously thought, 13.6% (n = 328) to think vaccines are better than previous thought, 76.4% (n = 1846) claim to not have been influenced by the posts, and 4.7% (n = 113) had not seen any posts. Lastly, people claimed to trust doctors 89.4% (n = 2160) the most with their immunization related information/decisions. The remaining people trust the internet 4.1% (n = 100), family 2.0% (n = 48), peers and friends 2.3% (n = 55), social media 0.2% (n = 5) and the government 2.0% (n = 49) with their information and decisions.
Knowledge
Table 1, found in the supplementary document 2 labeled “Tables”, depicts the frequency of knowledge scores in the sample population. As described in the methods, knowledge scores are based on a scale from 0 to 12 derived from 6 questions with answers ranked from 0 to 2 points. Scores toward 0 represent negatively skewed knowledge, or lack of correct information. Scores toward 12 represent positively skewed knowledge, or adequate vaccine knowledge. Scores of 6 represent uncertainness.
Analysis of all demographic questions against the respondent’s knowledge score was completed by Welch and then further analyzed by Games Howell. Explanations of why these tests were chosen can be found in Methods. When age was compared with knowledge scores a Welch statistical value of 0.763 and the significance of 0.576 (p > 0.05). Post hoc was not necessary.
Gender analysis showed a Welch statistic of 1.627 with a significance value of 0.204 (p > 0.05). Post hoc analysis was not examined because there was no significance.
Geographical Welch testing showed a statistic value of 11.552 with a significance of < 0.001(p < 0.05). Since this value is statically significant post hoc analysis was examined. North Americans (USA) has significantly lower knowledge scores compared to Europe (mean difference − 0.78309, significance < 0.001), and Australia/Oceania (means difference − 0.84316, significance < 0.001). North Americans (Other) also showed significantly lower knowledge scores compared to Europe (means difference − 0.76122, significance 0.001) and Australia/Oceania (means difference − 0.84316, significance 0.001). Values from Asia, Africa, and South American should be looked at with caution because of low responses. Antarctica was excluded from these calculations because there was only one respondent.
Analysis of respondents highest level of education completed showed a Welch statistic of 13.030 and significance of 0.001 (p < 0.05). Post hoc showed that those who completed a Professional degree had significantly higher scores than Bachelor’s degree (means difference 0.55353, significance of 0.007), Associates degree (means difference 1.21578, significance of < 0.001), and high school (means difference 1.11273, significance < 0.001). Those with Masters Degrees were significantly higher scoring than Associate degrees (means difference of 0.80000, significance < 0.001), and high school (means difference of 0.69695, significance of 0.001). Bachelor’s degree holders had significantly higher scores compared to Associates degree (means difference of 0.66224, significance of < 0.001) and High school (means difference of 0.55920, significance of 0.003). Those values from who have no formal school or only completion of elementary school should be looked at with caution due to low frequencies.
Socioeconomic class compared to knowledge scores yielded a Welch statistic of 0.266 and a significance of 0.767 (p > 0.05). No further analysis was needed.
The type of social media used compared to knowledge score showed a Welch statistic of 7.175 and significance of < 0.001(p < 0.05). Games Howell determined that Twitter users had significantly higher scores than Facebook (means difference 0.43812, significance of 0.001) and Instagram (means difference 0.69491, significance of 0.001).
Hours spent on social media showed a Welch statistic of 2.531 and significance of 0.044 (p < 0.05). Post hoc testing showed significantly lower values in those who use social media for 3–4 h compared to 0–2 h (means difference 0.33869, significance of 0.018). No other means from this analysis were significant.
Whether or not a respondent had seen anything on social media about vaccines was not analyzed because there are only 2 categories and therefore the question is noncompliant with the Welch analysis. The influence of vaccine posts on social media had a Welch statistic of 145.202 with a significance of < 0.001 (p < 0.05). Post hoc testing revealed that those who now perceived their opinion of vaccine of being worse than previously thought had significantly lower scores compared to those who now think vaccines are better (means difference − 6.36712, significance of < 0.001), no influence/change in opinion (means difference − 5.83564, significance of < 0.001) and those who had not seen anything (means difference − 4.70483, significance of < 0.001). Those who think vaccines are better after seeing social media posts had significantly higher scores compared to worsened opinions (as mentioned before), those who were not influenced (means difference 0.53148, significance < 0.001) and those who have not seen anything (means difference 1.66229, significance < 0.001). In addition, those who have not been influenced by posts had significantly higher scores than those who have not seen any posts (means difference 1.13081, significance < 0.001).
Lastly, those trusted for immunization related information and decisions was analyzed and found a Welch statistic of 83.032 with significance of < 0.001 (p < 0.05). Post hoc analysis showed those who trusted Doctors the most have significantly higher scores than those who trusted the internet (means difference of 5.32139, significance of < 0.001), family (means difference 5.94306, significance < 0.001), and peers (means difference 6.31957, significance of < 0.001). Those who trusted the government the most also had significantly higher scores than internet (means difference 5.13429, significance < 0.001), family (means difference 5.75595, significance of < 0.001) and peers (means difference 6.13247, significance of < 0.001). Trusting of social media should be looked at with caution due to low frequencies.
Beliefs
Depiction of the frequency of belief scores in the sample population can be found in Table 2, found in the supplementary document 2 labeled “Tables”. As noted in the methods, the remaining 6 questions were scored on a two point scale resulting in a belief score from 0 to 12. Scores toward 0 represent negatively skewed beliefs or belief in common myths. Scores toward 12 represent positively skewed beliefs or disbelief in common myths. Scores of 6 represent uncertainness.
The analysis of the demographic questions against the individual’s belief score was completed by Welch and then further analyzed by Games Howell. Explanations of why these tests were chosen can be found in methods. When age was compared with belief score a Welch statistical value of 2.923 and significance of 0.013 (p < 0.05). Post Hoc revealed 65-year-olds and older had significantly lower scores than 10–24-year-olds (mean difference − 1.37750, significance 0.014) and 24–34 year olds (mean difference − 1.19606, significance 0.047).
Gender analysis showed a Welch statistic of 0.320 with a significance value of 0.728 (p > 0.05). Post hoc analysis was not examined because there was no significance.
Geographical Welch testing showed a statistic value of 29.212 with a significance of < 0.001(p < 0.05). Due to this value being statically significant, post hoc analysis was examined. North Americans (USA) had significantly lower belief scores compared to Europe (mean difference − 1.47989, significance < 0.001), and Australia/Oceania (means difference − 1.81575, significance < 0.001). North Americans (Other) also showed significantly lower belief scores compared to Europe (means difference − 1.29021, significance < 0.001) and Australia/Oceania (means difference − 1.62607, significance < 0.001). Values from Asia, Africa, and South American should be looked at cautiously because of the low response rate. Antarctica not included in these calculations because there was only one individual who responded.
The analysis of individuals with the highest level of education completed showed a Welch statistic of 17.789 and significance of < 0.001 (p < 0.05). Post hoc showed that those who completed a professional degree had significantly higher scores than those with master’s degree (mean difference 0.74516, significance of 0.009), bachelor’s degree (means difference 1.10881, significance of < 0.001), associate’s degree (means difference 2.02797, significance of < 0.001), and high school (means difference 1.97009, significance < 0.001). Respondents with master’s degrees were significantly higher scoring than those with associate degrees (means difference of 1.28280, significance < 0.001), and high school (means difference of 1.22493, significance of < 0.001). Those with bachelor’s degrees had significantly higher scores compared to those with associate’s degrees (means difference of 0.91916, significance of < 0.001) and high school (means difference of 0.86128, significance of < 0.001). The values from those individuals that had no formal schooling, or only completion of elementary school, should be looked at cautiously due to low frequencies.
Socioeconomic class, compared to belief scores, resulting in a Welch statistic of 0.028 and a significance of 0.972 (p > 0.05). No further analysis was needed.
The type of social media users compared to belief score showed a Welch statistic of 8.011 and a significance of < 0.001(p < 0.05). Games Howell determined that Twitter users had significantly higher scores than Facebook (means difference 0.55094, significance of 0.001) and Instagram (means difference 0.98733, significance of < 0.001).
Hours spent on social media showed a Welch statistic of 3.162 and a significance of 0.016 (p < 0.05). Post hoc testing showed significantly lower values in those who used social media for 3–4 h compared to 0–2 h (means difference 0.39195, significance of 0.034). No other means from this analysis were significant.
Exposure to posts on social media about vaccinations was not analyzed because there were only two categories and therefore, noncompliant with the Welch analysis. The influence of vaccine posts on social media had a Welch statistic of 312.900 with a significance of < 0.001 (p < 0.05). Post hoc testing revealed that those who now perceived their opinion of vaccines of being worse than previously thought, had significantly lower scores compared to those who now think vaccines are better (means difference − 7.97280, significance of < 0.001), No influence or change in opinion (means difference − 7.27248, significance of < 0.001), and those who had not seen anything (means difference − 4.97992, significance of < 0.001). Those who thought vaccines were better after seeing social media posts had significantly higher scores compared to worsened opinions, as mentioned before, and to those who were not influenced (means difference 0.70031, significance < 0.001), and those who had not seen anything (means difference 2.99288, significance < 0.001). Also, those who had not been influenced by social media posts had significantly higher scores than those who had not seen any posts at all (means difference 2.29256, significance < 0.001).
Finally, those trusted in immunization related information and decisions were analyzed and found a Welch statistic of 150.953 with a significance of < 0.001 (p < 0.05). Post hoc analysis showed those who trusted doctors the most had significantly higher scores than those who trusted social media (means difference of 6.50713, significance of < 0.001), family (means difference 7.28796, significance < 0.001), and peers (means difference 7.41258, significance of < 0.001). Individuals who trusted the government the most also had significantly higher scores than social media (means difference 6.87265, significance < 0.001), family (means difference 7.65349, significance of < 0.001) and peers (means difference 7.77811, significance of < 0.001). Trusting of social media should be looked at cautiously due to its low frequencies.