Introduction

The United Nations’ Sustainable Development Goals (SDGs) have become an important guideline for organizations to monitor and plan their contributions to social, economic, and environmental transformations. Existing approaches to identify efforts addressing each of the 17 goals rely on economic indicators or, in the case of academic output, on search engines of academic databases. text2sdg is the first open source, multi-system tool to detect SDGs in text.

The text2sdg package consists of five functions: detect_sdg(), detect_sdg_systems(), detect_any(), plot_sdg(), and crosstab_sdg(). The function detect_sdg() carries out the detection of SDGs in text using a trained machine learning model, whereas detect_sdg_systems() does so using up to six different query systems. The function detect_any() enables search for custom search queries. Finally, the functions plot_sdg() and crosstab_sdg() help visualize and analyze the resulting SDG matches.

Detecting SDGs using detect_sdg()

The detect_sdg() function identifies SDGs in texts that are provided via the text argument. Inputs to text must either be a character vector or an object of tCorpus from package corpustools. text is the only non-default argument of the function, which means that the function can be run with only the texts as input. The output of the function is a tibble with one row per match including the following columns (and types):

  • document (factor) - index of element in the character vector or corpus supply for text
  • sdg (character) - labels indicating the matched SDGs
  • system (character) - the query system that produced the match
  • hit (numeric) - running index of matches

The detect_sdg() function implements a ensemble model based on a random forest architecture that pools the predictions of six labeling systems generated using detect_sdg_systems() and also considers text length. The ensemble model can be run for any subset of the 17 SDGs by using the sdgs argument. Our research shows that the ensemble model implemented by detect_sdg() outperforms any individual labeling system and the OSDG tool developed by the UN SDG AI Lab (Wulff, Meier, & Mata, 2023).

The example below runs the detect_sdg() function for the projects data set included in the package and prints the results. The data set is a character vector containing 500 descriptions of randomly selected University of Basel research projects that were funded by the Swiss National Science Foundation (https://p3.snf.ch). The analysis produced a total of 62 matches.

# detecting SDGs in projects
hits_default <- detect_sdg_systems(projects)
hits_default

Detecting SDGs using detect_sdg_systems()

The detect_sdg_systems() function generates SDG labels by using up to six different SDG labeling systems. The function works similarly to detect_sdg() but has additional arguments to control which systems are run and the aggregation of the output. The output is a tibble with one row per match including the following columns (and types):

  • document (factor) - index of element in the character vector or corpus supply for text
  • sdg (character) - labels indicating the matched SDGs
  • system (character) - the query system that produced the match
  • query_id (integer) - identifier of query in the query system
  • features (character) - words in the document that were matched by the query
  • hit (numeric) - running index of matches for each query system

The example below runs the detect_sdg_systems() function for the projects data. The analysis produced a total of 835 matches using the four default query systems Aurora, Elsevier, Auckland, and SIRIS.

# detecting SDGs in projects
hits_default <- detect_sdg_systems(projects)
hits_default

Selecting query systems

By default sdg_detect_systems() runs the four query systems, Aurora, Elsevier, Auckland, and SIRIS. Using the function’s system argument the user can control which systems are run. There are two additional query systems that can be selected, SDSN and SDGO. These two systems are much simpler and less restrictive than the former four as they only rely on basic keyword matching, whereas the four default systems are based on more complex search queries. As a result, the four default systems are more specific but also more prone to misses, whereas the two optional systems are more sensitive but also more prone to false alarms. Our research suggests that the four default systems are likely more accurate in balanced datasets including SDG-related and SDG-unrelated content (Wulff, Meier, & Mata, 2023). More information about the systems can be gathered from the help files of the respective query data frames, aurora_queries, elsevier_queries, auckland_queries, siris_queries, sdsn_queries, and sdgo_queries.

The example below runs the detect_sdg_systems() on the projects using all query systems, including the two keyword-based systems. The resulting tibbles reveal that Aurora is most conservative (60 hits), followed by SIRIS (166), Elsevier (235), Auckland (374), and then by large margin the two keyword-based systems SDSN (2,589) and SDGO (3,629). Note that the high numbers for the two keyword-based systems imply that, on average, 5 and 7 SDGs, respectively, are identified per document.

# detecting SDGs using all query systems
# hits_all <- detect_sdg_systems(projects,
#                        system = c("Aurora", "Elsevier", "Auckland", "SIRIS", "SDSN", "SDGO"))
hits_all <- detect_sdg_systems(projects,
                       system = c("Aurora", "Elsevier", "Auckland", "SIRIS", "SDSN"))
#> 
#> Running Aurora
#> Running Elsevier
#> Running Auckland
#> Running SIRIS
#> Running SDSN

# hits_all <- detect_sdg_systems(projects,
#                        system = c("SDGO"))

# count hits of systems
table(hits_all$system)
#> 
#> Auckland   Aurora Elsevier     SDSN    SIRIS 
#>      374       60      235     2589      166

Selecting SDGs

By default the detect_sdg() and detect_sdg_systems() will try to detect all 17 SDGs. However, the user can limit the set of SDGs sought in text using the sdgs argument, which takes a numeric vector with integers in [1,17] as input. When using the detect_sdg_systems() function, the user should note that Elsevier, SIRIS and Auckland contain queries only for the first 16 SDGs, exluding queries for goal 17 - Global Partnerships for the Goals.

The example below runs the detect_sdg_systems() function only for SDGs 1, 2, 3, 4, and 5.

# detecting only for SDGs 1 to 5
hits_sdg_subset <- detect_sdg_systems(projects, sdgs = 1:5)
#> 
#> Running Aurora
#> Running Elsevier
#> Running Auckland
#> Running SIRIS
hits_sdg_subset
#> # A tibble: 489 × 6
#>    document sdg    system   query_id features                                hit
#>    <fct>    <chr>  <chr>       <int> <chr>                                 <int>
#>  1 1        SDG-03 Auckland        3 tuberculosis, human, tuberculosis, d…     1
#>  2 1        SDG-03 Elsevier        3 tuberculosis, human, tuberculosis, d…     1
#>  3 2        SDG-03 Auckland        3 SARs                                      2
#>  4 3        SDG-03 Auckland        3 immunology, medicine                      3
#>  5 6        SDG-03 Auckland        3 cancer                                    4
#>  6 6        SDG-03 Elsevier        3 cancer                                    2
#>  7 8        SDG-03 Auckland        3 epidemics, epidemics, vaccine             5
#>  8 8        SDG-03 Elsevier        3 vaccine                                   3
#>  9 9        SDG-03 Auckland        3 primary, care, primary, care, Primar…     6
#> 10 12       SDG-03 Auckland        3 epidemics                                 7
#> # … with 479 more rows

Controlling the output

By default, detect_sdg_systems() returns matches at the level of query. If a system has multiple queries for a single SDG, the output can include multiple hits (and rows) per document and system. Separating hits by queries can be useful because different queries typically capture different aspects of a given SDGs, which will be revealed through the keywords that were matched by the queries. These keyword matches are shown in the column features and, hence, we refer to thsi type of output as "features". If the user is not interested in separating matches by queries and only cares about matches at the level of documents, a reduced output can be selected by setting the output argument to "documents". In this case, the detect_sdg_systems() returns a tibble that includes only distinct matches of document, system, and sdg combinations, concatenates the values of features into a single character string, and drops the column query_id.

The example below shows the alternative output resulting from setting output = "documents".

# return documents output format
detect_sdg_systems(projects, output = "documents")
#> 
#> Running Aurora
#> Running Elsevier
#> Running Auckland
#> Running SIRIS
#> # A tibble: 791 × 5
#>    document sdg    system   n_hit features                                      
#>    <fct>    <chr>  <chr>    <int> <chr>                                         
#>  1 1        SDG-03 Auckland     1 tuberculosis, human, tuberculosis, disease    
#>  2 1        SDG-03 Elsevier     1 tuberculosis, human, tuberculosis, disease    
#>  3 2        SDG-03 Auckland     1 SARs                                          
#>  4 3        SDG-03 Auckland     1 immunology, medicine                          
#>  5 6        SDG-03 Auckland     1 cancer                                        
#>  6 6        SDG-03 Elsevier     1 cancer                                        
#>  7 8        SDG-03 Auckland     1 epidemics, epidemics, vaccine                 
#>  8 8        SDG-03 Elsevier     1 vaccine                                       
#>  9 9        SDG-03 Auckland     1 primary, care, primary, care, Primary, care, …
#> 10 12       SDG-03 Auckland     1 epidemics                                     
#> # … with 781 more rows

Keeping track of progress

By default the detect_sdg() and detect_sdg_systems() function prints messages to communicate the progress of the underlying SDG detection process, which can take several minutes. To suppress these messages, the user can set the verbose argument to FALSE.

Custom search with detect_any()

The detect_any() function permits specification of custom query systems. The function operates similarly to detect_sdg_systems(), but it requires an additional argument queries that specifies the queries to be employed. The queries argument expects a tibble with the following columns:

  • system (character) - names used to label query systems.
  • queries (character) - queries used in search.
  • sdg (integer) - mapping of queries to SDGs.

The queries in the custom query set can be Lucene-style queries following the syntax of the corpustools package. See vignette("corpustools"). This is illustrated in the example below. First, a tibble of three queries is defined that includes a single system and three queries that are mapped onto two sdgs, 3 and 7. The first query represents a simple keyword search, whereas queries 2 and 3 are proper search queries using logical operators.

# definition of query set
my_queries <- tibble::tibble(system = "my_system",
                              query = c("theory", "analysis OR analyses OR analyzed", "study AND hypothesis"),
                              sdg = c(3,7,7))

# return documents output format
detect_any(text = projects, 
           system = my_queries)
#> # A tibble: 280 × 6
#>    document sdg    system    query_id features             hit
#>    <fct>    <chr>  <chr>        <dbl> <chr>              <int>
#>  1 2        SDG-07 my_system        2 analysis              62
#>  2 4        SDG-07 my_system        2 analysis             189
#>  3 8        SDG-07 my_system        2 analyses, analysis   267
#>  4 9        SDG-07 my_system        2 analysis             274
#>  5 10       SDG-07 my_system        2 analysis, analyses     1
#>  6 11       SDG-07 my_system        2 analysis               6
#>  7 13       SDG-07 my_system        3 hypothesis, study     19
#>  8 15       SDG-07 my_system        2 analysis              28
#>  9 15       SDG-07 my_system        3 study, hypothesis     29
#> 10 16       SDG-07 my_system        2 analyzed              39
#> # … with 270 more rows

Visualizing hits with plot_sdg()

To visualize the hits produced by the detect_* functions, the text2sdg package provides the function plot_sdg(). The function produces barplots illustrating either the hit frequencies produced by the different query systems. It is built on the ggplot2 package, which provides high levels of flexibility for adapting and extending its visualizations.

By default plot_sdg() produces a stacked barplot of absolute hit frequencies. Frequencies are determined on the document level for each system and SDGs present in the tibble that was provided to the function’s hits argument. If multiple hits per document, system and SDG combination exist, the function returns a message of how many duplicate hits have been suppressed.

The example below produces the default visualization for the hits of all five systems. Since the object was created with "output = features", the function informs that a total of 2490 duplicate hits were removed.

# show stacked barplot of hits
plot_sdg(hits_all)
#> 765 duplicate hits removed. Set remove_duplicates = FALSE to retain duplicates.

Adjusting visualizations

The plot_sdg() function has several arguments that permit adjustment of the visualization. The systems and sdgs arguments can be used to visualize subsets of systems and/or SDGs. The normalize argument can be used to normalize the absolute frequency by the number of documents (normalize = "documents") or by the total number of hits within a system (normalize = "systems"). The color argument can be used to adapt the color set used for the systems. The sdg_titles argument can be used to add the full titles of the SDGs. The remove_duplicates argument can be used to retain any duplicate hits of document, system, and SDG combinations. Finally, the ... arguments can be used to pass on additional arguments to the geom_bar() function that underlies detect_sdg_systems().

The example below uses some of the available arguments to make adjustments to the default visualization. With normalize = "systems" and position = "dodge", an argument passed to geom_bar(), it shows the proportion of SDG hits per system with bars presented side-by-side rather than stacked. Furthermore, due to sdg_titles = TRUE the full titles are shown rather than SDG numbers.

# show normalized, side-by-side barplot of hits
plot_sdg(hits_all, 
         sdg_titles = TRUE,
         normalize = "systems", 
         position = "dodge")
#> 765 duplicate hits removed. Set remove_duplicates = FALSE to retain duplicates.

Extending visualizations with ggplot2

Because plot_sdg() is implemented using ggplot2, visualizations can easily be extended using functions from the ggplot2 universe. The example below illustrates this. Using the facet_wrap function separate panels are created, one for each system, that show the absolute frequencies of hits per SDG.

# show system hits in separate panels
plot_sdg(hits_all) + 
  ggplot2::facet_wrap(~system, ncol= 1, scales = "free_y")
#> 765 duplicate hits removed. Set remove_duplicates = FALSE to retain duplicates.

Analyzing hits using crosstab_sdg()

To assist the user in understanding the relationships among SDGs and query systems, the text2sdg package provides the crosstab_sdg() function. The function takes as input the tibble of hits produced by any of the detect functions and compares hits between either systems or SDGs. Comparing hits by system means that correlations are determined across all documents and all SDGs for every pair of systems to produce a fully crossed table of system correlations. Conversely, comparing hits by SDG means that correlations are determined across all documents and all systems for every pair of SDGs to produce a fully crossed table of SDG correlations.

Correspondence between query systems

By default the crosstab_sdg() function compares systems, which is illustrated below for the hits for all five systems. Note that the crosstab_sdg() function only considers distinct combinations of documents, systems, and SDGs implying that the output type of detect_sdg_systems() does not matter; it will automatically treat the hits as if they had been produced using output = documents.

The analysis reveals two noteworthy results. First, correlations between systems are overall rather small. Second, query systems are more similar to systems of the same type, i.e., query or keyword-based.

# evaluate correspondence between systems 
crosstab_sdg(hits_all) %>% round(2)
#>          Auckland Aurora Elsevier SDSN SIRIS
#> Auckland     1.00   0.26     0.75 0.27  0.32
#> Aurora       0.26   1.00     0.24 0.12  0.30
#> Elsevier     0.75   0.24     1.00 0.23  0.27
#> SDSN         0.27   0.12     0.23 1.00  0.19
#> SIRIS        0.32   0.30     0.27 0.19  1.00

When crosstab_sdg() evaluates the correspondence between query systems it does not distinguish between hits of different SDGs. Correlations for individual SDGs could be different from the overall correlations, and it is likely that they are higher, on average. To determine the correspondence between query systems for individual SDGs, the user can use the sdgs argument. For instance, sdgs = 1 will only result in a comparison of systems using only hits of SDG 1.

Correspondence between SDGs

The crosstab_sdg() can also be used to analyze, in a similar fashion, the correspondence between SDGs. To do this, the compare argument must be set to "sdgs". Again, correlations are calculated for distinct hits, while ignoring, in this case, the systems from which the hits originated.

The example below analyzes the correspondence of all SDGs across all systems. The resulting cross table reveals strong correspondences between certain pairs of SDGs, such as, for instance, between SDG-01 and SDG-02 or between SDG-07 and SDG-13.

# evaluate correspondence between systems 
crosstab_sdg(hits_all, compare = "sdgs") %>% round(2)
#>        SDG-01 SDG-02 SDG-03 SDG-04 SDG-05 SDG-06 SDG-07 SDG-08 SDG-09 SDG-10
#> SDG-01   1.00   0.50   0.08   0.20   0.16   0.23   0.13   0.28   0.40   0.29
#> SDG-02   0.50   1.00   0.09   0.13   0.09   0.21   0.12   0.23   0.35   0.19
#> SDG-03   0.08   0.09   1.00   0.08   0.11  -0.01  -0.02   0.08   0.10   0.24
#> SDG-04   0.20   0.13   0.08   1.00   0.17   0.08   0.03   0.10   0.17   0.26
#> SDG-05   0.16   0.09   0.11   0.17   1.00   0.17   0.08   0.15   0.20   0.28
#> SDG-06   0.23   0.21  -0.01   0.08   0.17   1.00   0.21   0.21   0.25   0.12
#> SDG-07   0.13   0.12  -0.02   0.03   0.08   0.21   1.00   0.14   0.34   0.07
#> SDG-08   0.28   0.23   0.08   0.10   0.15   0.21   0.14   1.00   0.46   0.35
#> SDG-09   0.40   0.35   0.10   0.17   0.20   0.25   0.34   0.46   1.00   0.37
#> SDG-10   0.29   0.19   0.24   0.26   0.28   0.12   0.07   0.35   0.37   1.00
#> SDG-11   0.32   0.27   0.07   0.11   0.15   0.45   0.27   0.26   0.45   0.29
#> SDG-12   0.29   0.32   0.04   0.05   0.12   0.42   0.41   0.23   0.40   0.19
#> SDG-13   0.08   0.09  -0.08   0.00   0.00   0.21   0.28   0.07   0.15   0.01
#> SDG-14   0.07   0.10  -0.07  -0.02   0.03   0.14   0.04   0.05   0.06  -0.01
#> SDG-15   0.13   0.24   0.02   0.00   0.03   0.24   0.14   0.17   0.20   0.06
#> SDG-16   0.21   0.14   0.04   0.21   0.38   0.15   0.05   0.21   0.19   0.31
#> SDG-17   0.09   0.02   0.03   0.12   0.05  -0.01  -0.01   0.02   0.03   0.07
#>        SDG-11 SDG-12 SDG-13 SDG-14 SDG-15 SDG-16 SDG-17
#> SDG-01   0.32   0.29   0.08   0.07   0.13   0.21   0.09
#> SDG-02   0.27   0.32   0.09   0.10   0.24   0.14   0.02
#> SDG-03   0.07   0.04  -0.08  -0.07   0.02   0.04   0.03
#> SDG-04   0.11   0.05   0.00  -0.02   0.00   0.21   0.12
#> SDG-05   0.15   0.12   0.00   0.03   0.03   0.38   0.05
#> SDG-06   0.45   0.42   0.21   0.14   0.24   0.15  -0.01
#> SDG-07   0.27   0.41   0.28   0.04   0.14   0.05  -0.01
#> SDG-08   0.26   0.23   0.07   0.05   0.17   0.21   0.02
#> SDG-09   0.45   0.40   0.15   0.06   0.20   0.19   0.03
#> SDG-10   0.29   0.19   0.01  -0.01   0.06   0.31   0.07
#> SDG-11   1.00   0.43   0.21   0.12   0.22   0.19   0.04
#> SDG-12   0.43   1.00   0.20   0.14   0.29   0.13   0.02
#> SDG-13   0.21   0.20   1.00   0.23   0.32  -0.02  -0.01
#> SDG-14   0.12   0.14   0.23   1.00   0.17   0.01  -0.01
#> SDG-15   0.22   0.29   0.32   0.17   1.00   0.02   0.07
#> SDG-16   0.19   0.13  -0.02   0.01   0.02   1.00   0.11
#> SDG-17   0.04   0.02  -0.01  -0.01   0.07   0.11   1.00