The recent rise of publications in predatory journals is a big issue in the academic community and it hurts the development of science. This is especially true for developing countries such as Brazil, where the academic evaluation system is not yet well established. Surprinsingly, even standard impact assessment systems such as JCR and SJR are not immune to predatory publishers.

Source: Leonid Schneider blog.

One of the problems in doing empirical research regarding predatory journals is that the only available resource for their identification is Beall’s list. While it is certainly useful and its author deserves all the credit we can give him, the information in the site is unstructured as it only shows the names and links of predatory publishers and journals.

As part of a research paper related to the analysis of predatory publications in Brazil, I’m building a database of predatory journals based on Beall’s site. While the stand-alone journals are easy to collect information using web scrapping algorithms, the part of the site for predatory publishers is not. In this sections, each website has its own format for several journals and it would be impossible to get information from all of them in the same way. In order to solve that, we are doing manual collection of the required information and storing it in a csv file.

Based on this file, the package predatory makes it easy for researchers and librarians to check whether a particular journal or publisher is in Beall’s list or not. Not only that, it also allows direct access to the database that we are building. A shiny app is also available in https://msperlin.shinyapps.io/shiny-predatory/.

BE AWARE that the database is not yet complete. The collection of the data is manual and it takes time. The package will be updated in a regular basis.

Examples of usage

Name or issn lookup

Let’s say you have the name of a journal called Biomedical Laboratory and Clinical Research that you want to check whether it is in Beall’s list or not. For that, you can simply use the following code:

library(predatory)

## Thank you for using predatory! I also built a simple shiny app with similar funcionality.
## It is available at:
## 
## https://msperlin.shinyapps.io/shiny-predatory/

name <- 'Biomedical Laboratory and Clinical Research'
temp <- find.predatory(x = name)

## 
## Found 1 row(s)

temp

## # A tibble: 1 × 3
##          publisher                                journal_name      issn
##              <chr>                                       <chr>     <chr>
## 1 1088 Email Press Biomedical Laboratory and Clinical Research 2473-3423

As you can see, this journal is published by 1088 Email Press and has an issn of 2473-3423.

Another example would be to look for a journal based on its ISSN number. Let’s try the value 0028-0836.

my.issn <- '00208-0836'
temp <- find.predatory(x = my.issn, by = 'issn')

## 
## Found 0 row(s)

temp

## # A tibble: 0 × 3
## # ... with 3 variables: publisher <chr>, journal_name <chr>, issn <chr>

This time, however, the search returned a dataframe with 0 length. The result is not surprising since the issn belongs to the journal Nature.

Partial lookup

Another possibility of usage is to look for all predatory journals within a particular subject. Let’s try all journals that have the word finance in its title.

my.str <- 'finance'
temp <- find.predatory(x = my.str)

## 
## Found 134 row(s)

head(temp)

## # A tibble: 6 × 3
##                                  publisher
##                                      <chr>
## 1 Academic and Business Research Institute
## 2 Academic and Business Research Institute
## 3       Academic and Scientific Publishing
## 4       Academic and Scientific Publishing
## 5       Academic and Scientific Publishing
## 6       Academic and Scientific Publishing
## # ... with 2 more variables: journal_name <chr>, issn <chr>

This time we found 134 journals that are related to finance.

Acessing the database of predatory journals

The database of predatory journals is available within the package. It is stored as a csv file in the inst/extdat folder. If you are interested in its contents, you can find it using command system.file("extdata", 'predpub.csv', package = "predatory") or simply calling function Get_PredPubTable from the package, as follows:

df.predpub <- get.predpubTable()

head(df.predpub)

## # A tibble: 6 × 3
##          publisher
##              <chr>
## 1 1088 Email Press
## 2 1088 Email Press
## 3 1088 Email Press
## 4 1088 Email Press
## 5 1088 Email Press
## 6 1088 Email Press
## # ... with 2 more variables: journal_name <chr>, issn <chr>

str(df.predpub)

## Classes 'tbl_df', 'tbl' and 'data.frame':    9789 obs. of  3 variables:
##  $ publisher   : chr  "1088 Email Press" "1088 Email Press" "1088 Email Press" "1088 Email Press" ...
##  $ journal_name: chr  "Advanced Research on Economics and Management" "Biomedical Laboratory and Clinical Research" "International Journal of Advanced Science, Industry and Society" "Journal of Advanced Education" ...
##  $ issn        : chr  "2473-3474" "2473-3423" "2473-3431" "Pending" ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 3
##   .. ..$ publisher   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ journal_name: list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ issn        : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"

Happy hunting!