Reconstructing Intellectual Networks: From the ESTC’s bibliographic metadata to historical material Mark J. Hill1[0000-0001-7273-1775], Ville Vaara1[0000-0001-7924-4355], Tanja Säily 1[[0000-0003-4407- 8929] , Leo Lahti2[0000-0001-5537-637X], and Mikko Tolonen 1[[0000-0003-2892-8911] 1 University of Helsinki, Department of Digital Humanities, Helsinki, Finland 2 University of Turku, Department of M athematics and Statistics, Turku, Finland mark.hill@helsinki.fi Abstract. This paper demonstrates the use of the ESTC as a representation of material history through the extraction and parsing of its data in a way which al- lows it to be used in social network analysis. In doing this it makes two contri- butions. The first is methodological, outlining how such a transformation of da- ta is possible. The second is historical, by demonstrating how this data can be used to support historical claims. Keywords: Digital History; Social Network Analysis; M etadata; Book History; Bibliographic Data; Intellectual History . 1 Introduction The English Short Title Catalogue (ESTC) , according to the British Library, is the comprehensive, international union catalogue listing early books, serials, newspapers and selected ephemera printed before 1801. Covering over 480,000 documents, wh ich are held by mo re than 2,000 libraries, it is an essential record of early English print culture, used by researchers to discover and locate early modern texts. However, like other library catalogues, it is “a greatly underestimated source of knowledge” (To- lonen et al. 2018: 1). Historians of the booktrade have noted the relevance of shifting relat ionships b e- tween authors, printers, publishers, and other actors when studying the history of print. The early modern develop ment of the English market for printed bo oks has been described as a process fro m an individual cantered medieval craft towards a tightly organised wholesale business orchestrated through a London monopoly (Po l- lard 1978). Publishing networks naturally, therefo re, p lay a central ro le when it co mes to understanding the nature of these changes (Belanger 1975; Collins 1927). By e x- tension, these networks can also be seen to represent intellectual, relig ious, and social groups connected by their shared endeavours and interests (Raven 2007). The aim of this paper, therefore - and following recent research wh ich has highlighted the quant i- tative utility of library catalogues for historical research - is to demonstrate that the ESTC is a representation of these comp lex and changing historical relat ionships which can be analysed using quantitative methods (Lahti et al 2015; Tolonen et al. 202 2018). An important caveat to this claim, however, is that the changes in the publis h- ing landscape are impossible to understand solely through informat ion discovered on the imprint of a document. Thus, the claims made in this paper are exploratory rather than conclusive in this regard - they are proofs of concept and demonstrations as to how this data can be used in novel ways, but they should be understood as aiming to augment traditional h istorical research rather than being the end source of historical knowledge of booktrade itself. In this way, the ESTC is used not as a cat alogue, nor as a research tool per se, but instead as historical material itself which adds to the existing evidence and expertise utilized in the field. In doing this, this paper makes two research contributions: methodological and historical. 2 Methodological Contribution The first contribution comes fro m the transformat ion of historical record into histo ri- cal relic; specifically, the process of taking a discrete set of catalogue records and turning them into a harmonized relational dataset rich in historical data. 2.1 The Data As noted, the ESTC is the comprehensive, international union catalogue for Englis h titles. Its construction aimed to include all books (1475-1801), pamphlets, broadsides, and other miscellaneous documents. Each document’s record can contain as many as 420 d iscrete data points, covering details fro m the document’s location in the colle c- tions it is held, to its former owner. It is, therefore, an incred ibly rich source of histo r- ical material, all of which is recorded using the Machine-Readable Cataloging (MARC) 21 standard.1 While this means that the data is no minally machine readable, th e process of creat- ing a linked dataset should not be trivialized. Originally designed to make the repro- duction of catalogues simple r, the content of MARC records was not designed with extraction of linked data, and quantitative analysis of this data, in mind. There are a number of issues which make using it for purposes other than locating texts proble m- atic. Specifically, a more structured data model is required for catalogue data to be useful for systematic quantitative research. To this end, the Helsinki Co mputational History Research Group (COMHIS) has an ongoing project wh ich aims to ext ract all data points fro m the ESTC. 2 For the pur- poses of this paper, however, we focus on the ext raction of MA RC fields 100 and 700 (personal names); 110 and 710 (corporate names); and 260 (imp rint), and how we have created a relational data model in which each actor is an unique and identifiab le object connected to all t itles they were reported to have been involved with, wh ile maintaining the document-specific details of their particular role.3 1 The Library of Congress has an overview of M ARC 21 (https://www.loc.gov/marc/). 2 This paper is a part of this larger research project being conducted by COM HIS (https://www.helsinki.fi/en/researchgroups/computational-history). 3 The code used to parse the ESTC was written in R and Python. 203 2.2 Extraction, Harmonizati on, and Preparation Actors Fields MARC tags 100, 110, 700, and 710 contain informat ion with regard to actors in- volved with a specific docu ment. While there may be as many as 20 discrete pieces of data, the key points of interest include names; letters, initials, abbreviations, phrases, or numbers used in place of a name; tit les or other words associated with a name; dates associated with a name (birth/death years, active years); the relationship b e- tween a name and a work; attribution informat ion when the responsibility is unknown, uncertain, fictitious or pseudonymous; and other miscellaneous information. With regard to d iscrete records, we have been able to extract 557,847 references to actors from 397,061 documents (those for which there were actors named). Once extracted, these names were processed to correct numerous unicode issues, data entry errors, and standardize accents. The ro le of each actor was either extracted (when available), deduced fro m the MA RC tag, or marked as unknown. If a single entry had mu ltip le roles (e.g., “Publisher and printer”), new entries for the same actor were created to reflect these multiple roles on the document. Importantly, there is no recorded relationship between references to actors record- ed in the ESTC. Thus, there is no way of knowing whether one entry for William Shakespeare is those found elsewhere. Or, mo re proble mat ically, whether any or all of the 137 records for “A. B.” are the same person. Thus, before analysis was possible, extensive processing, harmonization, and validation were necessary. In many cases records within the ESTC themselves were robust enough for this (for examp le, when an actor’s name included specific years of activ ity). In many cas- es, however, such informat ion was not available. In these situations we turned to the Virtual International Authority File (VIA F: https://viaf.org/), a harmonized record of mu ltip le international library catalogues which has collected and unified individual records and identifiers for actors. The aim was to take these actor identifiers and apply them to records in the ESTC. To do this, mu ltiple matching steps were made, for e x- amp le: exact string matches; string matches minus particular types of punctuation; bag-of-word matches for records which contained data considered unique such as years of birth and/or death. Once these matches were exhausted, we made use of the VIAF API to conduct fuzzy searches matching both actor name and the title of the document they were linked to. Th is resulted in unification of 523,955 records into 68,230 actors, with 34,288 references to actors unmatched. There were problems, however. First, VIAF has duplicate records for the same a c- tor, and these duplicates made their way into our own data. Additionally, there are harmonized records in VIAF which represent mu ltip le individuals with a shared name. Due to these issues, a number of verification steps were imp lemented to vali- date and further harmonize actors. Specifically, there is an extensive semi-automated verification process which looks for false positives (names un ified under the same ID, but which are not recorded as identical in the ESTC) and false negatives (names which were identical in the ESTC, but do not share an ID following the ha rmonization process). In these cases, historical knowledge was used to make a judgement. For the actors which we were unable to un ify, a final match was made between those with an 204 exact name string which were attached to records for multip le editions of the s ame text. Actors which d id not meet any of the previous criteria were treated as distinct, regardless of similarity. At this point 558,243 references to actors have been harmo- nized into 92,044 unique actors. However, the process has been written to make addi- tional unification a simple task, and thus improvement is an iterative process. Imprint Field MARC tag 260 covers the imprint field, notes relating to the publication, printing, distribution, issue, release, or production of a work. While the field itself contains fewer data points (nine) than the actor fields, it is recorded in a much mo re problema t- ic manner. Specifically, the names of entities rep resenting booktrade actors are co n- tained within a text chunks replicat ing the fu ll publisher statement. Thes e can be very amb iguous for standard computational extract ion methods. 4 To parse these state- ments, first relevant data was identified, differentiated, and separated from the rest of the imprint using natural language processing. This data is then identified as entities representing persons, roles, names of organisations, and locations.5 Once extracted, a similar unification process to that outlined above is followed. First, post processing of names is necessary. This includes correcting spelling errors and normalizing spelling variations when sensical (i.e., Iohn becomes John; VVoo d- cocke becomes Woodcock), and location info rmation and years of act ivity are used to match in itials with full names (e.g., J. Newbery and John Newbery). Entities are then unified as far as possible by cross -referencing names with existing records e xtracted fro m the ESTC, the VIAF, and the Brit ish Book Trade Index (BBTI: http://bbti.bodleian.o x.ac.uk/ ). As with the VIAF, there are issues with the BBTI – in particular duplicate entries which require further processing. Currently we are able to harmonize 35,252 unique actors from 332,410 discrete entries. Output To conclude, we have extracted extensive metadata from the catalogue, including titles, publication years and places, edition and imprint informat ion, and all named actors involved or related to a publicat ion. Although this process is largely automated, it has required extensive verification and post-ext raction cleaning. Particu lar attention was given to careful ext raction and unification of named entities. Additional enrich- ment steps are also applied. For examp le, genders were either ext racted fro m external sources (VIAF) or assigned using a historical gender-name d ictionary wh ich we con- structed using historical parish records.6 Overall, however, the processing and clean- ing of this data is an iterative process, and we continue to refine the data. 4 Two offer two examples: "printed for Bernard Lintott at the Cross-Keys, between the two Temple-Gates, in Fleet-Street. The Double Gallant: Or, the Sick Lady's Cure. A Comedy. Written by M r. Cibber"; “printed by E: Coates. 1655. Sould by Thomas Heath in Covent garden, and Henry Herringman at the Ancker on the lowest side of the New-Exchange.” 5 The current method makes use of the Stanford Natural Language Parser. 6 See: https://github.com/COM HIS/names_and_genders 205 2.3 Historical Network Data and its Validity As the aim of this paper is to use the extracted data for social network analysis, we needed to further transform the bibliographic records. To this end, we created node and edge tables in which documents are edges and unified actors are nodes. The cu r- rent outputs result in a network of 72,066 nodes connected by 328,996 edges.7 Ho w- ever, due to the temporal co mplexity of the booktrade, further refinement was neces- sary. In particular, in order to discard actors (in particular authors) who continued to be published after their deaths, the dataset was refined to only create links during an actor’s lifet ime, or active years. Additionally, because we were interested in changes over the entire t imespan of the ESTC, as well as during an individual’s life, further subsets, covering ten year periods which overlap by five years, were created. It is important to reflect on the mean ing of these connections. The premise is that a shared effort on a part icular document represents a real world h istorical conne ction. While it is difficult to know whether a printer, for example, could really be said to have had a close relat ionship with an author, the records nonetheless represent rela- tionships between actors taken fro m historical objects (Raven 2001: 2). That is, they are not constructed out of curated (by us) informat ion. 8 Additionally, these are a par- ticular type of historical record - a record of the booktrade - wh ich is particularly well suited for network research for at least two reasons: first, the importance of geogra- phy; and second, technology as a limiting factor in book production. The history of the English booktrade is t ied to London.9 By a large marg in the ma- jority of early modern publications in Eng lish came fro m London (317,679 to runner- up Edinburgh’s 31, 887). Th is is impo rtant because, although it was a large city (Lo n- don’s population grew to roughly one million people by 1800), the booktrade func- tioned on a human scale in which personal networks mattered, and geographical loca- tion reflected personal and intellectual connection. As Raven (2007: 155) reports, booksellers located themselves in pro ximity to their clientele, and thus clustered amongst competition. Additionally, these locations could be further linked through networks of publishers and printers specializing in g iven genres or types of public a- tions. A printer, for examp le, could be attached to a specific bookseller or publisher, even sharing a premise. Th is is important because it means that the networks which emerge in the ESTC can reflect very real spatial and personal relationships.10 7 Network construction and analysis were conducted using the iGraph package for R. 8 The ESTC is an incredible record of the early modern booktrade. However, it is not entirely comprehensive (Raven 2007: 406-407). For further reflections on comprehensiveness and digital history see: Hill (2017). 9 For more on London and the history of the booktrade see: Chartier (1987); Feather (1982); Greg (1956); Belanger (1975); Blagden (1951); M cKenzie (1969); Myers and Harris (1982; 1987); Treadwell (1982). 10 Even as publishing established itself within North America, imports remained dominant, and for those who wished to purchase English texts, personal relationships with individuals in London were key (Green 2009). Even in exceptional cases, such as Boston in the 17th and early 18th centuries, and Philadelphia and New York later in the 18th century, London ac- tors remained entangled, be it via direct connections, such as with Benjamin Franklin and William Strahan, or as relocated Londoners, such as David Hall and James Rivington. 206 The second aspect of the booktrade which suits it for network analysis is one of technological limitation - specifically that of the hand-operated printing press. For the entirety of the period covered by the ESTC, the key factor li miting increases in print production was labour. As the numbers of publications increased, the industry (and the humans who made it ) had to grow in tandem. The upshot of this reality (as well as the centrality of London within the industry) is that the booktrade was made up of, and dependent on, human networks (Hirschfeld 2001; Dobranski 2014). Th is makes the ESTC a potentially rich source from which to construct historical networks. 3 Historical Contribution The results presented in this paper are meant as proofs of co ncept that bibliographic metadata can be transformed into historical network data (not to form a cohesive his- torical argu ment with regard to the booktrade), and to this end, the next section of this paper is divided into four parts wh ich apply network analysis to the ESTC: an over- view of the network data extracted in relation to authors; individual case studies; a u- thorial centrality over the early modern period; and historical community detection. 3.1 Authorial Networks Overview As a starting point, an overview of data, as recorded and thus reported by the ESTC is offered (Fig. 1). Fig. 1. ESTC network changes: 1500-1799. While the data for much of the beginning o f the period covered is sparse, this in itself allo ws for co mparison. 11 As noted above, the growth of the booktrade was tied to 11 The jump in the amount of records covering the era of the civil war may be related to an increase in political writings from the time, but the records of these writings can be tied to 207 increased labour - and this relationship is visible immediately: as the number of do c- uments printed went up, the size the network g rows in tandem. However, the trans- formation is more co mplex than this, as visible in the increase of cliques and drop in edge density. These are both signs that the network was becoming more d iverse, ra- ther than a singular densely interconnected network, indicat ing an emerging structure in which part icular nodes played central roles. 12 That is, as new individuals were brought into the booktrade to increase production, aspects of the trade become in- creasingly specialized and vert ically integrated with specific actors emerging as key in managing and developing the industry (Plant 1974: 59). It should be noted, however, that there is much historical nuance and debate with regard to the details of the production end of the booktrade. Trade publishing, distri- bution networks, proofreading, imp recision in both the titles assigned to professions within the trade, and what was actually recorded on imprints, all make the history of publishing an incredibly difficult field to document, and this complexity is clearly visible in the figures above.13 It is therefore important to, again, note that the data extracted fro m these networks do not represent claims as to a new understanding of the historical reality of the booktrade. Instead, this data should augment existing his- torical knowledge in a way which supports it and provides further insights. With this in mind : when we investigate the relationships between roles specifically, we can see that this complexity is not universal. Publishers, needing to develop more robust networks to advance their co mmercial ends, grew larger networks over t ime, while printers became, if not less entangled in these networks, less visible in the i m- prints reporting them. 14 Authors, however, maintained a similar nu mber of connec- tions (Fig. 2); it was who these connections were with that shifted. the existence of the Thomason Collection of Civil War Tracts, and thus claims with regard to this increase should be tempered. On the other hand, the Great Fire is clearly responsible for the loss of historical works, the records of works, and the potential for production. 12 These changes also reflect the reality of imprint records not always acknowledging all in- volved in production. Additionally, the increase in the size of network cliques, particularly in the 18th century, is representative of the historical practise of shared copyrights – a situa- tion which is clearly visible in Fig. 4. 13 To note how these historical difficulties may impact our data: as the records being used are largely extracted from imprints, and as printers became less important in terms of the distri- bution of works to the audiences purchasing them, their details may have been less likely to be included. Additionally, as printing houses became larger, or the work more frequently distributed between houses, the pragmatics of including all printers may have been ques- tioned. There is also ambiguity in imprint statements which has previously been noted as problematic. As Raven writes: “Recovering these trading relationships is not easy. M any distribution agents are unspecified.” Having said this, the ESTC remains the best record of these relationships we have, and as Raven importantly concludes: “Despite these reserva- tions, we can at least be certain that in most cases inclusion of a name in an imprint line in- dicates some sort of financial involvement. This is particularly the case where the author is mentioned” (Raven 2001: 2). 14 It should be noted that the distinction between publisher and printer during this time was not formalized as it is today, and this is an additional historical issue which needs to be taken in- to consideration. 208 Fig. 2. Left: mean ego network size of authors, publishers, printers, and other actors. Right: mean connections from authors to printers, publishers, and other authors. Feather (1994: 79) has written that: By the middle of the eighteenth century, it was possible to make a decent liv - ing fro m writing … Pat ronage was no longer essential even to literary authors, and the idea of the author as a participant in co mmercial activ ity was begin- ning to be accepted. At the same time as authors were becoming more overtly mercenary, there was also developing a more elevated idea of the author as art- ist or creator. The very concept of ‘originality’, in the sense of an ‘original work’ of art or literature, is an eighteenth-century idea. What we may be witnessing in the figures above, then, is the shift fro m the “self- publishing” author (if such a conceptual category could be said to have existed) to the established “professional author” wh ich, by the eighteenth century, turns to existing publisher-headed networks, rather than printers, to manufacture and distribute their works (Raven 2004: 4). For such a claim to be considered, however, we must acknowledge another co mplexity in the data: the recorded drop in authorial connec- tions to printers is not uniform. Fro m the middle of the 18th century the relationship begins to reverse as pu blisher connections drop and printers once again increase in author ego networks. There are, at least, three historical reasons for this. First, the g rowth of the colonial booktrade in the 18th century, where publishing had yet to take the form it had in London, meant printers were more frequently identified on imprints than publishers (Fig. 3). The impact of this distinction towards the end of the 18th century was ampl ified as, by 1792, non-London-based locations outnumber London for the first time. 209 Fig. 3. Left: Total documents in ESTC with publishers or printers attributed to them by year. Right: Ratio of publishers to printers in London and elsewhere. The second cause for the recorded increase in printers was the 1710 Copyright Act. While the legality of the statute is complicated, in theory it ended perpetual co pyright after 28 years. Th is resulted in reprints of wo rks which entered the public do main - first in the 1740s by printers outside London, and then in the 1770s by those in Lo n- don (both events visible in Fig. 4) (Feather 1994: 81). The third reason was piracy; while a work legit imately tied to an established publisher would make this clear on the imp rint, pirated works were, unsurprisingly, more vague in this regard (Feather 1994: 68). Overall, it is clear that there is extensive historical data availab le in these network overviews which o ffer longue durée insights - which is an important contribution in itself fro m the historians perspective (Armitage 2012). These insights must, however, be tempered by detailed analysis of the finer points - and one way o f doing just that is by turning to individual authors. 3.2 Indivi dual Case Studies The realit ies of the booktrade for individual authors are better seen when examined independently. To demonstrate this we provide two case studies: William Shake- speare and Bernard Mandeville. To begin, we o ffer a co mparison of the ego network of Bernard Mandeville as extracted automatically fro m the ESTC, and as constructed by a scholar of Mandeville (Fig. 4). The two networks are similar in construction: relationships and names reoccur, and a cluster of publishers who shared the copyright of Mandeville’s Fable of the Bees is visible in both. When discounting unknown actors and booksellers fro m the ESTC, there is only one actor in the hand-crafted network wh ich was not found in the ESTC network: the publisher John Peele. 210 Fig. 4. Bernard M andeville’s (1670-1733) professional network. Left: constructed by a histori- an of M andeville.15 Right: constructed with parsed ESTC metadata. There are, however, three addit ional nodes found in the ESTC ne twork: two authors and one publisher. Additionally, the ESTC network contains nine additional public a- tion records (five of wh ich record Mandeville as “Unknown,” and three as an attribut- ed author) and 49 further unique connections between actors, a detail wh ich would be exponentially mo re difficult record when constructing a network by hand. It is, there- fore, both historically accurate and robust, and when turning to the makeup of the relationships further historical detail can be noted. With regard to author popularity, death would perhaps be unsurprising as a limit ing factor. While this may not be entirely intuitive in terms of the nu mber of publications attached to an author, it is should certainly be the case with regard to social networks. Fig. 5. Breakdown of types and number of connections over time. Dotted red line represents year of death. 15 Data for hand-crafted ego network provided by M ikko Tolonen. 211 Our data shows, however, that death need not entirely hinder one's social network; posthumous publications - and those who worked on them - need to be taken into consideration when using this data (Fig. 5). This is an important point. Methodologically, it highlights the issue of simply e x- tracting all reported relationships. If one is interested in examining potentially real historical relationships, data must take into consideration life and/or active years. Historically, however, there are reasons one may be interested in exact ly these types of posthumous records. While, as an author, Shakespeare remained in print after his death, there are noticeable peaks and troughs in his connection counts which are in- dicative of known editions issued by different publishers. Fig. 6. Ego networks weighted by number of connections and the shift in key publishers over time When looking at ego maps from d ifferent periods, we can clearly identify these pub- lishers (Fig. 6). What is more, the networks themselves offer addit ional contextual detail - specifically, co mpetition and shifting changes in importance. For examp le, in 1730-1739 we see distinct connections between feuding publishers Robert Walker and the Tonsons, the latter o f which would co me to do minate the network in the mid- dle of the century. By 1770-1779, however, the market had changed. John Bell, whose more cheaply printed works, “satisfied public demand for inexpensive edi- tions,” emerged as a key actor, and remained vis ible until the end of the century - although further competition in the discount-edition market can be seen in Edward Harding’s arrival (Rasmussen 2000: 343). 212 While these examp les are, purposefully, built upon previous research, they offer demonstrations as to the novel ways in which the ESTC may be used to track authori- al relationships at different historical moments. 3.3 Author Centrality As Wasserman and Faust (1994: 169) have noted, “[o]ne of the primary uses of graph theory in social network analysis is the identification of the ‘most i mportant’ actors” through centrality measurements – a quantitative method which, by taking into ac- count the relative relat ionships between actors, can identify key nodes. What makes a node key depends on the measurement chosen, with each emphasizing particular types of relationships. Thus, careful assessment of one’s data and the usefulness of each method is necessary when calculating centrality. With this in mind, this section of the paper applies four centrality measurements - degree, betweenness, closeness, and eigenvector - to the ESTC with the aim of both assessing their particular uses, as well as validating the historical richness of the data. We extracted the ten most central authors per ten year time slices (to ensure con- temporarily meaningful results) and compared them: first, against the most published authors for the same t ime period; second, for instances of direct overlap (i.e., were the same people identified by different measurements); and third, mo re qualitatively, in terms of how representative those authors were with regard to the historical period they were extracted from. Below is an overview of these results. Fro m 1500-1535, an era which one may identify with renaissance humanism, d e- gree, betweenness, and closeness centrality measurements most frequently return to humanist authors (e.g., Erasmus, Tho mas More, Sebastian Brant, Baptista Mantuanus) while eigenvector and the most-published pointed to religious authors and grammari- ans. During the reformat ion (1535 to the mid-16th century) all measurements identi- fied reformation and counter-reformation figures (e.g., Martin Luther, William Tyn- dale, Tho mas Beccon, John Kno x, Jean Calvin, Theodore Beza). Ho wever, those found in the most-published category are almost exclusively English authors, some of who do not show up in any of the centrality measurements (John Hooper and Ed mund Bonner). The do minance of the reformation continued for the first half o f the Elizabe- than era, although by the 1570s familiar dramat ists, poets, and other literary figures emerge (although Puritan authors remain present throughout). Authors include: George Gascoigne, Tho mas Newton, Thomas Nash, Robert Greene, Shakespeare, Thomas Heywood, and Michael Drayton. This trend generally continues during the Jacobean era (1605-1640) with the exception of eigenvector centrality which, interes t- ingly, takes an explicitly Anglican and royalist turn, returning (as authors) James I and Charles I, Lancelot Andrewes, John White, William Laud, and John Williams. Unsurprisingly, during the civil war (1640-1655), literary authors disappear and are replaced by overtly political and religious authors (especially nonconformists). On the political end of the spectrum we find, amongst others, Charles I, Oliver Cro mwell, Thomas Fairfax, Ed ward Montagu, John Pym, Robert Devereu x, and Christopher Love. Religious figures include George Fo x, James Naylor, W illiam Prynne, Richard Baxter, and the first colonial-based author identified, John Cotton. 213 Fig. 7. Great Fire of London’s impact on publications. The redline marks the year of the fire (1666). Fro m 1660-1675 the data is, unfortunately, tarnished by the Great Fire o f London which, while taking place in 1666, resulted in the loss of texts going back to at least 1660, and impacted production for years to follow (Fig. 7). There is little cohesion in terms of authors identified as central during th is period, although non-conformists dominate the publication records.16 The restoration period and beyond - the “Augustan Age,” lasting roughly 1660 un- til 1785 - can be conveniently divided into three authorship -defined sub-eras: the Age of Dryden, the Age of Pope, and the Age of Johnson. The centrality of the namesakes for these literary eras is quickly confirmed. During the first, Dryden is more frequent- ly identified than any other author until 1695 (although other important literary a u- thors returned include Roger de Coverley, Elkanah Settle, Thomas D’Urfey, Abel Boywer, and Gilbert Burnet). This is followed by the Age of Pope (1700-1745) which, again, includes Pope himself, Daniel Defoe, Jonathan Swift, Richard Steele, Eliza Haywood, Co lley Cibber, A mbrose Ph ilips, and many others. The Age of John- son, from 1750 until his death in 1784, is again dominated by the literary g iants of the era, such as Johnson himself (although he first appears in 1765), Oliver Go ldsmith, Tobias Smollett, John Lockman, George Colman, Hannah Cowley, etc.. This era of literary centrality ends abruptly, however, as political turmo il again emerged – this time round the American and French revolutions – and key figures shift to the likes of Thomas Paine, George Washington, Benjamin Franklin, Joel Barlow, William Cob- bett, and Edmund Burke. For the most part, these results are remarkab ly in line with historical expectations. To better compare the measurements, however, they were correlated into a series of tables from which the follo wing visualizat ion was constructed (Fig. 8). Each bar rep- resents a specific measurement, and segments ten historical eras. The opacity of each segment represents how well each measurement reported authors deemed to be rele- vant to a given historical era. The aim is to offer a simp lified representation from which the usefulness of the measurements can be assed. 16 There are two potential reasons for this: one is the prominence of the Dutch printing non- conformist texts in Europe during this time; another being nonconformist social groups in London being located in the East End, away from the fire. 214 Fig. 8. Centrality measurements and the historical representativeness of the authors returned. The first finding to draw one's attention to is that the centrality measurements seem to successfully identify key figures, and often better than simple publication records (although Eigenvector is an outlier). Additionally: there is a large amount of diversity between who measurements report as central - especially towards the end of the peri- od covered. Eigenvector was, by far, the most unique, with nearly 50% of all results not found in other measurements. This was followed by closeness (36%), between- ness (27%), and degree (11%). Additionally, 27% o f the authors - largely religious - returned as most frequently published were not found in any of the centrality mea s- urements. Interestingly, authors often appear in the publication count category after they are first noted by a centrality measurement with one exception: the first half of the eighteenth century, a time coinciding with the development of the publisher. While beyond the remit of this paper, this points to a complex structural relationship between publishing as an industry, and the popularity of an author.17 The results generated from the eigenvector centrality measurements are also worth noting. Figure 8 reports that, as a measurement, eigenvector was quite poor at identi- fying authors representative of a given period. There were exceptions to this, however - in particu lar, in the lead up to, and during the, civil war. To understand the reasons for this one must understand that eigenvector identifies centrality by weighing each node by the centrality of all other nodes. That is, any given node is as central (or as important) as the nodes it is connected to. The upshot of this is immediately visib le in the data: nodes identified as central by eigenvector measurements often represent 17 There are, of course, even larger questions which one should ask: for example, do the results represent a central author because of their reception while alive, or do the connections which we are now identifying play a role in our perception? Additionally, the records for those who we have come to expect to be important are likely to be the bibliographic records which are more robust. 215 more established institutional figures. That is, in the lead up to the civil war we find royalist and Anglican authors, but at the start of the civil war they are rep laced by parliamentarian authors. This is perhaps further h ighlighted by Henry Scobell b eing identified in the 1650s. While certainly an important figure in h is own right, what perhaps contributes to him being identified as central in the entire network was his role as editor of parliamentary proceedings and licensor of newspaper and political pamphlets. This institutional centrality, however, means that the measurement is per- haps less appropriate in situations - or with data - in which a clear hierarchy is not visible. For example, at the end of the 18th century, while most measurements ident i- fied leading polit ical authors, eigenvector returned various literary figures - with one exception: the very much institutional figure Edmund Burke. 3.4 Community Detection Centrality is not the only way in wh ich institutions or establishments can be ident ified in networks. With algorithmic co mmunity detection the ESTC can be used to identify different and changing intellectual groups. To do this, we extracted (up to) the ten largest commun ities for each overlapping ten year slice using the fastgreedy algo- rith m. 18 This provided 577 co mmun ities varying fro m as fe w as two actors to over 1,200. To test whether these represented actual historical co mmunit ies we developed a semi-auto mated classificat ion method. First, we extracted all actor IDs attached to a given community, purging non-authors (e.g., publishers). 19 These IDs were then lin ked back to ESTC IDs, which allowed us to ext ract the titles and subtitles for the entirety of each commun ity. The tit les representing the 577 co mmun ities were still too numerous to investigate manually (the total collection of raw text tit les was over 350 megabytes). We therefore token ized tit les, counted the occurrences of each type per community, and converted them into proportions. We then measured each individual community against its peer-communities as a single entity. From this, we extracted the tokens for each community wh ich had the highest distance measurement, and a Z score for two population proportions which was statistically significant (p < 0.05). The results were less than a megabyte, and much easier for a human to parse. When examin ing the time slices covering the English civil war, the contextual mean ing of the tokens for two h istorical groups looked immediately pro mising. In one community we identified words such as: army, lieutenant, parliament, forces, co m- mons, protector, souldiers, victory, warre, and cro mwell. In the other, we found: king, majesties, proclamation, parliament, declarat ion, gracious, subjects, charles, royall, kingdom, and dominion. That is, the two co mmunities appeared to represent the pa r- liamentary and royalist sides of the civil war (plotted below in Fig. 9). 18 Fastgreedy was chosen due to the amount of data that needed to be processed. Better results are likely to be achieved using a different method (Yang et al 2016). 19 This was done, first, because we are primarily interested in authors for this study, second, to minimize duplicate titles, and third, limit the amount of data being processed. 216 Fig. 9. 1645-1655: English Civil War communities (royalists purple; parliamentarians yellow). Examining the actors who made up each co mmunity confirmed expectations. To offer some examp les: the red nodes are pro minent parliamentarian military leaders Tho mas Fairfax and Percy A lgernon, soldier Nicholas Culpeper, and republican pamph le teer William Stafford. Blue nodes represent prominent royalist military leaders Henry Worcester, John Humfrey, William Hamilton Hamilton, and, one-time- parliamentarian-turned-royalist, Edward Massey. Finally, the black node represents an interesting edge case connected to both communities: Marchamont Nedham, a pa m- phleteer who wrote for both sides during the civil war. To turn to another examp le, one which does rely on as clear h istorical divisions : In the 1645-1654 time slice one can find a co mmunity of religious authors, including Quaker founders George Fo x, Ed ward Bu rrough, and Francis Howgill, as well as various other nonconformists and Puritans. Interestingly, when one follows these actors through time slices, a Quaker-specific co mmun ity develops, made up of many more pro minent Quakers (including Margaret Fell, George Whitehead, James Nay ler, and nearly half of the ‘Valiant Sixty’). Fig. 10. Left: Key Quakers (blue) in general religious community (1645-1654). Right: Distinct Quaker community (blue) and previous religious community (red) (1650-1659). While exp loratory, these methods, again, offer a number o f pro mising avenues for researchers interested in identifying, exploring, and tracking historical authorship. 217 4 Conclusion The goal of this paper has been to offer an overview of the processes used to turn the ESTC into a historical relic, rather than produce a historical argument with regard to authorship as a whole. This remains impo rtant future research. However, we believe that there are many pro mising contributions in this work with regard to both its meth- odological and historical claims. With regard to the former contribution, we envision a wide variety of scholars who could make use of this work - including historians, linguists, and literary scholars. Fo r example, h istorical sociolinguists aimin g to recon- struct and analyse authorial social networks may find the processing and parsing pro- cedures particularly info rmative (Bergs 2005; Sairio 2009). Additio nally, there is the possibility of co mbining this type of metadata with massive full-text databases like Early English Books Online (EEBO) and Eighteenth Century Collections Online (ECCO). The latter - historical - contribution made by this paper is of particular rele- vance to book history. By introducing the statistical measurements unique to social network analysis, this paper augments and improves upon the existing, albeit sparse, quantitative analysis of the early English booktrade. 20 Additionally, we are part icular- ly interested in its potential contribution to intellectual h istory, and the possibility of using these methods to reconstruct intellectual contexts , such as the relationships between specific actors involved in producing and disseminating philosophical and political texts. Bib liographic records have already shown themselves to be an i m- portant foundation for research in this field (Tolonen 2013), and this paper hopes to push this research further. 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