kai arzheimer

Short of training a hypercomplex and computationally expensive neural network (i.e. a grad student) to look at the actual content of the texts, analysing citation patterns is the most straightforward way to address the research question. Because I needed citation information, I harvested the Social Science Citation Index (SSCI) instead of my own bibliography. The Web of Science interface to the SSCI lets you save records as plain text files, which is all that was required. The key advantage of the SSCI data is that all the sources that each item cites are recorded, too, and can be exported with the title. This includes (most) items that are themselves not covered by the SSCI, opening up the wonderful world of monographs and chapters. To identify the two literatures, I simply ran queries for the phrases “Extreme Right” and “Radical Right” for the 1980-2017 period. I used the “TS” operator to search in titles, abstracts, and keywords. These queries returned 596 and 551 hits, respectively. Easy.

But how far separated are the two strands of the literature? To find out, I first looked at the overlap between the two. By overlap, I mean items that use both phrases. This applies to 132 pieces, or just under 12 per cent of the whole stash. This is not a state of zilch communication, yet by this criterion alone, it would seem that there are indeed two relatively distinct literatures. But what I’m really interested in are (co-)citation patterns How could I beat two long plain text lists of articles and the sources they cite into a usable data set?

When you are asking this kind of question, usually “there is an R package for that”™, unless the question is too silly. In my case, the magic bullet for turning information from the SSCI into crunchable data is the wonderful bibliometrix package. Bibliometrix reads saved records from Web of Science/SSCI (in bibtex format) and converts them into data frames. It also provides functions for extracting bibliometric information from the data. Before I move on to co-citations, here’s the gist of the code that reads the data and generates a handy list of the 10 most-cited titles:

library(bibliometrix)
  D <- readFiles("savedrecs-all.bib")
  M <- convert2df(D, dbsource = "isi", format = "bibtex")
# remove some obviously unrelated items
  M <- M[-c(65,94,96,97,104,105,159,177,199,457,459,497,578,579,684,685,719,723),]
  M <- M[-c(659,707),]
  M <- M[-c(622),]

  results <- biblioAnalysis(M, sep = ";")
  S=summary(object = results, k = 10, pause = FALSE)
  #Citations
  CR <- citations(M, field = "article", sep = ".  ")
  CR$Cited[1:10] 

If this was indeed the case, the literature should display a low degree of separation between users of both labels. Looking for co-citation patterns is a straightforward operationalisation for (lack of) separation. A co-citation occurs when two publications are both cited by some later source. By definition, co-citations reflect a view on the older literature as it is expressed in a newer publication. When two titles from the “Extreme Right” and “Radical Right” literatures are co-cited, this small piece of evidence that the literature has not split into two isolated streams. The SSCI aims at recording every source that is cited, even if the source itself is not in the SSCI. This makes for a very large number of publications that could be candidates for co-citations (18,255), even if most of them are peripheral European Radical Right studies, and a whopping 743,032 actual co-citations.

To get a handle on this, I extracted the 20 publications with the biggest total number of co-citations and their interconnections. They represent something like the backbone of the literature. How did I reconstruct this network from textual data? Once more, R and its packages came to the rescue and helped me to produce a reasonably nice plot (after some additional cleaning up)

NetMatrix <- biblioNetwork(M, analysis="co-citation",network = "references", sep = ".  ")
# Careful: we are not interested in loops and not interested in separate connections between nodes. We convert the latter to weights 
g <- graph.adjacency(NetMatrix,mode="max",diag=FALSE)
# Extract the top 20 most co-cited items
f <- induced_subgraph(g,degree(g)>quantile(degree(g),probs=(1-20/ length(V(g)))))
# Now build a vector of relevant terms (requires knowledge of these titles)
# 1: extreme, 2: radical, 3:none/other
# Show all names
V(f)$name
term <- c(3,2,1,1,2,1,1,2,1,2,3,2,2,2,3,1,1,1,1,1)
mycolours <- brewer.pal(3, "Greys")
V(f)$term <- term
V(f)$color <- mycolours[term]

Co-citation analysis: results

So, what are the results? First, here is the top 20 of co-cited items in the field of Extreme/Radical Right studies:

Many of these titles are familiar, because they also appear in the top ten of most cited titles and are classics to boot. And here is another nugget: for each title, a substantial share of about 10 per cent of all co-citations happen within this top twenty. This is exactly the (sub)network of co-citations I’m interested in. So here is the plot I promised:

Co-citations within top 20 titles in Extreme / Radical Right studies

But what does it all mean? Stay tuned for the next episode, or read the full article (author’s version, no paywall):

• Arzheimer, Kai. “Conceptual Confusion is not Always a Bad Thing: The Curious Case of European Radical Right Studies.” Demokratie und Entscheidung. Eds. Marker, Karl, Michael Roseneck, Annette Schmitt, and Jürgen Sirsch. Wiesbaden: Springer, 2018. forthcoming.
  [BibTeX] [Download PDF] [HTML]

  @InCollection{arzheimer-2018,
  author = {Arzheimer, Kai},
  title = {Conceptual Confusion is not Always a Bad Thing: The Curious Case of
  European Radical Right Studies},
  booktitle = {Demokratie und Entscheidung},
  publisher = {Springer},
  address = {Wiesbaden},
  pages = {forthcoming},
  year = 2018,
  url =
  {https://www.kai-arzheimer.com/conceptual-confusion-european-radical-right-studies.pdf},
  html =
  {https://www.kai-arzheimer.com/conceptual-confusion-european-radical-right-studies},
  editor = {Marker, Karl and Roseneck, Michael and Schmitt, Annette and Sirsch,
  Jürgen},
  dateadded = {01-06-2018}
  }