Let quantitativo represent an unknown document and let y represent per random target author’s stylistic ‘profile’. During one hundred iterations, it will randomly select (a) fifty a cent of the available stylistic features available (e.g. word frequencies) and (b) thirty distractor authors, or ‘impostors’ from a pool of similar texts silverdaddy. Per each iteration, the GI will compute whether quantitativo is closer esatto y than sicuro any of the profiles by the thirty impostors, given the random selection of stylistic features sopra that iteration. Instead of basing the verification of the direct (first-order) distance between quantitativo and y, the GI proposes onesto primato the proportion of iterations durante which interrogativo was indeed closer to y than esatto one of the distractors sampled. This proportion can be considered verso second-order metric and will automatically be a probability between nulla and one, indicating the robustness of the identification of the authors of quantitativo and y. Our previous work has already demonstrated that the GI system produces excellent verification results for classical Latin prose.31 31 Complice the setup con Stover, et al, ‘Computational authorship verification method’ (n. 27, above). Our verification code is publicly available from the following repository: This code is described per: M. Kestemont et al. ‘Authenticating the writings’ (n. 29, above).

For modern documents, Koppel and Winter were even able esatto report encouraging scores for document sizes as small as 500 words

We have applied per generic implementation of the GI puro the HA as follows: we split the individual lives into consecutive samples of 1000 words (i.ed. space-free strings of alphabetic characters), after removing all punctuation.32 32 Previous research (see the publications mentioned per the previous two taccuino) suggests that 1,000 words is a reasonable document size in this context. Each of these samples was analysed individually by pairing it with the profile of one of the HA’s six alleged authors, including the profile consisting of the rest of the samples from its own text. We represented the sample (the ‘anonymous’ document) by a vector comprising the correlative frequencies of the 10,000 most frequent tokens in the entire HA. For each author’s profile, we did the same, although the profile’s vector comprises the average relative frequency of the 10,000 words. Thus, the profiles would be the so-called ‘mean centroid’ of all individual document vectors for verso particular author (excluding, of course, the current anonymous document).33 33 Koppel and Seidman, ‘Automatically identifying’ (n. 30, above). Note that the use of per scapolo centroid a author aims puro veterano, at least partially, the skewed nature of our momento, since some authors are much more strongly represented durante the corpo or retroterra pool than others. If we were not using centroids but mere text segments, they would have been automaticallysampled more frequently than others during the imposter bootstrapping.

Onesto the left, per clustering has been added on sommita of the rows, reflecting which groups of samples behave similarly

Next, we ran the verification approach. During one hundred iterations, we would randomly select 5,000 of the available word frequencies. We would also randomly sample thirty impostors from per large ‘impostor pool’ of documents by Latin authors, including historical writers such as Suetonius and Livy.34 34 See Appendix 2 for the authors sampled. The pool of impostor texts can be inspected sopra the code repository for this paper. Con each iteration, we would check whether the anonymous document was closer sicuro the current author’s profile than to any of the impostors sampled. Mediante this study, we use the ‘minmax’ metric, which was recently introduced con the context of the GI framework.35 35 See Koppel and Winter, ‘Determining if two documents’ (n. 26, above). For each combination of an anonymous text and one of the six target authors’ profiles, we would supremazia the proportion of iterations (i.ancora. a probability between niente and one) per which the anonymous document would indeed be attributed preciso the target author. The resulting probability table is given mediante full mediante the appendix onesto this paper. Although we present per more detailed dialogue of this giorno below, we have added Figure 1 below as an intuitive visualization of the overall results of this approach. This is verso heatmap visualisation of the result of the GI algorithm for 1,000 word samples from the lives durante the HA. Cell values (darker colours mean higher values) represent the probability of each sample being attributed esatto one of the alleged HA authors, rather than an imposter from a random selection of distractors.