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Paper: Do Birds of a Feather Really Flock Together, or How to Choose Test Samples for Authorship Attribution

Eder, Maciej, Pedagogical University of Kraków, Poland,

Rybicki, Jan, Pedagogical University of Kraków, Poland,

In the house of non-traditional authorship attribution are many mansions, or methods, based on statistical analysis of authorial style. They all compare text samples of disputed or unknown authorship to texts written by known authors, or “candidates”. The degree of similarity or dissimilarity between samples allows informed guesses on the possible authorship of a given text. The so-called machine-learning methods are supposed to be among the most effective; they include Support Vector Machines, Nearest Shrunken Centroid classification, Burrows’ Delta and so on (for a comparison of their effectiveness cf. Jockers and Witten 2010).

The general feature of the methods in question is a two-step supervised analysis. In the first step, the traceable differences between samples constitute a set of rules, or a classifier, for discriminating authorial “uniqueness” in style. The second step is of predictive nature – using the trained classifier, one can assign other text samples to the authorial classes established by the classifier; any disputed or anonymous sample will be assigned to one of the classes as well.

The procedure described above relies on a pre-processed corpus of samples. Namely, the clue is to divide all the available text samples into two groups: primary (training) set and secondary (test) set. The first set, being a collection of texts written by known authors (“candidates”), serves as a sub-corpus for finding the best classifier, while the second set is a pool of texts of known authors, anonymous samples, disputed ones and so on. The better the classifier, the more samples from the test set are attributed correctly and the more reliable the attribution of the disputed samples.

Such procedures have been successful in social and medical studies; no wonder, then, that it soon made its way into authorship attribution. Yet, contrary to the former applications where the researcher usually enjoys a high number of test samples (e.g. patients), authorship attribution has to struggle with a limited number of samples available to train a convincing classifier. This makes the classifier sensitive to statistical error. What is more, the generally-accepted division of data studied into a training set and a test set further limits the texts that can be attributed.

This sensitivity of machine-learning classifiers to the choice of samples in the training set has already been observed (Jockers and Witten 2010: 220). Intuition suggests composing the training set from the most typical texts (whatever “typical” means) by the authors studied (thus, for Goethe, Werther rather than Farbenlehre). In practice, this can be quite complicated: in a small corpus, to change a single training set sample for another can upset the delicate mesh of interrelationships between all other texts. This potentially heavy impact on the effectiveness of attribution tests has not been lost on Hoover: “As a reminder of how much depends upon the initial choice of primary and secondary texts, consider what happens if the same 59 texts are analyzed again, but with different choices for primary and secondary texts [...]. If the analyses that are the most successful with the initial set are repeated, Delta successfully attributes only 16 of the 25 texts by members of the primary set” (Hoover 2004a: 461).

Last but not least, any manual selection of texts to both sets must be highly arbitrary. To further quote Hoover: “The primary novels for this test are intentionally chosen so as to produce poor results, but one might often be faced with an analysis in which there is no known basis upon which to choose the primary and secondary texts, and nothing prevents an unfortunate set of texts like this from occurring by chance” (Hoover 2004a: 461-62).

Machine-learning methods routinely try to estimate the potential error due to incorrect choice of the training set samples. This cross-validation consist in a few random changes to the composition of both sets, followed by a comparison of the classifier’s success, ten-folded cross-validation being the standard solution (Tibshirani et al. 2003: 107; Baayen 2008: 162; Jockers and Witten 2009: 219). The question arises whether ten trials are sufficient for a classifier which, based on but a few samples, can be unstable.

Assuming that the training set contains 10 samples by 10 authors, and the test set another 10 samples by these authors, there are 210 = 1024 possible combinations of members of the training set. For a corpus of 60 novels by 20 authors, this number becomes so large that testing all possible permutations of both sets is unrealistic. Instead, the impact of the composition of the training set on attribution success can be assessed basing on several hundred random permutations; this can be done with a variety of bootstrap procedures (Good 2006).

To test this problem, we have selected several corpora of similar size and similar number of authors studied (with the obvious caveat that any comparison between different languages can never be fully objective). For each of these corpora, we have performed 500 controlled attribution experiments, each with a random selection of the training and the test sets. We have compared the number of correct authors guessed, with the hypothesis that the more resistant a corpus is to changes in the choice of the two sets, the more stable the results.

All tests featured the simplest, the most intuitive and the most frequently used of machine-learning attribution methods: Burrows’s Delta (Burrows 2002; Hoover 2004b). Delta was run for 100 MFWs, then for 200 and then, at increments of 100, all the way to 2000 MFWs. This was performed at five different culling settings (0-100% incrementing by 20), giving a total of 1000 results, and a mean of these was recorded. The above procedure was then repeated for 500 random permutations of the texts in the training set. The density function was estimated for the final results thus obtained.

It can be assumed that the distribution of these 500 final results should be Gaussian rather than anything else. The peak of the curve would indicate the real effectiveness of the method, while its tails – the impact of random factors. A thin and tall peak would thus imply stable results, i.e. those resistant to changes in the primary set.

The analysis of the results begins with the corpus of 63 English novels by 17 authors. As expected, the density of the 500 bootstrap results follows a (skewed) bell curve (Figure 1). At the same time, its gentler left slope suggests that, depending on the choice of the training set, the percentage of correct attributions can vary, with bad luck, to below 90%.

Figure 1. Density (vertical axis) of attributive success percentage rates (horizontal axis) in the English novel corpus

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It is quite natural that the stability of the results might also depend on the number of authors and/or texts analyzed. The same Figure shows that, with fewer authors, a higher number of texts has no significant impact on the stability of the results at any permutation of both sets (the dashed line), as already observed by Hoover and Hess (2009: 474). With more authors (i.e. when guessing becomes more difficult), the curve widens and a perfect match is even less frequent.

And this is still good accuracy and a fairly predictable model. However, it has to be remembered that Delta has been shown to be somewhat less perfect in other languages (Rybicki and Eder 2011).

Figure 2. Density of attributive success rates in the French novel corpus

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Figure 3. Density of attributive success rates in the German novel corpus

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Figure 4. Density of attributive success rates in the Italian novel corpus

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Figure 5. Density of attributive success rates in the Polish novel corpus

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Indeed, the discrepancies in Figures 2-5 seem to question the validity of attribution tests based on arbitrary choice of training sets. Although peaks for some combinations of numbers of texts and authors may be at acceptable levels, the left slopes of the curves tend towards dangerously low values; and the wide tails of the curves show that a high success rate outliers might be a stroke of luck rather than a consequence of the method, the data and the statistical assumptions – the most ominous memento appearing here from the inexplicable dispersion in the corpus of 39 Polish novels by 8 authors (Figure 5, grey solid line). Therefore, the ideal authorship attribution situation is not only that of many texts by many authors; it is equally important to assess the validity of the training set with a very high number of trials. This seems to be the only way to escape the quandary of arbitrarily naming each author’s “typical” text.


Burrows, J. F. 2002 “‘Delta’: A Measure of Stylistic Difference and a Guide to Likely Authorship, ” Literary and Linguistic Computing, 17(3) 267-87

Good, P. 2006 Resampling Methods, Birkhäuser Boston, Basel, Berlin

Hoover, D. L. 2004a “Testing Burrows’s Delta, ” Literary and Linguistic Computing, 19(4) 453-75

Hoover, D. L. 2004b “Delta Prime?, ” Literary and Linguistic Computing, 19(4) 477-95

Hoover, D. L. Hess, S. 2009 “An Exercise in Non-ideal Authorship Attribution: The Mysterious Maria Ward, ” Literary and Linguistic Computing, 24(4) 467-89

Jockers, M. L. Witten, D. M. Criddle, C. S. 2008 “Reassessing Authorship in the ‘Book of Mormon’ Using Delta and Nearest Shrunken Centroid Classification, ” Literary and Linguistic Computing, 23(4) 465-91

Jockers, M. L. Witten, D. M. 2010 “A Comparative Study of Machine Learning Methods for Authorship Attribution, ” Literary and Linguistic Computing, 25(2) 215-23

Rybicki, J. Eder, M. 2011 “Deeper Delta Across Genres and Languages: Do We Really Need the Most Frequent Words?, ” Literary and Linguistic Computing, 26 (forthcoming)

Tibshirani, R. Hastie, T. Narashimhan, B. Chu, G. 2003 “Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays, ” Statistical Science, 18 104-17