Towards Visual Sociolinguistic Network Analysis

Kostiantyn Kucher

1,2 a

, Masoud Fatemi

2,3 b

and Mikko Laitinen

2,4 c

Department of Computer Science and Media Technology, Linnaeus University, V

axj

o, Sweden

Center for Data Intensive Sciences and Applications, Linnaeus University, V

axj

o, Sweden

School of Computing, University of Eastern Finland, Kuopio/Joensuu, Finland

School of Humanities/English, University of Eastern Finland, Kuopio/Joensuu, Finland

Keywords:

Social Networks, Social Media, Variationist Sociolinguistics, Social Network Analysis, Network

Visualization, Text Visualization, Visual Analytics, Information Visualization.

Abstract:

Investigation of social networks formed by individuals in various contexts provides numerous interesting and

important challenges for researchers and practitioners in multiple disciplines. Within the ﬁeld of variationist

sociolinguistics, social networks are analyzed in order to reveal the patterns of language variation and change

while taking the social, cultural, and geographical aspects into account. In this ﬁeld, traditional approaches

usually focusing on small, manually collected data sets can be complemented with computational methods

and large digital data sets extracted from online social network and social media sources. However, increasing

data size does not immediately lead to the qualitative improvement in the understanding of such data. In

this position paper, we propose to address this issue by a joint effort combining variationist sociolinguistics

and computational network analyses with information visualization and visual analytics. In order to lay the

foundation for this interdisciplinary collaboration, we analyse the previous relevant work and discuss the

challenges related to operationalization, processing, and exploration of such social networks and associated

data. As the result, we propose a roadmap towards realization of visual sociolinguistic network analysis.

1 INTRODUCTION

The term social network gained immense popularity

during 2000s due to the emergence of Web 2.0 ser-

vices (Furht, 2010), which allowed users to explic-

itly denote their relations to other users and explore

the relations between other users as well as the digital

content created by them (thus leading to social media

services). However, research on such relations exist-

ing between individuals had already been conducted

within sociology for decades by that point (Gra-

novetter, 1973; Scott, 1988; Scott and Carrington,

2011). Analysis of social networks has also provided

a useful tool—and corresponding challenges—to re-

searchers in linguistics. More speciﬁcally, networks

have been studied within variationist sociolinguis-

tics (Milroy and Milroy, 1985; Labov, 2001; Cham-

bers and Schilling, 2013; Laitinen, 2020) as part of

the inquiry into the evolution of languages, their use,

and variation among individuals, groups, and popula-

https://orcid.org/0000-0002-1907-7820

https://orcid.org/0000-0002-3000-0381

https://orcid.org/0000-0003-3123-6932

tions (Milroy, 1980; Milroy, 1992; Marshall, 2004).

The main ﬁnding in this ﬁeld is that social networks

inﬂuence how innovations diffuse into communities.

On the one hand, if people are linked with dense and

multiplex ties, their networks are close knit, and such

structures tend to resist change. On the other hand,

network ties can be weak, in which case individuals

are predominantly linked through occasional and in-

signiﬁcant ties, and the network is loose knit. Em-

pirical evidence shows that loose-knit networks pro-

mote innovation diffusion. This somewhat counter-

intuitive observation builds on the idea that loose-

knit networks consist of people who are on the so-

cial fringes, which means that the cost of adopting an

innovation is low. Adopting an innovation is socially

risky, and people do not want to risk their social stand-

ing in close-knit social structures (Granovetter, 1973).

However, the traditional data sets used for social

network analysis within variationist sociolinguistics

were typically limited to manually collected observa-

tions and questionnaires with less than 50–70 indi-

viduals (Milroy, 1992; Marshall, 2004). Ample ev-

idence from social anthropology suggests that aver-

248

Kucher, K., Fatemi, M. and Laitinen, M.

Towards Visual Sociolinguistic Network Analysis.

DOI: 10.5220/0010328202480255

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP, pages

248-255

ISBN: 978-989-758-488-6

Language

Users

Traditional, Manual

Data Collection

Approaches

Small-Scale

Data Sets

Knowledge of

Language Use,

Variation, and

Change

Computational

Methods

Sociolinguistics

Experts

Opportunity for

Visual Sociolinguistic Network Analysis

Digital Data

Collection

Approaches

Small-Scale

Sociolinguistic

Networks

Large-Scale

Sociolinguistic

Networks

Large-Scale

Data Sets

Effective Means of

Analyzing and Making

Sense of the Data

Traditional analyses possible for small-scale data sets,

but problematic for larger ones!

Geospatial Tem po ra l MultivariateTextual

Data

Aspects:

Figure 1: An overview of the concepts and challenges associated with visual sociolinguistic network analysis. While the

traditional, manual investigation methods can be sufﬁcient for smaller data sets, they do not scale up to the large and complex

networks extracted from digital data sources such as social media. In order to make sense of such network data and the

associated metadata, an interdisciplinary approach combining linguistic, computational, and visual perspectives is necessary.

age network size, at least in Western societies, tends

to be over 150 nodes (McCarty et al., 2001). What

is more, the emergence of online social network and

social media services has provided the researchers

in this community—as well as the social sciences

and the humanities in general (Sch

och, 2013)—with

an opportunity to expand the scope of their analyses

to much larger data sets, potentially with additional,

rich multivariate information associated with the net-

works (Hale, 2014; Kim et al., 2014). Such analy-

ses typically rely on computational methods devel-

oped within sociology, computer science, and compu-

tational sociolinguistics (Jurafsky and Martin, 2009;

Newman, 2010; Nguyen et al., 2016), as demon-

strated by the recent work (Laitinen et al., 2020).

The enthusiasm for the opportunities promised by

big data is sometimes met with a more pragmatic po-

sition that stresses the importance of smart data in-

stead (Sch

och, 2013). The large scale and the ability

to process data sets with high speed are not always

sufﬁcient on their own to help the researchers under-

stand the data better, gain useful insights, or formulate

further hypotheses, thus highlighting the importance

of interactive visual analyses.

In this position paper, we propose to look at the

challenge of analyzing social networks for the tasks

of variationist sociolinguistics from the point of view

of information visualization and visual analytics. By

combining the sociolinguistic, computational, and vi-

sual perspectives on social networks and the associ-

ated multivariate data (which can include geospatial,

temporal, and text attributes), we aim to lay the foun-

dation for visual sociolinguistic network analysis (see

Figure 1) and raise the awareness of both visualiza-

tion and sociolinguistic communities.

2 RELATED WORK

In order to understand the challenges and opportuni-

ties for our research problem, in this section, we dis-

cuss the related work on social network analysis and

visualization of network and text data.

2.1 Social Networks in Sociolinguistics

Social networks are studied in variationist soci-

olinguistics in the context of language use and

change (Milroy and Llamas, 2013; Dodsworth and

Benton, 2020). By investigating the structure and

the patterns of interaction between the individuals and

groups in such networks, researchers in sociolinguis-

tics are able to understand how the information prop-

agates, and how it affects the language used by in-

dividuals. One of such interaction aspects is related

to strong vs weak ties between the members of so-

cial networks (Granovetter, 1973), which might lead

either to suppression or facilitation of innovative lan-

guage use in the respective communities. Further as-

pects taken into account can include latent (long-term

and stable) vs emergent (swiftly evolving and rene-

gotiable) networks, coalitions (situational dense clus-

ters), and communities of practice (formed on the ba-

sis of certain group activity) (Bergs, 2006), and so on.

In the past decades, the availability of computa-

tional methods and digital data from online services

has made it possible to investigate not only small-

scale networks (including ego networks), but also

large simulated networks (Fagyal et al., 2010) and

networks extracted from social media data (Grand-

jean, 2016; Laitinen and Lundberg, 2020; Laitinen

et al., 2020). In order to make sense of such larger

networks, both computational and visual methods are

necessary, which are discussed next.

Towards Visual Sociolinguistic Network Analysis

249

2.2 Computational and Exploratory

Network Analysis

Social networks can be viewed from the more general

perspective of network analyses and applied graph

theory in computer science (Brandes and Erlebach,

2005; Newman, 2010), with the focus on the topol-

ogy, structures, and important elements existing in

such networks. The last of these tasks can be achieved

through the analysis of network centralities for the

nodes (such as betweenness or closeness), for in-

stance. In general, multiple network analysis methods

have been proposed and applied for social network

analyses (Scott, 1988; Aggarwal, 2011; Scott and

Carrington, 2011) and relevant aspects of social me-

dia and literary data analyses (Agarwal et al., 2012;

Pitas, 2016). We should also mention the multilayer

network approach (Kivel

a et al., 2014), which pro-

vides a promising uniﬁed framework for modeling,

analyzing, and representing complex networks.

From the practical point of view, the tools and li-

braries available for computational analyses of social

networks typically include support for multiple tasks,

including centrality analysis, community detection,

and so on; here, we could list graph-tool (Peixoto,

2014), SNAP (Leskovec and Sosi

c, 2016), and scikit-

network (Bonald et al., 2020) as several examples.

Some of the existing tools and libraries also provide

at least some capabilities for visualization and ex-

ploratory analysis (Brath and Jonker, 2015); here, we

could mention the JUNG library (O’Madadhain et al.,

2005) and the tools such as Pajek (Batagelj and Mrvar,

2004; de Nooy et al., 2018), EgoNet (McCarty et al.,

2007), Gephi (Bastian et al., 2009), NetMiner (Ghim

et al., 2014), and NodeXL (Hagberg et al., 2008;

Hansen et al., 2011). Some of the approaches devel-

oped for network analyses in other domains (e.g., bio-

logical network data) have also been successfully ap-

plied to social networks, for instance, Zhou et al. de-

scribe the application of Cytoscape for social network

data analyses as part of the VAST challenge (Zhou

et al., 2009). The recent approaches also provide

support for computational analyses and visualization

of multilayer networks, e.g., MuxViz (De Domenico

et al., 2015) and Py3plex (

Skrlj et al., 2019). Further

contributions on representation and interactive anal-

ysis of graphs and networks that originate from the

visualization research community are discussed next.

2.3 Network Data Visualization

The recent research efforts in the ﬁelds of graph draw-

ing and network visualization cover a number of im-

portant tasks for representing and interacting with

multivariate networks (Kerren et al., 2014; Nobre

et al., 2019), temporal and dynamic graphs (Beck

et al., 2014; Kerracher et al., 2015; Beck et al., 2017),

group structures (Vehlow et al., 2015), and large-

scale graphs (von Landesberger et al., 2011). Several

frameworks encompassing multiple aspects of com-

plex real-world networks have been proposed as well,

including multi-faceted graph visualization (Had-

lak et al., 2015) and multilayer network visualiza-

tion (McGee et al., 2019).

Some of the tasks even more closely related

to social networks in sociolinguistics have also

been addressed to some extent by the existing ap-

proaches, for instance, visualization of small world

networks (Auber et al., 2003; van Ham and van Wijk,

2004) and visual analysis of centralities (Correa et al.,

2012; Kerren et al., 2012; Zimmer et al., 2012).

Visualization (Vi

egas and Donath, 2004) and vi-

sual analysis (Zhao and Tung, 2012) of social net-

works in particular has been in the focus of some

previous works, e.g., NodeTrix (Henry et al., 2007)

and NodeXL (Bonsignore et al., 2009). Several sur-

veys provide further overview of the approaches ex-

isting in this ﬁeld (Du et al., 2015; Correa, 2017).

Finally, we should note that the interest for the chal-

lenges of social network visualization exists from the

perspective of social media visual analytics (Wu et al.,

2016; Chen et al., 2017), network visualization for the

humanities (B

orner et al., 2019), and visual analysis

of multilayer networks across various disciplines and

domains (Kivel

a et al., 2019).

2.4 Text Data Visualization

While the approaches discussed above focus mainly

on the network data relevant for sociolinguistics, we

should not forget the importance of tasks of visual

representation and interaction with language, speech,

and text data for this research ﬁeld. The existing

text visualization and visual text analysis techniques

have been covered by several existing surveys (Alen-

car et al., 2012; Kucher and Kerren, 2015; Liu et al.,

2019; Alharbi and Laramee, 2019), including the

surveys focusing on the more speciﬁc problems of

topic (Dou and Liu, 2016) or sentiment (Kucher et al.,

2018) visualization, or visual analysis of texts for the

digital humanities (J

anicke et al., 2015; J

anicke et al.,

2017). El-Assady et al. describe their work on a com-

plete software platform that can be used for linguistic

analyses (El-Assady et al., 2019); and Hammarstr

et al. make use of visualization in their work that

does not focus on text data per se, but rather the in-

formation about the statuses of languages around the

world (Hammarstr

om et al., 2018).

IVAPP 2021 - 12th International Conference on Information Visualization Theory and Applications

250

As previously mentioned, the information about

social networks can also be extracted from rich so-

cial media data, which also has been in the focus of

multiple visual analytic approaches (Wu et al., 2016;

Chen et al., 2017). Several examples of such systems

that make use of both network and text data include

Whisper (Cao et al., 2012) and Veriﬁ2 (Karduni et al.,

2019), among others; and one recent example that fo-

cuses on the language use trends on Twitter is Story-

wrangler (Alshaabi et al., 2020), for instance.

3 ROADMAP FOR VISUAL

SOCIOLINGUISTIC NETWORK

ANALYSIS

Based on (1) the prior experiences of researchers in

sociolinguistics and computational network analysis

and (2) the analysis of the state of the art in InfoVis

and visual analytics, we can now propose the roadmap

towards realization of visual sociolinguistic network

analysis as an interdisciplinary research effort:

Find the Common Ground. In order to establish

successful interdisciplinary collaboration, it is impor-

tant to be aware of the gaps existing between the

disciplines and domains (van Wijk, 2006), and to

align the goals set by the members of such a col-

laboration (Kirby and Meyer, 2013). Besides our

own prior experiences (Laitinen et al., 2017; Mar-

tins et al., 2017; Kucher et al., 2018; Laitinen et al.,

2018; Kucher et al., 2020; Simaki et al., 2020), we

could also rely on the discussions of previous col-

laborations between the experts in visualization and

the digital humanities (J

anicke, 2016; Hinrichs et al.,

2017; Bradley et al., 2018). When designing applica-

tions and tools as part of such collaboration, it is also

important to consider the gaps between the designers’

and the end-users’ expectations and preferences: in

many cases, “simple is good” (Russell, 2016).

Establish the Design Process. The process for dis-

cussing the requirements of domain experts and de-

signing solutions proposed by the visualization re-

searchers can be structured according to one of the

models proposed in visualization, for instance, Mun-

zner’s nested model for visualization design and eval-

uation (Munzner, 2009). Since the end goal is not

just to design a novel visual representation for net-

work data, but rather to contribute to the efforts by

variationist sociolinguistics experts in making sense

of complex data from digital sources (Laitinen et al.,

2017; Laitinen et al., 2018; Laitinen et al., 2020), the

models and workﬂows discussed in visual analytics

must be taken into account, too (Sacha et al., 2014;

Andrienko et al., 2018). The visualization design

process can also make use of the categorizations of

user tasks discussed in literature (Shneiderman, 1996;

Brehmer and Munzner, 2013).

Address the Speciﬁc Visual Analysis Challenges.

Based on the discussion above, we expect that at least

the following challenges will have to be tackled in the

context of visual analysis of sociolinguistic social net-

works and the associated data:

• Representation and interaction with multiple (and

possibly numerous!) networks, subnetworks, and

network elements (Wang Baldonado et al., 2000;

Roberts, 2007);

• Comparison of such networks and network ele-

ments (Gleicher et al., 2011), including the com-

parison driven by the centrality analyses and de-

tected group structures (Vehlow et al., 2015);

• Facilitation of the complete visual analytic pro-

cess for the users (i.e., experts in sociolinguistics),

including the tasks of provenance, guidance, and

externalization of generated knowledge (Sacha

et al., 2014; Andrienko et al., 2018); and

• Integrating the computational and visual analy-

ses of the network data with the corresponding

analyses of the associated (meta-)data, which can

potentially involve textual (Kucher and Kerren,

2015; J

anicke et al., 2015; J

anicke et al., 2017;

Kucher et al., 2018), temporal or dynamic (Cot-

tam et al., 2012; Beck et al., 2014; Kerracher

et al., 2015; Beck et al., 2017), as well as geospa-

tial (Dykes et al., 2005) aspects.

This list is not conclusive, of course, and we expect

further challenges to be identiﬁed in the future.

Evaluate the Resulting Approaches. Besides the

challenges of designing and implementing visual an-

alytic solutions for the tasks discussed above, evalua-

tion of such visual analytic solutions is a major chal-

lenge on its own (Isenberg et al., 2013). Here, we

can use the body of knowledge focusing on task-based

user studies and evaluation of visualization and inter-

action techniques (Purchase, 2012); but also the ap-

proaches related to critical discussion (Kosara et al.,

2008), reﬂection (Meyer and Dykes, 2018), expert re-

views (Tory and M

oller, 2005), questionnaires (Wall

et al., 2019), and even crowdsourcing (Archambault

et al., 2017) for evaluating InfoVis and visual analytic

approaches. Additionally, previous work focusing on

design and application studies is also available (Sedl-

mair et al., 2012; Weber et al., 2017).

Towards Visual Sociolinguistic Network Analysis

251

Raise Awareness within the Sociolinguistic Com-

munity. The last—but deﬁnitely not the least

important!—part of this roadmap is to use the exist-

ing examples, intermediate results, and applications

of new approaches to raise awareness about the value

and applicability of visualization methods (Fekete

et al., 2008) within the sociolinguistic research com-

munity. By nourishing such interdisciplinary collab-

orations (Hinrichs et al., 2017; Bradley et al., 2018),

all of the participants can gain new knowledge and

progress towards novel, important contributions.

4 CONCLUSIONS

In this position paper, we have discussed the problem

of visual analysis of social networks for variationist

sociolinguistics. Given the range and the complex-

ity of theoretical and practical challenges for making

sense and applying the knowledge about such net-

works and all of the associated data, including tex-

tual, temporal, geospatial, and other aspects, we ar-

gue that an interdisciplinary approach is required to

tackle this research problem, and that visual analytics

should be an integral part of that approach. As our fu-

ture work, we intend to proceed with realization of the

steps listed as our roadmap towards visual sociolin-

guistic network analysis, and we hope that both so-

ciolinguistic and visualization communities become

aware and involved in the work on this interesting and

important problem, which can lead to both theoretical

ﬁndings and practical applications in the future.

ACKNOWLEDGEMENTS

This research has been partially supported by the

funding of the Center for Data Intensive Sciences and

Applications (DISA) at Linnaeus University. We are

extremely grateful for this support.

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