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## D IATOPIC VARIATION IN DIGITAL SPACE : W HAT T WITTER  CAN  TELL  US  ABOUT T EXAS E NGLISH DIALECT AREAS

Axel Bohmann University of Freiburg axel.bohmann@anglistik.uni-freiburg.de

## Abstract

The availability of large amounts of social media text offers tremendous potential for  studies  of  diatopic  variation.  A  case  in  point  is  the  linguistic  geography  of Texas,  which  is  at  present  insufficiently  described  in  traditional  dialectological research.  This  paper  summarises  previous  work  on  diatopic  variation  in  Texas English  on  the  basis  of  Twitter  and  presents  an  approach  that  foregrounds functional interpretability over a maximally clear geographical signal. In a multidimensional analysis based on 45 linguistic features in over 3 million tweets from across  the  state,  two  dimensions  of  variation  are  identified  that  pattern  in geographically  meaningful  ways.  The  first  of  these  relates  to  creative  uses  of typography and distinguishes urban centres from the rest of the state. The second dimension  encompasses  characteristics  of  interpersonal,  spoken  discourse  and shows an East-West geographical divide. While the linguistic features of relevance for the dimensions are not generally considered in dialectological research, their geographic patterning reflects major tendencies attested in the literature on diatopic variation in Texas. 1

Keywords: Texas  English,  diatopic  variation,  dialectology,  Twitter,  computermediated discourse, register, multi-dimensional analysis

1 I am grateful to Alex Rosenfeld for sharing his data with me. This work was initially presented at a panel on Twitter in sociolinguistic research at NWAV 49, organised by Stef Grondelaers and Jane Stuart-Smith. I would like to thank both of them for giving me this opportunity and the attendees of the panel, especially Lars Hinrichs and Alex Rosenfeld, for fruitful discussion. Finally, my gratitude goes to Erling Strudsholm and Anita Berit Hansen for their invitation to participate on the Coseriu Symposium and their patience in organising this special issue.

## 1. Introduction

The  availability  of  large  amounts  of  social  media  text,  geotagged  with precise  latitude-longitude  information,  offers  tremendous  potential  for studies of what Coseriu (1955) refers to as diatopic variation, i.e. spatially stratified  language  use.  Often,  such  research  adopts  the  view  of  lexical variation, establishing how individual word use creates dialect areas. While such a perspective is statistically powerful in that it can draw on the full range of words contained in a data set (and their collocational behaviour), it sometimes sacrifices interpretability. Specifically, whether differences in word use reflect meaningful linguistic differences or are rather tied back to local discourse referents (such as sports teams, place names, etc.) is not always easy to establish.

The present paper proposes an analysis of diatopic variation in Texas English based on a corpus of Twitter messages from across the state, but focuses its attention on specific linguistic features attested in the literature to  play  an  important role in distinguishing registers and lects. Based on frequency information for each of these 45 features in the subcorpus of tweets for each Texas voting precinct, a multi-dimensional analysis (MDA; Biber 1988) is run to identify fundamental dimensions of variation in the data.  Variation  along  these  dimensions  is  interpreted  in  geographic  and social terms and shown to be meaningfully correlated with these factors. While the restriction of the analysis to a pre-selected set of features loses much of the word-level information other methods can draw on to establish dialect  areas,  the  method  gains  in  interpretability  since  each  feature  is associated directly with functional or stylistic motivations.

## 2. Regional variation in Texas English

Diatopic  variation  in  Texas  English  has  been  the  subject  of  as  much controversy as it has received attention. Throughout the second half of the twentieth century, repeated attempts were made to delineate Texas English dialect areas, with a significant amount of disagreement among individual authors (Underwood 1990). The first dialect border drawn across the state of Texas appeared in Baugh's (1935:447) map of US dialect regions and divided the state into an Eastern and a Western part, by a line extending in a roughly northerly direction from Victoria on the Gulf Coast in the South to Sherman in the North (see Figure 1). This basic division between East

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## Scandinavian Studies in Language, 14(2), 2023 (73-103)

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and West Texas is retained in many other dialect maps, although the precise location of the border is subject to a great degree of variation.

Figure 1: American English dialect regions, according to Baugh (1935: 447).

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Most later attempts at identifying Texas English dialect areas were heavily indebted to the Linguistic Atlas of the United States and Canada , the first major project in North American dialectology. This project established a tripartite division of American English into a Northern, a Midlands, and a Southern dialect area, initially based on lexical evidence from the North East  and  the  states  along  the  Eastern  Seaboard  (Kurath  1949),  but  later replicated in further regional studies associated with the Linguistic Atlas project (see Grieve 2016:1-8 for an overview). Bagby Atwood, surveying Texas English for the Linguistic Atlas , came to the conclusion that Texas speech was characterised by a mixture of Southern and Midlands dialect words, with the addition of Spanish loans, and did not show clear indication of internal dialect boundaries: 'I will not draw lines showing the limits of Southwestern or of any of its subareas. Far too many lines have been drawn already, probably  by  popular  demand  and  certainly  on  insufficient North

## Scandinavian Studies in Language, 14(2), 2023 (73-103)

North Midland

South Midland

evidence'  (Atwood  1962:98-99).  However,  the  extrapolation  of  dialect areas from the individual projects associated with the Linguistic Atlas to the  entirety  of  the  United  States  has  often  led  to  the  postulation  of  a boundary between Midlands and Southern dialects extending across the state of Texas. A composite map produced by Grieve (2016:4) to represent the joint findings of the Linguistic Atlas (reproduced here as Figure 2), for instance, dissects Texas into a Southeastern (Southern dialect area) and a Western (Midlands) part.

Figure 2: Dialect areas established by the Linguistic Atlas project (composite map from Grieve 2016:4).

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In his survey of fifteen different attempts to sub-divide Texas into dialect areas, Underwood (1990) takes a pessimistic, if not cynical, stance. Neither do the individual sources converge on any common pattern, he argues, nor do  they  give  an  indication  of  any  reliable  evidence  for  the  lines  they establish:

Some of these representations of Texas dialects  are,  at  best,  based  upon scant and sometimes dubious evidence. More commonly, they are based on nothing more than conjecture, speculation, or personal whim. And even a few scholars have justified their dialect maps of Texas on the basis of earlier research by linguists who had previously concluded that their evidence did not  warrant  establishment  or  even  postulation  of  dialect  boundaries  in Texas. (Underwood 1990:96)

Underwood's point, crucially, is not that it is implausible per se to map dialect areas of Texas. Rather, he takes issue with the paucity of evidence on whose basis such mapping is conducted and the lack of accountability in the literature he cites. Since the publication of his article in 1990, major empirical advances have been made, however. Chief among these is the Atlas  of  North  American  English:  Phonetics,  Phonology  and  Sound Change (ANAE; Labov et al. 2005), which maps phonological variation across the nation based on telephone interviews with 762 informants. The phonologically based dialect regions established therein see Texas squarely belonging to the South, an area characterised by monophthongization of the PRICE vowel and the Southern Vowel Shift. Grieve (2016:210, 218) as well, in his study of regional grammatical variation in American English, based on a large corpus of letters to the editor, produces maps which leave Texas largely intact as a unified whole, bar its westernmost tip around El Paso.

It  would  appear,  then,  that  recent  evidence  converges  on  the uniformity of Texas English. Atwood's reluctance to draw dialect borders within the state, Underwood's criticism, and the tendency for Texas English to be subsumed under one dialect region in recent, large-scale studies all appear to point in this direction. It has to be remembered, however, that diatopic variation is an inherently scalar notion: far from being limited to a finite set of large-scale dialect regions, variation is pervasive in language down to small-scale  individual  differences  (Kretzschmar  2015).  Indeed, this variability and the complex settlement history, rather than any simple notion of uniformity, is what inspired Atwood's scepticism of clear borders. It  should  also  be  noted  that,  while  the  individual  studies  derided  by Underwood  (1990)  differ  in  the  precise  borders  they  draw,  there  is overwhelming convergence between them in regard to a general East-West division of Texas English.

There  certainly  is  evidence  for  internal  linguistic  differentiation within the state that warrants closer attention. Bailey (1991), providing the kind  of  careful  empirical  evidence  whose  absence  Underwood  (1990) laments in other studies, shows two important dimensions of distinction in ongoing  changes  in  Texas  English.  The  first  is  a  regional  distinction between East and West Texas and the second an increasing tendency for urban and rural areas to take diverging courses of linguistic development. A study by Cukor-Avila and colleagues (2012) complements this picture from  a  perceptual  perspective.  Based  on  dialect  maps  drawn  by  367 informants  from  across  the state, they establish a complex  set  of dimensions along which Texans perceive linguistic differences within the state.  They conclude that 'Texans do not view themselves as a homogeneous speech community, nor do they consider Texas to be the land of cowboys and hillbillies'. (Cukor-Avila et al. 2012:18)

While the integrity of Texas English from the coarse perspective of large-scale  US  dialect  regions  may  be  taken  as  consensus,  then,  there remains a good deal of internal variability at a finer level of granularity that invites further exploration. The associated challenges are: a) the complexity  of  the  socio-linguistic  fabric  of  the  state,  where  complex settlement histories, urbanisation, language contact with Spanish, etc. are likely to engender variation along multiple dimensions of differentiation and  b)  the  necessity  of  large  amounts  of  data,  ideally  with  good geographical  resolution  and  additional  social  information  to  model  this complexity. The following section explores how data from social media, and especially Twitter, may  help  to address these challenges and summarises extant research in this direction.

## 3. Mapping diatopic variation with social media data

A fundamental problem in mapping diatopic variation is achieving good geographic coverage. The foundational dialectological surveys reviewed in the previous section, such as the Linguistic Atlas and the ANAE, involved a  staggering amount of resources to achieve this task. Yet, even ANAE only  represents  145  cities  systematically,  with  additional  data  points opportunistically incorporated during the data collection procedure (Labov et al. 2005:23).

Data  from  the  social  media  service  Twitter  offer  the  potential  to achieve  much  higher  geographic  resolution  -  in  principle  down  to  the precise  latitude  and  longitude  coordinates  a  tweet  was  sent  from,  at  a fraction of the cost. With 821 million daily tweets in 2020 (a number that has steadily increased from 340 million in 2012; Yaqub M. 2022), many of which are geotagged, it has become possible to use big-data approaches to investigate linguistic developments in real time and across space at a level of detail dialectologists of the twentieth century could not have foreseen.

The fact that tweets can be directly harvested from Twitter's application programming  interface  (API)  in  a  structured  format  that  lends  itself  to downstream analysis further enhances the possibilities of doing Twitterbased dialectological research.

Data of this kind have been used in a number of previous studies. Takhteyev  and  colleagues  (2012)  find  that  Twitter  networks  tend  to coincide  with  geographical  units  (such  as  metropolitan  areas)  to  a  high degree, and that translocal network ties on the service are best predicted by variables  that  indicate  the  spatial  connectedness  among  places,  such  as frequency of air travel between them. These observations lend credibility to treating Twitter not as a locally undifferentiated virtual space, but as an arena in which regionally specific linguistic processes are likely to find articulation. Eisenstein and colleagues (2010) as well as Russ (2012) both report important geographical patterning of lexical variation on Twitter in the USA. The former authors identify new sets of topic-sensitive words with regional specificity, whereas Russ applies known indicators of dialect variation  (such  as soda , pop ,  and coke as  generic  terms  for  carbonated sweet beverages) to Twitter data and proves that corresponding findings replicate those derived from linguistic atlas data. Lexical variation between British  and  American  terms  is  further  studied  on  a  global  level  in Gonçalves et al. (2018). Eisenstein and colleagues (2014) further comment on the utility and methodological challenges of large-scale social media data for identifying regional dialects. A number of studies have mapped English dialect areas based on Twitter data. This is done for British English on a lexical basis by Grieve and colleagues (2019) and on the basis of a well-known grammatical alternation, the ditransitive, by Stevenson (2016). Strelluf  (2020)  maps  one  particular,  low-frequency  grammatical  feature (NEED/WANT + past participle) in tweets from cities across the Englishspeaking  world.  The  diatopic  perspective  may  also  productively  be combined  with  a  diastratic  one,  e.g.  in  research  focusing  on  regional variation in African American English (Austen 2017; Jones 2015). A useful overview  of  the  potentials  of  social-media  dialectology  is  provided  by Eisenstein (2018).

Before discussing some of the potential issues in using Twitter data to establish dialect areas, it is useful to summarise one particular approach that has been successfully applied for analysing diatopic variation in Texas English.  Rosenfeld  (2019)  employs  a  modified  variant  of  the  method proposed in Hovy and Purschke (2018) to model differences in word usage across  Texas  voting  districts.  The  central  element  of  the  method  is  a distributional model of word usage, a so-called word embedding model. Such models learn information about word usage based on large corpora of text, which they use to create similar vector representations for words with similar usage properties in a high-dimensional space. Lexical items that occur in similar contexts, i.e. with similar collocates, are located in close proximity in the vector space. Hovy and Purschke apply a version of such embeddings, the doc2vec algorithm (Le & Mikolov 2014), which learns representations for (in their case) different cities in the same space in which the  words  are  represented,  based  on  the  words  that  occur  in  tweets associated with a given city. For instance, the four nearest neighbours in embedding space to the city of Vienna are the words leiwand , ur , bissi , and oida , all of which are clearly recognisable Viennese dialectal forms (Hovy & Purschke 2018:4390). This allows a) for similarity relationships among cities  to  be  expressed  mathematically  and  b)  for  the  association  of particular words with particular regions to emerge. The innovation in Hovy and  Purschke  (2018),  a  study  of  German  dialect  areas,  lies  in  their application  of  retrofitting.  In  order  to  achieve  a  smoother  geographic signal,  they  update  the  vector  for  each  city  with  information  from surrounding cities. This allows them to model German dialect regions in good  accordance  with  independent  dialectological  work  and  with  the potential for analysis 'at a finer granularity than was previously possible' (Hovy & Purschke 2018:4383).

Rosenfeld makes important methodological contributions to further refine this method, which are, however, not the main focus here (for details, see  Rosenfeld 2019:87-94). What is important are his results for Texas English. Based on a corpus of 2.3 million tweets, each mapped onto one of the 8,000 voting precincts in the state, Rosenfeld establishes a (smoothed) vector representation for each voting precinct in word/document embedding space and subsequently subjects this data set to agglomerative clustering (Ward 1963). The result are the nine different dialect regions shown in Figure 3.

El Paso Amarillo Lubbock Odessa San Angelo

· Waco

- Temple

Austin « College Station

Austin HoustoBeaumont

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Figure 3: Texas English dialect regions established by Rosenfeld (2019: 111).

The map both reflects and gives a more precise shape to many observations that  have  been  made  about  variation  in  Texas  English  in  the  past.  For instance, the pervasive notion of an East-West division is reflected in the line separating the yellow (West Texas) precincts from the large areas of red (East Texas) and purple (South Texas). Bailey's (1991) observations regarding the increasing role of urbanisation are also directly reflected in the  map,  with  each  of  the  major  cities  (Dallas,  Houston,  Austin,  San Antonio) being identified as a separate cluster, and additional clusters for the suburbs of Dallas and Houston. San Antonio is interesting because it clusters together with El Paso and Laredo. All three cities are quite distant from each other, so geographical proximity cannot be the reason for their clustering  together.  Rosenfeld  (2019:101)  explains  this  behaviour  with respect to the high percentage of Hispanic populations in all three cities, showing that the method not only capitalises on diatopic but also diastratic variation.

Figure 3 amply demonstrates the power of combining large amounts of geo-tagged social media data with advanced machine-learning methods to achieve high-resolution dialectological maps. In interpreting the results linguistically,  however,  some  issues  arise  that  traditional,  survey-based dialectology does not run up against. Some of these have to do with the drawbacks of social-media data in general. First, while careful selection of participants  is  an  important  step  in  more  traditional  work,  and  precise information about many socio-demographic variables pertaining to each informant may be collected, Twitter data is 'demographically lean' (Iorio 2009). Information about the typical social characteristics considered in twenty-first-century dialectology - age, gender, ethnicity, socio-economic status  -  is  not  systematically  represented  for  Twitter  users.  The  most common response has been to use census-level statistics for each district or voting precinct as a stand-in for individual users' information (Eisenstein 2015; Rosenfeld 2019).

Next, there is the issue of representativeness. It is obvious that use of social media skews towards certain segments of the population and that Twitter  discourse  is  thus  not  fully  representative  of  offline,  vernacular language  use.  However,  the  precise  relationship  between  these  two concepts is rarely explored (although see van Halteren 2021 for a critical perspective), in part due to the fact that a full description - or even faith in the existence - of such a thing as offline, vernacular usage is anything but self-evident. The response to both these points of criticism has often been that the proof is in the pudding: in big corpora of Twitter language, attested patterns  of  offline  use  are  replicated  (e.g.  Hovy  &  Purschke  2018)  and district-level social information acts as a meaningful predictor of variation (Eisenstein 2015).

There is, however, a further issue that is more germane to the precise method  used  in  Rosenfeld  (2019).  Unlike  traditional  dialectological surveys, which work with closed lists of (lexical, phonological, less often: grammatical) features that are independently considered to be relevant for diatopic variation, word embeddings draw on all of the words in the data. This is not strictly a disadvantage. Labov, Ash, and Boberg (2005:7), for instance, point out the problem inherent in fixed sets of elicitation items, which often show 'a heavy concentration of rural and agricultural terms and  other  words  and  phrases  that  are  no  longer  current.  The  stream  of evidence  for  dialect differentiation  therefore  shrinks  over  time  and contributes to the widespread  impression  that  regional dialects are disappearing.'  A  word  embedding  method  does  not  suffer  from  such shrinkage and is arguably able to identify relevant distinctions without a researcher's bias towards certain forms contaminating the analysis (Hovy & Purschke 2018:4383).

On  the  other  hand,  carefully  designed  lists  of  features  retain interpretability that is lost in distributional models. It is not easy to account for the precise patterns of usage that cause the clustering of dialect areas in Figure 3 in general. Neither, and this is perhaps a more fundamental issue with  such  methods,  is  it  clear  to  what  extent  the  models  capitalise  on meaningful  linguistic -compared  to  relatively trivial -referential variation.  To  what  extent  is  the  clustering  driven  by  words  with  local referents, such as sports teams, the names of streets, neighbourhoods, local celebrities,  or  venues?  Traditional  dialectological  methods  are  able  not only  to  identify  dialect  regions  but  also,  by  considering  the  individual features that contribute to them, to come up with linguistic explanations for these regions. Recall, for instance, that the Linguistic Atlas project was able to identify settlement history as a key factor leading to different US dialect regions.  In  distributional  approaches,  post-hoc  analyses  may  uncover similar patterns (Rosenfeld 2019:98-102), but the question of interpretability of the clusters, and the extent to which they are based on meaningful structural variation, largely remains.

In the remainder of this paper, I discuss a method that draws on the same corpus of Twitter messages used in Rosenfeld (2019), but takes as its point  of  departure  a  set  of  pervasive  linguistic  features  established previously to indicate important differences in discourse structure. I show that such a method, while lacking the sophistication and fine granularity of distributional  models,  is  able  to  uncover  dimensions  of  diatopic  and diastratic  variation  in  Texas  English,  and  that  these  dimensions  are amenable  to  linguistic  interpretation.  This  method  is  proposed  not  as  a competitor to distributional approaches such as Rosenfeld (2019), whose statistical sophistication far surpasses my proposal here. Rather, the two approaches usefully complement each other: distributional models excel at uncovering regional structure in the data, whereas the method below has its strengths  in  identifying  fundamental  aspects  of  linguistic  variation  of relevance for such regional structure.

## 4. Data and methods

The corpus for the present analysis is the one used in Rosenfeld (2019). It consists of 3,511,253 individual tweets, each of which is associated with one  of  the  over  8,000  Texas  voting  precincts.  Locating  these  tweets  in geographical  space  is  done  by  precise  latitude-longitude  coordinates, wherever available. In addition, tweets without such information but with an identifiable Texan town in the 'place' field were also included. The coordinates for locating the latter tweets were derived from the town via data  from  simplemaps.org  (Rosenfeld  2019:85).  The  data  has  been  preprocessed  such  that  individual  elements  of  the  tweets  -  primarily  user handles and urls - have been replaced with generic placeholders (<url>, <user>, etc.). Other than the texts of the tweets themselves and their tweet IDs,  no  user-specific  information  is  retained  in  the  corpus.  However, detailed information for each voting precinct, collected from the US Census Bureau,  is  available  as  an  important  source  of  meta-data.  Of  primary importance for the present study are the following by-precinct pieces of information:  the  coordinate  bounds  of  the  district  (required  for  spatial analysis), the percentage of different ethnicities (White, Hispanic, Black), the percentage of people who voted Republican in the most recent election, and the precinct's population density.

The  choice  of  voting  precincts  as  the  socio-geographical  unit  of analysis over alternatives such as counties or voting districts are motivated by  several  factors.  First,  voting  precincts  are  the  smallest  geographical units  for  which  detailed  demographic  information  is  available,  thus providing the highest possible spatial resolution while retaining aggregate social information. Second, as opposed to counties, voting precincts are designed  in  such  a  way  as  to  keep  population  size,  rather  than  area, comparable (Rosenfeld 2019:86). Consequently, they allow for inferences about relatively small and specific demographic groups, even in densely populated areas of the state.

The  data  for  each  precinct  are  treated  as  one  corpus  text.  The resulting corpus of 8,080 texts is subjected to a multi-dimensional analysis (MDA; Biber  1988;  Bohmann  2019).  This  method  measures  frequency profiles for many linguistic features in each corpus text. In contrast to many approaches in computational sociolinguistics, which automatically detect distinctive features based on a large set of candidate items (often: words; e.g.  Louf  et  al.  2023),  MDA  follows  a  different  design.  Each  linguistic feature under consideration is theoretically motivated on the basis of its stylistic  and/or  discourse-structuring properties. As such, the goal is not finding features that maximise a certain task, such as predicting regional differences,  but  putting  individual  texts  into  linguistically  interpretable relations.

MDA rests on the assumption that linguistic  variation  is,  in  large parts, functionally motivated and that, consequently, the behaviour of many different  linguistic  variables  is  subject  to  a  small  set  of  underlying situational properties. To give a concrete example: personal pronouns are found  at  higher  frequencies  in  spontaneous  face-to-face  conversations, which also feature higher-than-average frequencies of private verbs (such as think , believe , etc.) and contractions. All of these features, in turn, are under-represented  in  academic  writing,  where  nominalisations,  passivevoice constructions, and complex noun phrases are highly frequent. Given a corpus of personal conversations and academic articles, an MDA is able to detect these commonalities and express all six features as part of one dimension of variation. In this case, the dimension expresses a difference between  conceptually  oral  and  conceptually  literate  texts  (Koch  & Oesterreicher 1985) and might be labelled involved versus informational production (Biber 1988; Bohmann 2019).

Mathematically, such dimensions are established by subjecting the matrix  of  texts  and  measured  features  to  exploratory  factor  analysis (Thompson  2004;  Gorsuch  2015).  Similar  to principal  components analysis, this method reduces the dimensionality of the data. Whereas the latter takes common as well as item-specific variance into account, factor analysis  only  considers  common  variance,  with  the  aim  of  establishing generalisable latent dimensions. Based on the covariance profiles of the features, factor analysis identifies groups of linguistic features that behave similarly across the corpus, i.e. that have a tendency to be over- or underrepresented in the same texts. In the present study, this means the method finds clusters of functionally related features that occur with greater- or lesser-than-average  frequencies  in  tweets  from  the  same  Texas  voting precincts.

The MDA procedure expresses the relationship between each feature and each dimension of variation by virtue of a measure of correlation, a socalled  structure  coefficient.  Features  with  a  structure  coefficient  whose absolute  value  is  above  a  pre-defined  threshold  are  considered  to  be relevant for a particular dimension. The method also, importantly, scores all corpus texts along each dimension, allowing for an analysis of how the multidimensional space of variation structures the corpus material itself.

## Scandinavian Studies in Language, 14(2), 2023 (73-103)

This  later  part  will  be  central  in  the  present  analysis,  as  relations  in multidimensional  space  can  be  mapped  onto  geographical  space.  The utility of MDA for analysing Twitter discourse has been demonstrated by Clarke and Grieve (2019).

Space limitations prevent any detailed discussion of the individual linguistic  features  extracted  from  the  Texas  English  Twitter  corpus.  I restrict myself to listing the 100 features here (see Table 1) and stating in general  terms  that  they  were  selected  based  on  the  role  they  have  been found  to  play  in  previous  research  on  register  variation,  variation  in computer-mediated  discourse,  or  as  markers  of  Texas/Southern  speech. Only those features that occurred with a frequency > 0 in at least 75% of the corpus texts/precincts under analysis were retained. The less frequent features that did not meet this criterion are enclosed in parentheses in the list below.

| Category                         | Items                                                                                                                                                | Primary role in structuring variation   |
|----------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------|
| Pronominal forms                 | first-person pronouns, second-person pronouns, third-person personal pronouns, pronoun it , indefinite pronouns                                      | General register variation              |
| Modality and stance devices      | possibility modals, prediction modals, (amplifiers, downtoners)                                                                                      | General register variation              |
| Subordination                    | if and unless , although and though , because                                                                                                        | General register variation              |
| Prepositions and adverbials      | all prepositions, time adverbials, place adverbials                                                                                                  | General register variation              |
| Features associated with orality | contractions, ( gotta, gonna, wanna, g - dropping),                                                                                                  | General register variation              |
| Prefixation                      | Prefixes re- , un- , ( anti-, co-, counter-, ex-, inter-, dis-, mis-, under-/over-, pre-, pro-, semi-, sub-, super-, trans-, uni-, with-)            | General register variation              |
| Suffixation                      | Suffixes -ic, -ion, (-able/-ible, -age, -ance, -ant, -ary, -ation, -dom, -ful, -hood, -ial, -ical, -ican, -ify, -ism, -ist, -ity, -ize, -ive, -less, | General register variation              |

## Axel Bohmann

## Scandinavian Studies in Language, 14(2), 2023 (73-103)

Table 1: Measured linguistic features.

|                                                             | -ment, -ness, -ory, -ous, -ship, -tor, -ture)                                                                              |                                     |
|-------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|-------------------------------------|
| Punctuation                                                 | &, !, ?, :, %, *,@ (other than as part of usernames)                                                                       | General register variation /CMD     |
| Twitter-specific discourse conventions and CMDdevices       | hahstags, usernames, ( smh, yolo)                                                                                          | CMD                                 |
| Emojis and emoticons                                        | tears of joy, heart eyes, loudly crying, ok hand, unamused, heart, kiss, smiling face eyes, weary, raising hands, :) / :-) | CMD                                 |
| Features associated with regionally/socially specific usage | fixing to , possessive pronoun + ass, ( yall , holler , th -stopping, word-final t/d deletion )                            | Dialectal and sociolectal variation |
| Reference to specific entities                              | date, money, number, time, url                                                                                             | Variation in discourse topic        |
| Further features                                            | Negator not                                                                                                                | General register variation          |

An important step of the analysis concerns the decision about how many dimensions  to retain, i.e. how  aggressively to reduce the original dimensionality  of  the  data.  This  decision  is  not  a  matter  of  strict mathematical criteria. Theoretical considerations as well as heuristic tools (considering the amount of variance that is still explained while reducing the data to given number of dimensions) play a role in this step. Since all corpus texts in the present study represent one situational variety, Twitter discourse, the register variation found in this data set is likely to be less pronounced  than  in  previous  MDA  research  working  with  diverse  text types. Therefore, and following the heuristic method of a scree plot analysis (Cattell 1966), a four-dimensional solution was selected to represent the variation in the present data set. Together, the four factors account for 45% of the total variance in the measured variables. Factors were established with the principal axes method and rotated to the oblimin criterion, which allows for moderate degrees of inter-factor correlation. The factor scores for individual precincts were calculated via regression.

The  analysis  below  considers  the  variation  along  the  first  two dimensions in the data, which account for 19 and 13% of the total variance in  all  features,  respectively.  Dimensions  3  and  4  are  less  informative, accounting for 7 and 6% of variance, respectively. It should also be noted that the first two dimensions show a significant degree of correlation, with a coefficient of 0.337. They are not to be understood as fully orthogonal, independent  dimensions,  then,  but  as  inter-related  to  a  relatively  large degree.

Each  dimension  is  discussed  separately  below.  First,  the  relevant linguistic features are explained and interpreted in functional terms. Next, the dimension's geographic patterning is explored by constructing a map of Texas in which each voting precinct is color-coded on a gradient that corresponds to its dimension score. This allows for an intuitive understanding  of  the  diatopic  profile  of  each  dimension.  Finally,  the following demographic variables available for each precinct are considered as predictors in a regression model fitted over the data, with the by-precinct dimension  score  as  the  outcome  to  be  predicted:  population  density (representing the degree of urbanisation), the percentage of Hispanic and Black people in the population, and the percentage of the Republican vote. All predictors were logarithmically transformed in order to arrive at more normally distributed values. Additional factors of potential relevance, such as  age  structure  of  median  household  income,  were  unfortunately  not available for this data set. As such, the discussion is restricted to the above variables.

## 5. Results

## 5.1. Dimension 1

The first dimension, accounting for almost half of the total feature variance, shows structure coefficients greater than 0.5 for 25 different features, and no salient negative coefficients. The relevant features and their structure coefficients are listed in Table 2. What is immediately striking in this list is the predominance of different emojis. The top seven items are all of this kind,  and  they  altogether  make  up  ten  of  the  25  features  listed.  Other strategies  drawing  on  individual  orthographic  symbols  -  the  'happy' emoticon :) , asterisks, the ampersand, the at-sign (where it is not part of a username), question marks, and colons - make up a significant portion of the  remaining  fifteen  features.  Only  a  handful  of  lexico-grammatical

## Scandinavian Studies in Language, 14(2), 2023 (73-103)

features  are  associated  strongly  with  the  first  dimension.  These  are subordinators because , if , and unless , place adverbials, prediction modals, and indefinite pronouns as well as the pronoun it . Two suffixation devices (adjectival ic and nominal ion ) round up the list.

Table 2: Salient features for Dimension 1 (structure coefficients in parentheses).

| emoji_loudly_crying (0.69)   | emoji_kiss (0.65)   | emoji_raising_hands (0.64) &    | (0.61)                   | Pronoun it (0.56)          |
|------------------------------|---------------------|---------------------------------|--------------------------|----------------------------|
| emoji_heart_eyes (0.69)      | emoji_weary (0.65)  | - ic (0.64)                     | @ (0.61)                 | ? (0.56)                   |
| emoji_unamused (0.67)        | :)/:-) (0.65)       | - ion (0.64)                    | Place adverbials (0.60)  | Indefinite pronouns (0.56) |
| emoji_tears_of_joy(0.67      | because (0.65)      | emoji_smiling_face_ eyes (0.63) | Prediction modals (0.59) | : (0.54)                   |
| emoji_ok_hand (0.66)         | if/unless (0.64)    | * (0.63)                        | emoji_heart (0.59)       | Money (0.51)               |

The primary functional interpretation for this first dimension is relatively easily established with reference to the typographic elements that predominate the list. Texts scoring highly on this dimension are characterised by a particular kind of multimodal online writing that draws on spelling and pictographic strategies as additional semiotic resources in the construction of a tweet. This focus on semiotic creativity is, however, not to be equated with a generally interpersonal, 'conversational' style. While third person pronouns ( it as  well  as  indefinite  ones) reach salient structure coefficients, this is not true for second- and first-person pronouns, the latter of which actually show a negative structure coefficient (-0.04). Other markers of a colloquial and interpersonal style, such as user mentions and contractions, are also ranked relatively low in terms of their structure coefficients (37 and 40 out of 45, respectively).

How  is  this  creative  style  of  online  writing  associated  with  the geography of Texas? Figure 4 illustrates the answer to this question in the form  of  two  maps.  Each  shows  the  state  of  Texas,  with  every  voting precinct represented as a separate polygon and receiving colour shading based  on  two  properties.  In  the  left  panel  of  Figure  4,  each  precinct  is shaded according to log-transformed population density. Darker shades of brown indicate higher population density, whereas darker shades of blue indicate more sparsely populated precincts. The right panel shows shading Population density Factor analysis dimension 1

## Scandinavian Studies in Language, 14(2), 2023 (73-103)

15.0

12.5

10.0

10

-10

-20

according  to  the  dimension  score  along  the  first  dimension  calculated during  the  MDA,  again  with  darker  browns  indicating  higher-scoring precincts and darker blues indicating particularly low dimension scores. The similarities between the two maps are striking, although the left panel appears  to  include  more  room  for  intermediate  light  zones  not  clearly

Figure 4: Distribution of log population density (left) and Dimension 1 score distribution (right).

<!-- image -->

shaded blue or brown. These represent the wider suburban areas surrounding  the  big  cities  in  East  Texas.  The  right  panel  shows  high dimension  scores  clearly  concentrated  around  the  city  centres.  The northernmost  cluster  of  predominantly  brown  shades  is  the  Dallas-Fort Worth metropolitan area. Going South from there, a string of brown cells extends  along  the  Interstate  35,  with  concentrations  in  Austin  and  San Antonio. Further East, towards the northern end of the Gulf Coast, the city of Houston is also clearly marked in brown.

The importance of urbanity for Dimension 1 is further confirmed by a regression model fitted over the data to predict a precinct's Dimension 1 score  from  its  population  density,  percentage  of  Black,  percentage  of Hispanic inhabitants, and percentage of people who voted Republican. The coefficients  of  the  model  are  shown  in  Figure  5,  created  with  the dotwhisker (Solt & Hu 2018) package in R (R Core Team 2022). The black vertical line is the intercept, which is simply the model's base prediction and of no immediate value for interpreting the coefficients. Each dot is the estimated coefficient for the effect of a predictor variable. Positive values, pop\_density BLACK\_PCT- HISPAN\_PCT- REP\_PCT 1

-0.1

i.e.  dots  located  to  the  right  of  the  intercept,  indicate  an  increase  in predicted  dimension  1  scores  as  the  value  for  the  predictor  variable increases.  Negative  values,  to  the  left  of  the  intercept  line,  indicate  an inverse  relationship  between  increase  in  the  predictor  and  outcome variable.  The  whiskers  extending  horizontally  from  the  coefficient  dots represent 95% uncertainty intervals. If these do not intersect the vertical line, this can be taken as evidence for a predictor's statistical significance in the traditional sense.

Figure 5: Coefficient estimates for a linear regression predicting Dimension 1 score.

<!-- image -->

While all predictors in the model reach significance, it is clearly population density which dominates in terms of effect size. More densely populated areas, such as the urban centres mentioned above, are predicted to show drastically higher values along dimension 1. In terms of ethnicity, higher proportions  of  Black,  but  lower  proportions  of  Hispanic  speakers,  also increase  the  predicted  dimension  scores,  although  with  a  much  smaller effect  size.  Political  leaning  is  the  weakest  predictor  in  the  model,  with higher percentages voting Republican slightly favouring lower dimension 1 scores.

As far as diatopic variation in Texas is concerned, dimension 1 lends support to the diverging paths urban and rural linguistic developments may be taking (Bailey 1991). Of course, Bailey's work initially established such a  divergence  with  respect  to  general  Southern  and  Standard  features, mostly at the level of phonology. The data underlying dimension 1 are of a fundamentally  different  kind:  semiotic  strategies  in  written  computermediated discourse, with no initial relation to any particular dialect areas. As such, reading the results of the present section as direct confirmation of Bailey's (1991) point would be far-fetched. What the results do show very clearly, however, is that the rural-urban divide has important consequences for linguistic variation in the state of Texas. Findings from related research in  computational sociolinguistics also corroborate this importance at the national  level  (Louf  et  al.  2023).  Together,  these  findings  point  to  the importance  of  the categories rural and  urban  in linguistic identity construction,  potentially  replacing  traditional  regional  distinctions  as Bailey  (1991)  observes. This topic deserves  continued  attention in sociolinguistic and dialectological research (e.g. Hinrichs et al. 2013).

## 5.2. Dimension 2

The second dimension comprises 16 features with a structure coefficient greater than 0.5, listed in Table 3. Five of these can also be found in Table 2  above:  indefinite  pronouns  and it ,  question  marks,  colons,  modals  of prediction, and subordinators if and unless .  However, the seven features with the highest structure coefficients along dimension 2 are unique to that dimension. Contractions and second-person pronouns indicate an involved, colloquial style, as do user mentions. Negator not is also associated with characteristics of interpersonal conversation (Bohmann 2019:94-95). The inclusion of additional pronominal forms (first- and third-person personal pronouns, it , indefinite pronouns) speaks both to an interpersonal and a less nominal, abstract style of discourse. The focus on modality, indicated by the high structure coefficients for both possibility and prediction modals, is not easy to interpret in the abstract, as a differentiation between epistemic and deontic uses would be required for a meaningful interpretation. Given the  patterning  of  all  other  features,  it  may  be  speculated  that  it  is predominantly deontic modality, used to indicate rights and obligations in interpersonal communication, that is captured by dimension 2. Finally, the inclusion of exclamation points and question marks in the list attests to a level of involvement captured by the second dimension.

## Scandinavian Studies in Language, 14(2), 2023 (73-103)

Table 3: Salient features for Dimension 2 (structure coefficients in parentheses).

| Contractions (0.76)           | Time adverbials (0.65)       | ! (0.63)                     | : (0.56)                 |
|-------------------------------|------------------------------|------------------------------|--------------------------|
| Second-person pronouns (0.71) | Possibility modals (0.65)    | ? (0.61)                     | Prediction modals (0.54) |
| Negator not (0.67)            | Third-person pronouns (0.65) | Indefinite pronouns (0.59)   | Prefix re - (0.53)       |
| User mentions (0.67)          | Pronoun it (0.65)            | First-person pronouns (0.58) | if/unless (0.51)         |

The  geographic  distribution  of  dimension  2  scores  can,  once  again,  be analysed visually in Figure 6. This time, no second map is included for comparison, since the main distinguishing feature of dimension 2 can be read from the distribution itself. Whereas  Figure 4 showed  clear concentrations of brown cells against a predominantly blue background, i.e. high scores for dimension 1 being clearly concentrated in a limited set of  urban  districts,  Figure  6  shows  a  much  wider  spread  of  brown.  The distribution of high dimension 2 scores is clearly not limited to the city centres; and, in fact, the downtown areas of Houston, Dallas, San Antonio, and Austin all show lighter brown than the surrounding suburbs. What can be seen, as well, is  a  noticeable  East-West  divide,  with  patches  of  blue being  more  common  in  the  West,  whereas  the  East  is  more  uniformly brown. The pattern is far from conclusive, as many deeper shades of brown can  also  be  found  in  West  Texas,  particularly  along  the  Interstate  20, moving West from Dallas. Conversely, there are pockets of blue on the Southern  Gulf  coast  and  close  to  the  border  to  Louisiana  in  the  East. Nonetheless, it is justified to speak of a general East-West split, with East Texas generally scoring higher along the dimension 2 continuum.

Factor analysis dimension 2

Dimension 2

score

-10

-20

-30

Figure 6: Distribution of scores along Dimension 2. Darker shades of brown indicate higher values, dark shades of blue low values.

<!-- image -->

A look at the coefficients from a regression model predicting dimension 2 scores (Figure 7) highlights the interrelatedness of both dimensions. All terms, with one exception, show an effect in the same direction as in Figure 5. Political affiliation is the only coefficient that has switched sides and can now be found considerably to the right of the intercept line. Whereas higher percentages of Republican vote predicted marginally lower dimension 1 scores, they predict quite substantially higher dimension 2 scores, although with a wide margin of uncertainty. I would suggest that population density and political preference need to be considered together in these models. Densely populated urban centres tend to be strongholds of the Democratic party, whereas other areas with relatively high, but not extreme, population density where the political affiliation is more towards Republican represent the smaller towns of East Texas. Figure 8 illustrates this point by showing pop\_density - BLACK\_PCT |

HISPAN\_PCT-

REP\_PCT†

-0.05

0.00

the correlation between each dimension's scores and population density in the data.

0.05

Fioure 7. Chefficient estimates for a linear reoression predictino I

Figure 7: Coefficient estimates for a linear regression predicting Dimension 2 score.

<!-- image -->

As can be seen from Figure 8, both Dimension 1 and Dimension 2 show a general  tendency  towards  higher  scores  with  higher  (log-transformed) population  density.  However,  whereas  the  relationship  is  approximately linear for Dimension 1, this is not the case for Dimension 2. The latter peaks at  logged  population  densities  of  around  11  and  plateaus  there.  For particularly densely populated areas, there is even a slight dip in Dimension 2 scores. This, in combination with the patterns identified in Figure 6 and the coefficient for REP\_PCT in Figure 7, supports the interpretation above, namely: that Dimension 1 is related to urban centres and Dimension 2 to the generally more densely populated eastern part of the state. Interestingly, then,  the  two  dimensions  together  converge  on  the  two  most  important aspects  of  diatopic  variation  identified  for  Texas  in  the  dialectological literature.

Score

-1-

-2

5.0

7.5

10.0

pop\_density

Dimension — Dimension 1

· Correlation hetween nonulation density and scores alono Dimensions 1

Figure 8: Correlation between population density and scores along Dimensions 1 and 2.

<!-- image -->

## 6. Discussion

The analysis presented above was introduced as a complement rather than a competitor to the distributional method used in Rosenfeld (2019). At this point, it is worth returning to the latter and relating the two methods to each other. It is obvious from a comparison of Figure 3 with Figures 4 and 6 that the clean representation of diatopic relations achieved in Rosenfeld (2019) is out of reach for the MDA-based approach taken here. There are at least two reasons for this fact. First, distributional methods can capitalise on a lot more information, namely: each word and its collocational behaviour in each  sub-corpus.  Second,  and  importantly,  the  smoothing  procedure employed by Rosenfeld increases the similarity between voting precincts which are in close geographical proximity to each other. Therefore, the geographical signal is strengthened and made to appear more regular by default.

It  would  have  been  possible,  in  principle,  to  introduce  such  a smoothing procedure in the present study as well, either by updating the frequency information for each feature in each precinct with information from the surrounding precincts or by updating the calculated dimension scores. The reason this was not done lies in the purpose of the analysis itself, which focused on interpretable linguistic patterns rather than clear and plausible regional distinctions. The primary aim of an MDA, in other words, is not to construct maps as shown in Figures 4 and 6, but the kind of feature bundles shown in Tables 2 and 3. When taking these as a starting point, the interpretability of Dimensions 1 and 2 - creative, multimodal writing and an interpersonal, colloquial style, respectively - is obvious and can be taken as a testament to the method's success.

The question, perhaps, is not why Dimensions 1 and 2 show weaker geographic signals, but why they do show such signals at all. The relevant features are not generally considered in dialectological research, and they apply  either  to  the  context  of  CMD  in  particular  or  to  very  general situational variation. The  traditional dialectological variants -often agricultural terms that are exceedingly rare in naturally occurring discourse -  are  not  represented  at  a  sufficient  rate,  even  in  a  multi-million  tweet corpus,  to  play  any  statistical  role.  Phonological  variation,  which  is  of course  much  more  pervasive  and  has  been  the  basis  of  more  recent approaches to American dialectology (Labov et al. 2005), is not directly represented in the written medium of Twitter. While some approaches have been made in the past to combine the perspectives of register and (social) dialectology  (Biber  &  Finegan  1994),  the  cross-fertilisation  has  been minimal. What the present study shows is that register variation indeed has a diatopic (and diastratic) component.

Having established the patterns of regional variation for Dimensions 1 and 2, the task remains to interpret them. Why is it that people in the urban centres draw decisively more actively on the full range of semiotic affordances on Twitter? One explanatory factor may be the age structure of the communities: younger users are expected to be found in these urban centres  at  higher  rates  than  elsewhere,  and  these  may  simply  be  more accustomed to the modal affordances of emoji usage. It is unfortunate that data  for  each  voting  precinct's  age  structure  is  not  available  to  further explore this hypothesis. Another line of interpretation may have to do with visibility as a valuable resource on Twitter (Squires 2015:245). In highly populated areas, more tweets are produced in general, and the competition for  attention  may  foster  the  increasing  use  of  multimodal  and  creative resources to enrich one's discourse.

This line of interpretation may be extended to Dimension 2 as well. As was argued above, it is the populated areas outside the urban centres in particular that show high scores along this dimension. Here, the competition for attention may be less fierce, and predominant modes of sociality  less  anonymous  than  in  the  city  centres.  These  properties  may contribute to a more interpersonal style. Put briefly, tweets from the cities may be situated more towards the 'public' end of the cline between private and public that Twitter on the whole occupies (Bruns & Moe 2014) and share characteristics of advertising language, whereas tweets from the more rural parts of the state (particularly in the East) may be more private in conceptualisation, sharing properties of spoken conversation. One aspect the  present  study  is  not  optimally  suited  to  address  is  precinct-internal variation, especially in the urban areas. The reduction of each precinct's linguistic profile to two-dimension scores is not able to capture the internal heterogeneity of the discourse in each. This element of internal heterogeneity deserves further attention in future research.

Returning to Underwood's (1990:96) scepticism of 'wordcarvers' and their attempts to draw precise linguistic boundaries across the state of Texas,  it  should  be  noted  that  any  such  line  is  indeed  an  arbitrary simplification.  No  actual  border  exists,  obviously,  which  separates  two different yet internally homogenous dialects. The task of the sociolinguist and the dialectologist is not to draw rigid borders but to identify patterns. In  this  regard,  multiple  sources  of  data  can  and  should  be  considered alongside  each  other  without  the  need  to  reduce  all  their  complexity  to simple oppositions. Twitter data have their place in this endeavour, not as a  better,  but  as  an  additional  source  for  studying  diatopic  and  diastratic variation.

## 7. Conclusion

The present paper has addressed the potential of Twitter data to explore aspects of diatopic, as well as diastratic, variation in Texas English. This variation, it was argued, is currently not fully understood in large parts due to an absence of robust empirical data. Twitter discourse was introduced as one potential source of such data and its utility demonstrated with reference to  Rosenfeld's (2019) study of regional variation in Texas English. The methodological contribution the present study sought to make was not a better dialect map  of  Texas,  but  the  identification  of  linguistically interpretable dimensions of variation. To this purpose, a multi-dimensional analysis was run on Rosenfeld's data: over 2 million tweets, mapped to the Texas voting precincts.

The  first two  dimensions  identified in this  procedure  proved interpretable  in  both  linguistic  and  geographic  terms.  Dimension  1 comprised  emojis  and  other  creative  uses  of  typography  to  enhance computer-mediated discourse and was found at particularly high scores in the  urban centres of the state, such as Houston, Dallas-Fort Worth, San Antonio, and Austin. The second dimension showed clear characteristics of interpersonal, oral discourse and was found at higher rates in East Texas than West Texas. Regression models for both dimensions, with demographic information for the voting precincts as predictors, confirmed and refined these interpretations.

It  was  argued  that  MDA  is  a  useful  perspective  to  investigate meaningful diatopic variation,  perhaps  surprisingly  so  since  the  method itself is designed to identify rather general aspects of situational language use.  The  extent  to  which  register  and  diatopic  variation  are  interrelated remains a fruitful area of future research.

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