Terms that describe why a document is related to another (DTerms)

The second contribution of (Liu 2017) : given two documents, it extracts the words of the titles of the references of one (to) that best indicate in what respect it is related to the other (from). It complements coupling_descriptive(): the weight says how much two documents are related, the terms say why.

Usage

descriptive_terms(
  dt,
  source,
  ref,
  title,
  from,
  to,
  n_terms = 10,
  stopwords = NULL
)

Arguments

dt

A data frame or a data.table of direct citations, one row per link between a citing document and a cited reference. It is not modified.

source

Character scalar. The column of the citing documents: they are the nodes of the coupling network.

ref

Character scalar. The column of the cited references.

title

Character scalar. The column with the title of the cited reference (one title per reference).

from, to

Identifiers of two documents (values of source).

n_terms

Number of terms to return.

stopwords

Character vector of words to ignore in the titles (NULL: none).

Value

A data.table with the term and its strength, in decreasing order.

Details

The descriptive strength of a term \(t\) is (Eq. 6 of the paper)

$$D(t) = \frac{\sum_{r' \in R_{to},\, t \in T(r')} \max_{r \in R_{from}} \mathrm{Sim}(r, r')}{|R_{to}|}$$

with \(\mathrm{Sim}\) the reference similarity of coupling_descriptive(). The n_terms terms with the highest strength are returned (the paper suggests about 10, the length of a title). The relation is not symmetric: swap from and to to explain the relation the other way.

Intuition

A document cites references for each of its issues. The references of to that have a close match among the references of from are the issues the two documents have in common, and the words of their titles name these issues. A word is a good descriptor if it appears in many references of to, and if these references are close to references of from.

References

Liu R (2017). “A New Bibliographic Coupling Measure with Descriptive Capability.” Scientometrics, 110(2), 915–935. doi:10.1007/s11192-016-2196-7 .

See also

Other coupling measures: biblio_coupling(), coupling_angle(), coupling_descriptive(), coupling_jaccard(), coupling_similarity(), coupling_strength(), coupling_vladutz()

Examples

library(biblionetwork)
toy <- data.frame(
  doc = c("a", "a", "b", "b"),
  ref = c("r1", "r2", "r1", "r3"),
  title = c("inflation expectations", "oil supply shocks",
            "inflation expectations", "supply shocks prices")
)
descriptive_terms(toy, "doc", "ref", "title", from = "a", to = "b")
#>            term strength
#>          <char>    <num>
#> 1: expectations     0.50
#> 2:    inflation     0.50
#> 3:       prices     0.25
#> 4:       shocks     0.25
#> 5:       supply     0.25