The first step was to find out how many scientific publications are available in the NCBI whose title contains the words "Covid-19" and "Conspiracy". That search yielded 501 publications as of April 1, 2023, which is distributed as follows: 104 publications published in 2020, 189 in 2021, 216 in 2022, and 69 in 2023. From all this, it can be seen that there was an increase in the number of publications in 2021 of just over 55%, while in 2022 the increase was 87.5% with respect to the previous year.

Table 1 shows the countries that have published 10 or more scientific papers. The countries with the lowest number of papers are Bangladesh, France, India, Indonesia, Malaysia, and Singapore with 9 publications each, Belgium and Spain with 7, Iran, Oman, Peru, and South Africa with 7, Colombia, Mexico, and Turkey with 6, Argentina, Czech Republic, Finland, Niger and Saudi Arabia with 5, etc.

An examination of the types of publications shows that 470 are Scientific Articles, 20 are Reviews, 13 are Editorial, 10 are Comments, and 8 are Letters. In other words, there is a significant effort made by the scientific community to explain the role of Conspiracy theories in Covid-19.

In the Table 2 shows the h5 value used by Google Scholar indicates the impact of the articles published in that journal in the last 5 years without interruption (in our study, this corresponds to the period between 2017-2021). It highlights the fact that of the nine publications indicated in Table 3, only 3 of them

have an h5 index in Google Scholar which indicates that this topic is not usually published in high impact journals.

The next step was to determine the "Net Prevalence by Covid19" (defined in this manuscript) parameter from the data obtained from Johns Hopkins University as shown in Table 3

As it can be seen in Table 3, the first countries that have a high Net Prevalence value for Covid-19 have a non-negligible number of cases and published works that disprove the Covid-19 conspiracy theories; therefore, it is interesting to carry out further studies to infer that those countries with a high prevalence rate are those countries where more communication campaigns should be sponsored to avoid the propagation of conspiracy theories.

The next step was to determine the number of words in each Abstract. To do this, the longest abstract had 765 words, and to find out if this is a trend in all the papers, we proceeded to determine the average number of words per article, which were 196 words (exactly 195.87 average words per article). The distribution of words per article is shown in Figure 1.

This distribution is not uniform, and to achieve this, we proceeded to calculate the unique number of words in each Abstract. The average word count was 117 words per article (117.29) and the distribution is shown in Figure 2. It should be noticed that 116 publications presented an abstract with only one word.

An important point is that there is a series of words that are frequent in the language and do not contribute anything to the analysis being carried out in the work, known as stopwords, such as "is", "that", "by", "so much", "then", among many others. These have been eliminated according to the methodology described in 24. By eliminating all of them, and counting the most repeated words we obtain that Covid19 is repeated 6106 and SARS-CoV-2 was used 2888 times.

This is a list of the frequency of the 30 most repeated words in the abstracts, it means, 'covid19' (6106) ir., covid19 appears 6106 in all papers, ‘sarscov2’ (2888), 'pandemic' (2026), 'health' (1671), 'origin' (1623), 'study' (1535), 'vaccine' (1513), 'patients' (1456), 'coronavirus' (1454), 'disease' (1366), 'virus' (1093), 'results' (1061), 'conspiracy' (1013), 'infection' (965), 'data' (938), 'public' (888), 'vaccination' (840), 'severe' (815), 'information' (809), 'respiratory' (806), 'analysis' (791), 'may' (759), 'cases' (728), 'social' (714), 'associated' (698), 'among' (684), and 'risk' (657).

The next step is to corroborate that all the Abstracts deal with Conspiracy Theories and COVID-19, and for this purpose, we find the keywords which said the general topics covered in the papers, buy with help of resumer obtained from abstract. The most frequent keywords obtained after applying the resumer are (from most frequent to least frequent) conspiracy (304 times), Covid (192), Belief and Beliefs (192), Theory(ies) (128), Study (110), Vaccine (93), Health (89), Pandemic (86), Vaccination(s) (92), Social (60), Public (45), Trust (38) and Perceived (34). According to this list, it can be seen that most of the papers deal with truth in conspiracy theories where Vaccine and Vaccination(s) have a high occurrence. Figure 3 shows a word cloud with the words that appear most frequently in the cloud where a virus has been used as a template for the figure

Finally, as well as the words, we proceeded to identify phrases that occur more frequently in the texts and that can serve as an orientation of the topics being studied in the works, where phrases such as (the frequency of these phrases was indicated in parentheses):

“this study aimed to evaluate covid 19 vaccine acceptance among” (0,20)

“this study explored the associations between covid 19 conspiracy beliefs” (0,20)

“conspiracy theories during the covid 19 pandemic” (0,29)

“feelings of anxiety and lack of control” (0,29)

“belief in conspiracy theories and the” (0,33)

“belief in covid 19 conspiracy theories” (0,33)

“belief in covid related conspiracy theories” (0,33)

“beliefs conspiracy theories about covid 19” (0,33)

“conspiracy beliefs about covid 19 and” (0,33)

“conspiracy theories related to covid 19” (0,33)

“and covid 19 conspiracy beliefs” (0,40)

“as well as conspiracy mentality” (0,40)

“association between conspiracy mentality and” (0,40)

“belief in the conspiracy theory” (0,40)

“beliefs in conspiracy theories and” (0,40)

“conspiracy beliefs are associated with” (0,40)

“vaccine acceptance results showed that” (0,40)

“and vaccine conspiracy beliefs” (0,50)

“believing in conspiracy theories” (0,50

“compliance with public health” (0,67)

“conspiracy theories was positively” (0,50)

“conspiratorial thinking and the” (0,50)

“covid related conspiracy beliefs” (0,50)

“study examined the association between conspiracy beliefs” (0,29)

“infodemics conspiracy beliefs and religious fatalism” (0,33)

“less support for public health policies” (0,40)

“web interest in conspiracy hypotheses and” (0,40)

“pollution and climate change” (0,50)

“an infodemic of” (0,67)

“anxiety and depression”(0,90)

“and conspiracy mentality” (0,67)

From all the above sentences, it is easy to observe that the studies are focused on the association of the occurrence of Covid-19 cases by a belief in a conspiracy associated with vaccines, where the relationship of the conspiracy with religion could be inferred, but it is a very punctual aspect.

The last (and future work) is to calculate the similarity using the cosine likelihood test, which is based on the cosine mathematical function that seeks to measure how similar texts are once they are reduced to a vector (details in 25), as a result of which a word is represented as a vector so that the text documents are represented in an n-dimensional vector space.