Since this study explores university websites’ promotional language adopting corpus lin-guistics’ techniques, several theoretical issues associated with corpus-based methods require attention in this Section.
Any introductory text on corpus linguistics will at some point mention a core principle of corpus-based studies, which is representativeness.13 Balance is the second "desideratum" of corpus linguists, which is very much related to the first principle since balanced sampling is more likely to produce representative results when there are different levels or variables to be considered. Other principles are listed as the basis of research in corpus linguistics, such as reproducibility and comparability, both relying on similarity of design criteria and content data.
McEnery and Hardie (2012) express a viewpoint probably shared by the majority of corpus linguists when they claim that representativeness is more of an ideal than a reality.
Leech (2007) calls it the "Holy grail"; Kilgarriff and Grefenstette (2003) open their section on representativeness using the "Pandora’s box" metaphor. Since it is very unlikely that a corpus can include all and only the texts addressing a specific research question, sampling data is considered a viable alternative, provided that the sample reflects the whole population.
Collecting representative data that match a research question is a prerequisite for obtaining reliable results and making generalisations. This is true of any type of study, regardless of whether one is dealing with a spoken or a written corpus, whether the corpus is compiled manually or (semi-)automatically or whether it is a web- or paper- based collection.
In this respect, Fletcher (2004) makes a crucial distinction between two approaches in which the web can be exploited for corpus-based studies, web as corpus and web for
13 The term “representativity” may also be used as an alternative to “representativeness” (cf. Leech (2007:140)).
2.5 Web-based approaches to corpus construction 53
corpus. The first term quite obviously refers to the use of the web as a corpus in itself, i.e.
an enormous source of linguistic data. This approach has raised a number of issues related to representativeness, for at least two reasons. First, language content on the web is still a grey area. Although statistics show that content in English covers more than half of all webpages,14 there is in fact a lack of evidence and sound arguments for ascertaining that the web reflects English language usage in general. Even if it did, another issue would be related to the variety of language adopted within those pages. Do they reflect spoken or written English? Native or non-native? The answer remains largely unknown. Second, the most widely used tools for querying the web (as a whole) rely on search engines’ results, which are not ranked according to linguistic parameters, thus potentially skewing results.
Some examples of these tools include WebCorp (Kehoe and Renouf, 2002) and KwicFinder (Fletcher, 2004), online concordancers that enable users to query the web and analyse concordance lines. The second term, i.e. web for corpus, tends to be regarded as a safer choice in which a controlled selection of webpages is performed (Hundt et al., 2007:2). The feeling behind this tension, particularly at the very beginning of this millennium, was that automatic techniques for text mining were replacing traditional methods for corpus building – such as careful human selection of texts – thus hindering corpus quality, representativeness
and other fundamental principles of corpus linguistics:
All this adds up to the rather uncomfortable impression that in the web-as-corpus-approach, the machine is determining the results in a most “unlinguistic” fashion over which we have little or no control. (Hundt et al., 2007:3)
As much as this is true, all the debate emerging from the use of the web for language purposes and very much centred on the methods for corpus building may have diverted the focus from the reasons why one resorts to web data, the purposes for using a specific web-based corpus and ultimately the scope of one’s study. Whether one decides to crawl the whole web, a small portion of it, or instead, to manually select a sample of pages, what really matters is how that corpus will be used afterwards and whether the use of that corpus is warranted by the building procedures used. There are two main reasons for selecting the web as a source of linguistic knowledge:
1. to increase the quantity of available data, as an alternative to more traditional sources, when existing data are not sufficient for the scope of a study;
2. to address a certain research question for which the web is the ideal source of data.
14http://w3techs.com/technologies/overview/content_language/all [last consulted on 15 December 2017].
As far as the first reason is concerned, two possible scenarios are envisaged that reflect a different usage of the web as a medium to:
• store, copy and share existing material that was originally produced for a different medium (e.g. paper or television);
• create and disseminate new content, for example running text within companis’ and institutions’ webpages, blogs and forums, twitters, posts and the like.
This distinction is not always clear-cut, since webpages may be drawn from paper-based material, especially for encyclopaedic genres, which means that more caution is required when the first aim is involved. Yet, archives and text collections are often restricted to a recognizable portion of the web, thus enabling researchers to exploit it through automatic techniques and, ultimately, increase their data sets. Examples include Hoffmann (2005), who compiled a large corpus of approximately 100,000 CNN transcripts and Nesselhauf (2005), who collected 23 fiction texts (1M words) in electronic format dated from the 19th century.
Their experiments are often counted among successful research studies using the web for corpus building (Hundt et al., 2007:3).
However, it is mainly the second scenario that showed its unlimited potential in sup-plementing traditional corpora with present-day, freely accessible linguistic resources. The World Wide Web is continuously growing in size and represents an extensive source of modern language varieties. This has triggered the development of web platforms such as KWiCFinder and WebCorp, as well as other tools that let users crawl large portions of the web for research and teaching/learning purposes. The WaCky project (Baroni and Bernardini, 2006) developed a set of tools for web crawling, as well as language resources addressed to people with different backgrounds and goals in the field of languages.15 Although this project is listed among the web as corpus approaches (Hundt et al., 2007:3), web-crawled corpora diverge from online concordancers (e.g. KWiCFinder and WebCorp) since the former do not heavily rely on search engines’ indexes. By crawling specific web subdomains (e.g.
.co.uk) and querying them offline via specific software, it is possible to have a higher control on the selection of webpages, as well as on retrieved queries. UkWaC – an English web corpus compiled within the WaCky project – and enTenTen – a similar project compiled at Masaryk University – can be employed by learners of English and language professionals, as suggested by Bernardini et al. (2010). Translators, for instance, often turn to the web as their primary source of information for documentation and terminological work. Besides being suitable for pragmatic reasons, the use of UkWaC has also proven successful as a reference corpus of general English in lexicographic studies (e.g. Ferraresi et al. (2010)).
15http://wacky.sslmit.unibo.it/doku.php [last consulted on 15 December 2017].
2.5 Web-based approaches to corpus construction 55
Therefore, using the web for the first reason, i.e. to compensate for a lack of (sufficient) data from other sources should not be regarded as an undesirable procedure, even when dealing with larger domains, as long as this decision is taken after careful reflection as to whether a detailed breakdown of the type of linguistic data is needed, and whether or not the influence of the digital medium on language will skew results. This scenario also includes choosing the web as an alternative to “expensive” corpora. Relying on existing well-balanced, large corpora is sometimes not possible due to restrictions on freely accessible corpora, as claimed by Schäfer and Bildhauer (2013:1), who made a list of corpora that cannot be downloaded as a whole.
The second reason for the use of internet data is related to the development of new genres and text types. The web has reshaped traditional text types – for example by repurposing news articles into blogs – and has given rise to an altogether new range of genres, for instance forums and chat rooms, social network posts, homepages, thus inspiring new strands of research specifically aimed at studying web language and content.16 Research addressing specific web genres in corpus linguistics has adopted various methods, depending on the scope of analysis and the desired level of granularity. Gesuato (2011) performed a hand-picked, qualitative selection of academic course descriptions (ACDs) aimed at examining a prototypical sample of web ACDs and ultimately exploring genre structure and communica-tive functions. Morrow (2006) described discourse features in forums about depression: he gathered three types of messages (problems, advice and thanks) for an overall number of 85 messages in order to look at how advice-givers use discourse strategies to soften their advice.
As an example of research methods with wider scope and higher granularity, Thelwall (2008) focused on the MySpace website and automatically crawled a number of profile homepages based on a range of similar URLs. Pak and Paroubek (2010) collected a large corpus of Twitter text posts for sentiment analysis purposes, by querying Twitter for types of emoticons through the Twitter API. Bhosale et al. (2014) extracted approximately 30,000 English articles from Wikipedia to analyse promotional content. Finally, the acWaC project17 provides a large resource for exploring a neglected but strategically important domain, i.e.
the institutional academic one (Bernardini et al., 2010). Although the authors use caution in deepening their focus to internal subgenres for which they have limited control, this resource can be said to reflect English language delivered by university websites at the time of crawling and can thus be used to compare university language across countries.
As this very brief outline shows, one can employ automatic techniques to download
“noisy” data from the web that serve general lexicographic purposes, or to fetch a limited
por-16For a complete overview of web genres see Mehler et al. (2010).
17http://mrscoulter.sslmit.unibo.it/acwac [last consulted on 15 December 2017].
tion of the web to match tightly focused research questions addressing a specific web domain (that may reflect a specific genre). Crawlers (either homemade crawlers or commercial/open-source programmes) provide a flexible strategy that can be arranged to address multiple goals, from general use of British English – by crawling the .co.uk domain and use it as a reference corpus (e.g. UkWaC and enTenTen) – to specific domain-related tasks.
2.6 Summing-up
The present Chapter provided a synthesis of the key concepts and previous studies related to each strand of research underlying this work. Section 2.1 provided a definition of institutional academic language and presented two main approaches to the study of this domain, namely Critical Discourse Analysis (CDA) and corpus-based studies. Section 2.3 offered a review of the various approaches to the analysis of evaluation (lexical and grammatical approaches, implicit or explicit evaluation) and produced an inventory of lexico-grammatical features that are listed in the relevant literature as an important device for expressing evaluative meaning.
Section 2.4 took a double focus on genre: on the one hand, it reviewed existing definitions and interpretations of the concept of genre (including sub- and macro-genre) and related ones such as register, text type, and style. On the other hand, it presented top-down and bottom-up approaches to genre classification (e.g. Swales (1990) and Biber (1988) respectively), as well as examples of studies that have adopted a complementary view, for instance Egbert et al.
(2015) and Sharoff (2018). Finally, Section 2.5, presented web-based approaches to corpus construction. Given the framework and the extensive research about web-based principles, defining the target population is the first step for building a corpus that ideally matches the initial research question. Corpus design criteria and corpus building will be described in Chapter 3.
Chapter 3
Corpus design and construction
3.1 Introduction
As pointed out by Biber (1993:1), at least two elements have to be taken into account in order to define a target population for corpus building:
1. the boundaries of the population — what texts are included and excluded from the population;
2. hierarchical organization within the population — what text categories are included in the population.
The first element is determined by the specific research questions of the study and the reasons why it is being conducted. Three types of boundaries can be drawn that reflect the general aims of this thesis:
1. macro-genre boundaries, namely academic websites;
2. geographical/institutional boundaries, i.e. European universities;
3. language boundaries, i.e. English language used as a means of communication between universities and their stakeholders.
As regards genre, academic websites are typically recognizable by a unique domain name, which can be used as a macro-genre delimiter. For instance, most URLs produced by the University of Bologna are in the❤!!♣✿✴✴✇✇✇✳✉♥✐❜♦✳✐!✴❡①❛♠♣❧❡ format, where “unibo”
is the domain name associated with the University of Bologna and the same applies to most universities in Europe. Therefore, the whole population comprises any webpage produced by
universities (using an institutional domain name) to deliver informational, instructional, pro-motional content through various web (sub-)genres. Considering the extensive population to be included in the corpus, texts are collected automatically through web crawling techniques (Section 3.3).
The second type of boundary is a geographical one and coincides with European bor-ders. As already discussed in this thesis Introduction (Chapter 1), the reasons for choosing European universities are related to EU efforts to foster mobility and harmonise degree programmes and web-based communication. Yet, it would be inaccurate to choose EU-only countries – thus excluding European countries that are not currently an EU member state – since many of these non-EU countries in Europe have indeed signed specific agreements with the Union to support mobility and provide exchange grants (e.g. the Swiss-European mobility Programme). Moreover, they are generally closely associated with the EU because of political and economic agreements (e.g. Free Trade Association and European Economic Area). The reasons that have kept some countries out of the EU are not preventing these same countries from creating a multicultural education environment, especially when geographical proximity favours this process. Therefore, this project compilation process includes any country in the European geographical area that is involved in the European international scene, e.g. by taking part in the Bologna Process and European Higher Education Area (EHEA). Information on country status (EU vs. non-EU) is recorded within metadata and can thus be used to either include or exclude specific countries. Finally, at this stage no attempt is made to include countries outside the European geographical area. Conducting a global-scale study would be hardly feasible at this point due to many varieties of native English language, as well as incomparable education systems and internationalisation policies outside Europe.
Finally, language boundaries are a crucial element in this project, which aims at analysing English language in its native and lingua franca varieties. Pages that are produced in other languages will have to be excluded through automatic techniques for language identification (Section 3.3.3).
These three external boundaries draw a line between data to be included in the corpus and data to be excluded from the corpus and represent a benchmark to define criteria for corpus building (Sections 3.2 and 3.3). On the other hand, hierarchical organisation within the population refers to a set of internal categories that will group texts according to specific parameters, such as the variety of English and genre. Corpus composition and its internal organisation are described in Section 3.4.