3.1 The variable

In most varieties of Welsh, the second-person singular (informal) pronoun is ti. Another form, di, occurs in certain syntactic environments, such as the postverbal subject of certain auxiliaries that end in a vowel or as a possessor in a possessed noun phrases. In northwestern varieties of Welsh, an alternate form of the pronoun has arisen, namely chdi. Historically, this is the result of phonological reduction and dissimilation of the Middle Welsh strong second-person singular pronoun tydi. After certain prepositions and conjunctions, the consonant alternation known as aspirate mutation was (and still is) triggered (e.g. tŷ ‘house’ but â thŷ ‘with a house’, with /t/ > /θ/). This applied regularly to tydi in the relevant syntactic environments, hence Early Modern Welsh â thydi ‘with you’. Syncope of the unstressed schwa led to â thdi (normally spelled â’th di in Early Modern Welsh). In northwestern and central northern varieties of Welsh, thdi was reanalysed as a new pronoun and spread beyond syntactic environments where aspirate mutation was triggered. Finally, a dissimilation occurred in some northwestern variety of nineteenth-century Welsh, so that thdi /θdi/ became chdi /χdi/, and this new form eventually supplanted the earlier form entirely (Willis 2017).

The dialect distribution of chdi is relatively well established. It was the subject of one question asked for the Welsh Dialect Survey (Thomas et al. 2000: 555), most of whose informants were born in the 1920s; see Figure 1.¹ This shows chdi to be solidly attested in the whole of the northwest, with some scattered attestation in adjacent areas of the northeast; it is not present in the south.

Figure 1

Distribution of the forms of the second-person singular pronoun (based on data from Thomas et al. 2000: 555).

The syntactic distribution of chdi is quite complicated. As we have seen, chdi (or rather its ancestor form thdi) arose in one particular syntactic environment, namely after a small group of prepositions and conjunctions that triggered aspirate mutation (namely â ‘with’, efo ‘with’, gyda ‘with’, â ‘(equative) as’, na ‘than’, and a ‘and’). From here, it seems to have spread first to focus constructions and to other independent contexts, such as free-standing answers to questions. Welsh distinguishes strong from weak pronouns: strong pronouns occur in contexts without agreement, while weak ones occur in contexts associated with agreement. The contexts in which chdi first appeared were typical strong contexts, and all dialects that use chdi use it in these contexts, namely after non-inflecting prepositions, (1); as the second conjunct in co-ordinated noun phrases, (2); in sentence-initial (fronted) focus position, (3); and in fragment answers, (4) (which can be thought of as a reduced form of the type in (3)).

However, chdi has spread beyond these environments, and is found also in various contexts traditionally associated with agreement. This may be part of a trend towards loss of agreement more generally in Welsh (Willis 2017: 44–46). Thus, we find chdi as the subject of complement clauses headed by bod, the nonfinite form (verbnoun) of bod ‘be’. In place of the more traditional construction illustrated in (5), with second-person singular agreement marker dy and pronoun di, we now find (6), with no agreement marker and chdi alone.²

There are a number of other contexts to which chdi has spread. With the object of an inflecting preposition, such as am ‘about’, amdanat ti ‘about you’ is replaced by amdana chdi. With the subject of various auxiliary and modal elements, oeddat ti ‘you were’ is replaced by oedda chdi; (by)sat ti ‘you would (be)’ is replaced by (by)sa chdi; rhaid (i) ti ‘you must (lit. ‘it is necessary for you’) is replaced by rhai’ chdi; and byddi di ‘you will (be)’ is replaced by by(dd) chdi. As the object of a nonfinite verbal form, chdi replaces doubling of a preverbal agreement marker and postverbal di (the pattern is parallel to that in (5) and (6) above):

Finally, in possessive noun phrases, chdi again replaces doubling of a prenominal agreement element and postnominal pronoun:

In other contexts, namely those where agreement is secure, chdi has made little to no impact and ti (or its syntactically conditioned variant di) is compulsory in all dialects. Such contexts include the subject of lexical verbs, (11); the subject of the present tense of the verb bod ‘be’, (12); and the subject of imperatives, (13).

The spread of chdi is documented historically, and the historical trajectory is reflected in current dialect variation. Historically, we know that chdi is first attested in different syntactic contexts at different dates. The contexts in which it is attested earliest are also those where it is found today in the widest range of dialects. Willis (2017) investigates the historical sequence of the spread of chdi in the nineteenth and early-twentieth centuries and finds the following order of innovation:

Chdi is absent in all other contexts in the historical data. Willis (2017) also conducts a study of contemporary patterns of geospatial variation by context. He shows that current variation mirrors the historical sequence of events fairly closely, consistent with wave-like diffusion from a single zone of innovation, in the sense that chdi is found to its fullest geographic extent only as the object of non-inflecting prepositions and in other independent contexts. In all other contexts, it is found in a subset of this area, with those that innovated earlier showing a wider geographic distribution than more recent ones. The relative order of contexts in dialect variation is the following (based on the intercept values of a global logistic regression, Willis 2017: 50):

Of these, efo ‘with’ and the independent contexts had already reached their current state in speakers born in the 1920s (Thomas et al. 2000: 555). Apart from the subject of nonfinite bod ‘be’, which is not included in (15), and allowing for the fact that (15) includes a number of environments where chdi innovated only after the end of the historical period covered in (14), the order of the items is identical in the two implicational hierarchies. Given that the two hierarchies above are based on entirely independent data and methods, we can be reasonably confident that they reflect the actual course of development and geographical distribution of chdi. We can therefore treat (15) as the reference distribution, a standard of comparison for the Twitter data.

The first question, then, is whether Twitter data can accurately identify the geographic area in which chdi is used. If this question is answered in the affirmative, then a second, more demanding question is whether Twitter data can provide a level of contextual geographic detail comparable to that obtained by traditional questionnaire and/or corpus methods.

3.2 Global analysis of the data

First consider the overall distribution of results for all 6,664 tweets identified as containing a second-person singular pronoun. This is given in Table 1.

Before we turn to examine the overall patterns, some discussion of the status of formulas is necessary: formulaic expressions turn out to have a much higher propensity to contain ti than chdi and were therefore treated as a separate context. This concerned da (iawn) ti ‘(very) good (on) you’, penblwydd hapus i ti ‘happy birthday to you’, Nadolig Llawen i ti ‘Merry Christmas to you’, diolch ti and diolch i ti ‘thank you’, helo ti ‘hello, you’ and hwyl ti ‘bye, you’. In some of these cases the syntactic structure is not clear: is diolch ti ‘thank you’ an elided form of diolch i ti ‘thank (to) you’ or is it a calque of English thank you? If the latter, it is not clear whether ti should treated as the object of the nonfinite verb diolch ‘thank’ or as a syntactically independent, unintegrated unit. Even where the structure is fairly clear, these formulas did not pattern at all with their associated context: penblwydd hapus i ti ‘happy birthday to you’ clearly exemplifies the context i ‘to’, but was only ever found with ti, never with chdi, even though i chdi ‘to you’ was common elsewhere. Formulaic expressions are widely recognized to be linguistically conservative and may resist linguistic innovations that have spread to most productive contexts for some centuries. Furthermore, in Wales, such formulas are widely known, in their standard form with ti, by people with no other knowledge or only limited knowledge of the language, and may therefore be used in an otherwise English-language context. Obvious instances of performance of Welsh by non-speakers were manually removed from the dataset (e.g. a single formulaic Welsh expression in a tweet otherwise in English or anti-Welsh racist insults from accounts otherwise tweeting in English). However, accounts could not be systematically examined to try to establish whether the user was a competent speaker of Welsh. Thus, it is likely that some use of formulas represented unidentified cases of this, cf. difficulties identified for Twitter research by Jones (2015: 411) arising from performance of African American Vernacular English. Thus, while searching for formulaic expressions in an untagged Twitter corpus might seem like an attractive way of extracting large amounts of data quickly, in practice it seems unlikely to yield satisfactory results.

Since the Twitter data include all instances of second-person singular pronouns, they inevitably cover more syntactic contexts than other studies. Restricting ourselves solely to the contexts included in (15), the following hierarchy emerges from Table 1.

This hierarchy differs from that in (15) in a number of ways. On the positive side, it correctly places bydd ‘will be’ and gan ‘with’ at the far right edge of the hierarchy, in the correct order: Willis (2017) shows that these two contexts differ sharply from the others in allowing chdi over a much narrower geographic range. However, it fails to order correctly the left-hand portion of the hierarchy. In particular, the historically earliest contexts, namely object of non-inflecting preposition and other independent are not identified as the most favourable environments. A number of comments on this are in order.

First, the four leftmost environments in (16) are not statistically significantly different from one another, and their relative ordering is therefore rather insecure.³

Secondly, the age profile of Twitter users (cf. the statistics given in section 2 above) means that we are, on average, dealing with speakers considerably younger than were used to establish the hierarchies in (14) and (15). Consequently, the data can be interpreted as representing, on average, the language of speakers born in the 1990s. All but the final two contexts in (16) are ones that Willis (2017) found had run to completion in speakers of this age group. Consequently, we would expect the frequency of chdi in these contexts to have largely reached its ceiling, this ceiling being set by the proportion of Twitter users coming from the chdi-region.

While this is reassuring about the adequacy of the Twitter data, the idea that the change has run to completion in a number of syntactic contexts sits uneasily with some other observed significant differences in the percentages given in Table 1. Some of these other differences seem to be due to another, somewhat surprising fact, namely that the syntactic contexts themselves are not evenly distributed across the map. Above all, the context non-inflecting preposition is itself spatially autocorrelated. Figure 2 shows a k-nearest neighbours kernel density estimation (KDE) (bandwidth = 2*√(dataset size) = 115.8 nearest neighbours) of the proportion of data points in the dataset that are instances of the non-inflecting preposition context; that is, it shows the mean frequency of the context across the 115.8 nearest data points to each location.⁴ The red areas on Figure 2 are those with above-average frequency of this context, with the darkest red showing areas where the frequency is more than 2 standard deviations away from the mean; the blue areas show the inverse, with areas below the mean. From this, it can be seen that, somewhat counterintuitively, the non-inflecting preposition context is found in the data significantly more frequently in the south than in the north, with a zone of very significantly elevated occurrence across all of the southwest. Since so much of the data for this context comes from the south, where chdi is not found, it is not surprising that the frequency of chdi overall in this context is lower than might be expected.

Figure 2

Map of geospatial distribution of the context non-inflecting preposition in the Twitter dataset.

Once this issue is identified, the reason why it arises can be seen fairly readily. Some non-inflecting prepositions are found only in certain dialects; for instance fatha ‘like’ and efo ‘with’ are both northern only. However, in these cases, other dialects express the same meaning also with a non-inflecting preposition (fel and gyda respectively), cancelling out any effect. Conversely, the ‘have’-construction, which varies significantly between dialects, contains a non-inflecting preposition in some dialects but not in others. Thus in southern varieties, the ‘have’ construction uses non-inflecting gyda ‘with’ in (17), while the commonest ‘have’ construction in the north uses inflecting gan ‘with’ in (18). A third construction using efo in (19) is confined to the north, but maps the object possessed to the object of the preposition, hence ‘you’ is the subject of the verb bod ‘be’ in this context and is counted in the statistics accordingly.

The result is that non-inflecting prepositions are simply commoner in the south than in the north, artificially raising the global frequency of ti for this context. This highlights the need to interpret the data geospatially, as will be done in the next section.

3.3 Geospatial analysis of the data

The previous section showed the need to include a geospatial dimension to the linguistic analysis. This is what will now be attempted. Consider first the overall distribution of chdi, shown in Figure 3. Here, each circle represents a tweet location; the colour of the circle represents a KDE value for the frequency of chdi at that point: KDE calculates the mean frequency of the variant within a kernel centred on the point in question. The size of the kernel is determined by the KDE bandwidth. KDE has previously been used to map dialect variation by Blaxter (2017) and for dialect variation in Twitter data by Jones (2015) and Van Halteren, Van Hout & Roumans (2018). In all cases in the current article, the bandwidth is twice the square root of the subset of data under consideration, in this instance, the nearest 115.8 data points.⁵ This method will be repeated for the various syntactic contexts below.

Figure 3

Overall distribution of chdi in the Twitter data.

Figure 3 shows the overall pattern for all the data. The frequency of chdi never exceeds 45% in any region, because these data include syntactic contexts in which chdi is categorically excluded in all varieties. The close similarity to the pattern found in the Welsh Dialect Survey in Figure 1 above is nevertheless clear, with a core region where chdi is strongest in the northwest, surrounded by a transitional ring to the east and southeast with moderate frequency of use.

For direct evaluation of the success of the Twitter method, we need to compare each syntactic environment with parallel traditional data. This is done below by comparing the geospatial pattern found in the chdi data with data from the Syntactic Atlas of Welsh Dialects. In each case, two maps are provided, constructed according to the same principles: KDE is conducted for the SAWD data in the same way as it was done for the Twitter data.

The data collection for SAWD assumed that chdi was not found anywhere in the south; the south is thus blank on the SAWD maps because no questionnaire data was collected there. For the Twitter data, the south is retained, as it is not obvious in advance that the method can correctly rule out the presence of chdi in the south; indeed, some instances of chdi are localized to points in the south, although KDE smoothing means that these points do not have a significant impact on the final outcome.

An examination of data across the hierarchy of syntactic contexts shows that overall distributions produced by the two methods are rather similar. Figure 4 shows the two historically primary contexts, after a non-inflecting preposition and in independent syntactic contexts (mainly focus and sentence fragments).

Figure 4

Comparison of Twitter and SAWD results for object of non-inflecting preposition and independent use of second-person singular pronouns.

Here, as in all cases examined, SAWD always produces higher absolute rates for chdi in regions where it is present, presumably because the oral questionnaire that was used focused speakers’ attention on spoken usage and further because the Twitter data contain a proportion of material in standard written Welsh (see also discussion of Twitter as a proxy for spoken data in section 4.2.1 below). The Twitter data for object of non-inflecting preposition are for all such prepositions occurring in the corpus; the SAWD data are for the object of the one such preposition, efo ‘with’, that was included in the questionnaire.

For the object of non-inflecting prepositions, the Twitter data overstate the geospatial distribution of chdi by attributing majority chdi usage to the whole of the northeast. This is due to the relatively small size of the dataset once it is subdivided into different syntactic contexts: of the 277 data points categorized as non-inflecting preposition, 140 could be localized; and of the 266 categorized as independent use, 129 could be localized. With this size of dataset, mislocalization of one or two data points (for instance, because a user mentions only the place where they now live, but not where they grew up, in their user description) can have a significant impact even once smoothing has applied. This particularly affects the northeast, where there are rather few data points (8–10) for these contexts. This is an issue that is likely to arise from time to time when using Twitter for smaller languages, but its effect would likely be reduced with a larger dataset collected over a longer period of time. For the independent-use context, the data contained no such outlier points and the issue did not arise. The result is an isogloss from the Twitter data that is remarkable close to that found using traditional means.

The two contexts discussed so far are historically primarily; that is, the changes that produced the current dialect distribution occurred in the eighteenth and nineteenth centuries, and there is little evidence of ongoing change in their geographical distribution today. We turn now to consider in turn contexts where there is more ongoing change. Consider first the object of the semi-inflecting preposition i ‘to’ and the fully inflecting prepositions (mainly am ‘about’, ar ‘on’ and o ‘from’), shown in Figure 5.

Figure 5

Comparison of Twitter and SAWD results for object of i ‘to’ and object of non-inflecting prepositions.

These are contexts where chdi emerged in the late-nineteenth century, and where there is some evidence today that chdi is still spreading geospatially. For i ‘to’ (186 localized points), the area for chdi defined by the two methods is again remarkably similar; for inflected prepositions (83 localized points), the Twitter result again slightly overstates the use of chdi in the northeast, and for the same reasons as before: the inflected-preposition dataset is too small to be immune to the presence of a small number of points whose localization does not reflect the place where the user in question grew up.

Next consider the subject of the imperfect auxiliary oedd ‘were’, the modal rhaid ‘must’, and the conditional auxiliary (bua)sa(i) or byddai. These are grouped together because previous research (Willis 2017: 58) has shown them to be undergoing similar rates of change today with respect to the spread of chdi, and the Twitter dataset clearly contained too few instances of each context (50 localized tweets for oedd, 17 for rhaid and 55 for the conditional) for analysis on an individual basis to be viable. Results for these three contexts combined are shown in Figure 6. The Twitter results agree with SAWD in identifying a smaller geographic area for chdi in these contexts than for the earlier contexts we have considered. Nevertheless, this is one context where the Twitter results suggest a somewhat wider geographic spread that the SAWD results. This may be due to the fact that these are contexts currently experiencing rapid diffusion of chdi, and the Twitter data reflect on average a younger age group, in which chdi does indeed have a wider geographic distribution. Closer inspection of the data suggest that the difference is mainly due to chdi being the overwhelming form among tweets localized to the Llŷn Peninsula around Pwllheli, while ti is the dominant choice (although by no means the only choice) of informants in this region in the SAWD questionnaire. This probably is a genuine instance of diffusion and change in progress.

Figure 6

Comparison of Twitter and SAWD results for subject of oedd ‘were’, rhaid ‘must’ and the conditional auxiliary.

Finally, consider the two contexts where chdi is a very recent innovation found only in a very small geographic area, namely the subject of the future auxiliary bydd ‘will be’ and the object of the preposition gan ‘with’. These results for these contexts are shown in Figure 7. In both cases, the Twitter dataset is small (47 localized tweets for bydd and 79 localized tweets for gan), and, consequently, it is difficult to draw firm conclusions. However, the results are consistent with the SAWD questionnaires. The Twitter data agree with SAWD in showing that these are the contexts where chdi is most geographically restricted and where it is the minority choice everywhere. Both methods record slightly higher frequencies in the northwest. The greater richness of the SAWD data allows the identification of a centre of innovation in the Caernarfon area. The Twitter data do not contradict this: all the localized tweets for these contexts turn out to be either from the Caernarfon area or from users ultimately from this area who have moved elsewhere (but who ended up being localized to where they currently live).

Figure 7

Comparison of Twitter and SAWD results for subject of future auxiliary bydd ‘will be’ and object of preposition gan ‘with’.

3.4 Discussion

The overall geospatial distribution of chdi established using Twitter data broadly matches that established using traditional means (Figures 1 and 3). While the global analysis of the distribution by syntactic context produces a hierarchy of contexts that differs in points of detail from the traditional one, and does not identify the historically primary contexts, the more fine-grained geospatial analysis conducted in section 3.3 suggests that this is to some extent due to spatial autocorrelation of the syntactic contexts themselves within the dataset. When plotted as maps for individual contexts, the Twitter data largely agree with the result from the earlier SAWD project. Two sources of discrepancy were identified: in a number of cases, insufficient data, either for a particular syntactic context globally, or for a particular geographic region, resulted in oversmoothing or lack of geospatial detail for the Twitter maps; in one case, subject of auxiliaries in Figure 6, the greater geographic extent of chdi identified in the Twitter data may plausibly represent real ongoing diffusion that accurately reflects the younger demographic of the Twitter data.

The hierarchy of contexts established by traditional means in (15) above is also largely respected. This can be seen by looking at the estimated smoothed frequencies at various locations, both within and beyond the traditional chdi-region, as shown in Table 2. If the hierarchy in (15) is respected, the values should decrease monotonically from left to right within the traditional chdi-region. This is broadly the case. There are some exceptions for the four most well-established contexts on the left of Table 2, contexts where the change may well have run to completion within the chdi-region. Outside of the chdi-region, we find scattered noise in the estimates for the four southern locations (Aberystwyth, Cardiff, Cardigan and Carmarthen), where chdi is absent from the local varieties. Aberystwyth and Cardiff have significant student populations; Cardiff, as the capital, has other migration from the north, and, furthermore, as an area of language revitalization, has an emerging new variety with some levelling of features from the south, the north and the literary standard. It is therefore not surprising that Aberystwyth and Cardiff have more noise, in the form of higher estimates for chdi, that other southern locations. Finally, Denbigh, in the northeast, shows a similar overall pattern to the northwest, but at rather lower frequencies. As we have seen, this is partly due to lack of data and artefacts of smoothing, although it may also suggest some genuine spread of chdi to this area.

4.1 The variable

Deletion of auxiliary bod ‘be’ in the present tense is characteristic of all varieties of spoken informal Welsh today. Thus, in the AuxSVO structure in (20), the auxiliary (r)wyt may be reduced or omitted entirely.

Such deletion is not restricted to the second person singular and is found with most other pronouns, at least in some varieties. It has even been reported for clauses containing lexical NP subjects (Davies 2010: 270).

Deletion is not dependent on the auxiliary being in absolute clause-initial position: it is grammatical in an embedded AuxSVO clause in (21) and in a main-clause wh-question in (22).

Auxiliary deletion in the second person singular requires ti rather than chdi as the subject pronoun (99.6% ti in Table 1 above). It thus patterns with its overt counterpart wyt ti ‘you are’ (100.0% ti), rather than with the independent-use context (68.0% ti). This suggests that auxiliary deletion should be interpreted as involving a real auxiliary that happens to be null, rather than as a construction with an independent pronoun not dependent on any auxiliary. Another argument in favour of this view is that a tag question with a full auxiliary may co-occur with a main clause containing auxiliary deletion (Borsley, Tallerman & Willis 2007: 260–61). If tag questions in some sense involve copying of the auxiliary of the main clause, this would suggest there is a syntactically represented auxiliary in the main clause.

It is agreed in the literature that the availability of auxiliary deletion is conditioned by person and (to a lesser extent) by number (Jones 2004: 101–2; Borsley, Tallerman & Willis 2007: 260–61; Breit 2012). There is a striking degree of variation between dialects in some person–number combinations; for instance, auxiliary deletion in the first person plural is common in the south but very rare in the north.

Davies (2010: 258–335), Davies & Deuchar (2014) and Davies (2016) investigate auxiliary deletion in the second person singular by 28 Welsh speakers from the Siarad Corpus of spoken Welsh (Deuchar et al. 2014; Deuchar, Webb-Davies & Donnelly 2018), 8 of whom grew up in the south and 20 of whom grew up in the north or with a northern background. Davies (2010: 285, 297) finds an overall frequency of auxiliary deletion of 92.8%, with slightly lower levels in older speakers (84.8% in the over-50 group, compared to 93.8% in the under-30 group) and thus age turns out to be a significant predictor of frequency of deletion. However, even some speakers born in the 1920s have deletion with a frequency approaching 100%, indicating that auxiliary deletion is a historically well-established feature of spoken Welsh. Indeed, Willis (2016) argues that the roots of auxiliary deletion go back to the nineteenth century, where it is used in literary representations of second-language Welsh. While Davies (2010: 293) found women to delete auxiliaries at a slightly higher rate than men, this difference was not significant; nor were there significant differences according to the region in which a speaker spent their first year of life (Davies 2010: 295).⁶

The impact of factors other than person and number on the frequency of auxiliary deletion is less well understood. Davies (2010: 303–22) investigated the impact of four factors in the Siarad corpus, failing to find any significant effect for clause type (declarative vs. interrogative), linguality (the presence of absence of code-switching in the clause), or negation. He found an inverse relationship between deletion of the auxiliary and deletion of an aspect marker in the same clause.⁷ Breit (2012) administered an acceptability-judgment task to 20 native speakers, almost all from north Wales. He found negligible degradation of acceptability of deletion in negative and interrogative clauses, in line with Davies’ findings that these are not factors conditioning variation. With focus fronting, a factor not investigated by Davies, he found a large reduction of acceptability of deletion in clauses with VP-fronting:

Such examples leave the sequence deleted auxiliary + subject pronoun in clause-final position and require deletion of the stressed auxiliary, leaving the unstressed pronoun to form the final phonological word of the sentence. This could be a phonological reason to disfavour deletion.

Davies (2010: 323–8) argues that the initial innovation of auxiliary deletion was internally motivated and due to phonological erosion, but that, once it had been innovated internally, it was accelerated by external factors, namely isomorphism between the SV(O) word order that results and the normal word-order pattern in English. This accords well with the nineteenth-century perception of it as a feature of non-native Welsh.

Davies further suggests that dialect differences arose because deletion was favoured in contexts where the auxiliary began with a vowel, thus northern ’dan ni ‘we are’ resists auxiliary deletion, while the equivalent southern form ŷn ni favours it. Note that both forms are ultimately reductions from Early Modern Welsh yr ydym ni (PRT be.PRS.1PL we) > rydyn ni > dyn ni > dan ni in the north and yr ydym ni > rydyn ni > rŷn ni > ŷn ni > ni in the south. This account thus amounts to saying that reduction has proceeded further in the south than in the north. The reasons for this differential degree of reduction remain unclear.

The same dataset collected to investigate the geographic distribution of chdi can be used to investigate auxiliary deletion in the second-person singular. In this environment, auxiliary deletion is common in all dialects of Welsh. Previous studies have not identified geospatial variation to date. The first question addressed by the data here is whether Twitter data provide a useful proxy for spoken data. Clearly, Twitter is a written rather than a spoken medium, but the informal register of Twitter data can make it an attractive proxy for much more difficult to obtain transcriptions of spoken language. In this case, we can compare the frequency of auxiliary deletion in tweets with its frequency in spontaneous speech as reflected in the Siarad Corpus. In addition, we can examine whether existing statements about the effect of linguistic and geographic factors on variation are borne out by the Twitter data.

4.2 Data analysis

4.2.3 Geospatial distribution

Having looked at the impact of linguistic factors in the global distribution, we turn now to the geospatial distribution of auxiliary deletion. In total, of the 2,245 tweets containing either overt or deleted auxiliary ‘be’, 1,100 (49.0%) could be localized at 189 distinct localities. The KDE-smoothed distribution is shown in Figure 8. Auxiliary deletion is the majority option everywhere, although there is some regional variation. The highest values, 80–95% are found across the northwest, with slightly lower values (75–85%) in the southwest, and the lowest values in the east (65–75% in the northeast, 65–72% in the southeast).

Figure 8

Geospatial distribution of second-person singular auxiliary deletion in Twitter data.

The success of this result is more difficult to assess than that the result for chdi in section 3.3 above, because the reference distribution against which to compare this result is itself not clearly established in the literature. While Davies (2010) found no statistically significant differences according to the region in which a speaker spent their first year of life, the analysis divided Wales into only two regions (north and south) and the speakers investigated were mostly from the north, so it is possible that such differences were missed.

A partial comparison with data from the SAWD questionnaire is possible, although it too is not ideal for current purposes. Nevertheless, such a comparison reveals rather similar overall patterns. No question in SAWD aimed specifically at testing variation in auxiliary deletion in the second person singular. However, the environment arises fortuitously in nine questionnaire items. Unfortunately, these are all in wh-questions or in subordinate clauses. Furthermore, most of the questions where the environment does arise were asked only in one region (north or south), which makes the data less than ideal for the current comparison. The only relevant item asked in all areas was question 45 (‘If you’re not happy, don’t come.’). No previous study has tested in detail whether a finite subordinate clause of this type is a favouring or disfavouring context for auxiliary deletion. Davies (2010: 316) notes that there are only 4 instances of this context in his materials, with a rate of deletion of 50.0%. This would in principle be a very low rate of deletion and would suggest that this is a strongly disfavouring context for auxiliary deletion. However, as Davies notes, the rarity of the context makes any conclusions difficult to draw. Nevertheless, the possibility that this is a disfavouring context should be borne in mind when interpreting the results.

A KDE plot of the responses for auxiliary deletion in this item (question 45) in SAWD is shown in Figure 9. The overall rate of auxiliary deletion is rather low, at 57.4% of 155 observations. This is either because the questionnaire-based interview favoured its retention or, in line with the discussion above, because the syntactic environment tested is itself one that favours retention. Geographically, we find the highest rates in the north, with 50–75% in the northwest and 50–65% in the northeast; rates are lower in the south, with 20–40% in the southwest, 25% around Cardiff and 10–20% in the central south. The north–south distinction is rather similar to that found in Twitter in Figure 8, albeit at lower absolute levels. An east–west division is clear in the SAWD data in the south, as in the Twitter data. This east–west effect is stronger in the Twitter data than in the SAWD data.

Figure 9

Geospatial distribution of second-person singular auxiliary deletion in SAWD data.

A reasonable hypothesis is that the east–west effect is due to the linguistic impact of language revitalization: in the east, closer to England, Welsh is a minority language and more dependent on revitalization efforts and Welsh-medium education to ensure language maintenance. Such a scenario promotes standard forms, reducing the frequency of colloquial options like auxiliary deletion. If so, it is not surprising to see this effect more strongly articulated in the Twitter data, where L2 speakers cannot be fully removed from the dataset. Further investigation is needed to establish whether this interpretation can be substantiated by additional research.