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THE RESEARCHER AND THE
NEVER-ENDING FIELD:
RECONSIDERING BIG DATA
AND DIGITAL ETHNOGRAPHY
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Christine Lohmeier
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ABSTRACT
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Purpose This chapter considers the challenges and potentials of using
so called big data in communication research. It asks what lessons big
data research can learn from digital ethnography, another method of
gathering digital data.
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Design/methodology/approach The chapter first takes on the task of
clearly defining big data in the context of communication and media
studies. It then moves on to analyse and critique processes associated
with the dealings of big data: datafication and dataism. The challenges
of data-driven research are juxtaposed with qualitative perspectives on
research regarding data gathering and context. These thoughts are
further elaborated in the second part of the chapter where the lessons
learned in digital ethnography are linked to challenges of big data
research.
Big Data? Qualitative Approaches to Digital Research
Studies in Qualitative Methodology, Volume 13, 75 89
Copyright r 2014 by Emerald Group Publishing Limited
All rights of reproduction in any form reserved
ISSN: 1042-3192/doi:10.1108/S1042-319220140000013005
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Findings It is proposed that by including the materialities of contexts
and transitions between material and mediated realms, we can ask more
relevant research questions and gain more insights compared to a purely
data-driven approach.
Practical implications
This chapter encourages researchers to reflect
upon their relations to the object of study and the context in which data
was produced through human/human technical interaction.
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Originality/value This chapter contributes to debates about qualitative
and quantitative research methods in communication and media studies.
Moreover, it proposes that methods which are in the widest sense used in
the never-ending digital field benefit from the mutual consideration of
both qualitative and quantitative approaches.
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Keywords: Digital ethnography; big data; qualitative research;
communication research; material turn
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INTRODUCTION
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Big data is hyping. The possibilities of big data have received a lot of attention by communication scholars. One of the most recent pieces of evidence
for this is the publication of a special issue on big data by the Journal of
Communication, one of the most prominent and well-respected publications
in the field. The magazine Research Trends (Halevi & Moed, 2012, p. 5)
attests to ‘an explosion of publications since 2008’. This chapter considers
how big data is used in communication research. Following an assessment
of what is meant by ‘big data’, it outlines the potentials and challenges of
(communication) research with big data. In a second step, big data as well
as digital ethnography are re-considered from a qualitative research
perspective. Over the past two decades, digital ethnography
another
research method with a strong focus on the digital world and online
activities
has experienced increasing popularity. I propose that
approaches to and with big data can benefit from what has been learned
in developing and refining digital ethnographies.
BIG DATA IN COMMUNICATION RESEARCH
Big data stands at the intersection of technology and social reality. It is a
‘cultural, technological, and scholarly phenomenon’ (boyd & Crawford,
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2011, p. 663). The term is used to refer to a method and an approach to
science and research as well as to large datasets themselves. In the past, big
data has caused some over-excitement and even mythologising, meaning a
‘widespread belief that large data sets offer a higher form of intelligence and
knowledge’ with a previously unachieved ‘aura of truth, objectivity, and
accuracy’ (boyd & Crawford, 2011, p. 663). Parks (2014, p. 355) even calls
what we are witnessing right now a ‘Big Data movement’. As the term suggests, we are talking about ‘big’ data, but there have always been data sets
which
in their time
were considered relatively large, so size ‘alone is
therefore an insufficient descriptor’ (Parks, 2014, p. 355). Even before the
term became fashionable, larger datasets than those which are now referred
to as big data were already available, such as census data (boyd &
Crawford, 2011, p. 663). For communication scholars, the datasets in questions can be ‘large social networks (including online networks such as
Twitter), automated data aggregation and mining, web and mobile analytics, visualization of large datasets, sentiment analysis/opinion mining,
machine learning, natural language processing, and computer-assisted content analysis of very large datasets’ (Parks, 2014, p. 355). In communication
research, the analysis of big data stemming from Twitter is particularly
common at this point in time. This is partly due to the fact that large datasets of tweets are relatively easy to get hold of. Nevertheless, even with
regards to Twitter, researchers are somewhat dependent on the benevolence
of Twitter Inc. and its regulations; the challenge of data availability will be
discussed in more detail below.
CHALLENGES OF DATAFICATION AND DATAISM
Why has big data been given such a prime spot in debates about social
sciences over the past few years? The coming together of technological
developments, that is computers having the capacity to store and carry out
analysis of large datasets, promises new findings
hopefully followed by
new insights that could not be obtained at an earlier stage. At the same
time, big data which is of particular interest to communication scholar is
continuously being generated by people using and ‘feeding’ information
and communication technologies. This process has been coined as ‘datafication’ (Mayer-Schönberger & Cukier, 2013). Data is being generated by
users and being conceived as something worth looking at by (communication) researchers. These developments are indeed exciting as they allow for
new types of research questions.
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A second aspect of ‘datafication’ is linked to the new computational prowess in analysing large datasets. These new capacities allows for the bringing together of multiple ‘datasets of different times, from different places,
or gathered at different times’. Big data has evoked scholars and commentators to refer to what we are experiencing now as a ‘big data revolution’
(Mayer-Schönberger & Cukier, 2013). No doubt, the benefits of big data
analysis might be ground-breaking in some disciplines and possibly lifesaving, for example when it comes to analysing medical data sets.
However, big data is also a continuation of how science, including the
social sciences, has evolved over the past 100 years (boyd & Crawford,
2012; Parks, 2014). As with other technologies and types of information
and data, what was once only accessible to few is now available for more
agents, including ‘scholars, marketers, governmental agencies, educational
institutions, and motivated individuals’ (boyd & Crawford, 2011, p. 664).
The question of how to go about an analysis of large data sets does not
require a trip to the local library: tricks and pitfalls can now be easily found
in blog posts (Bar-Joseph, 2013).
The process of datafication, alongside questions on how to deal with the
big data sets in question, brings several challenges. Anderson’s bold assertion that ‘[w]ith enough data, the numbers speak for themselves’ (2008) has
been widely refuted, even in circles of researcher that are strongly associated with quantitative research. Moreover, if we think about the social
world from an epistemological perspective, ‘data’ is ubiquitous; the (digital)
ethnographer in the field
just like the big data analyst
is surrounded
by data. The challenge then becomes to relate different pieces of data, trace
and confirm patterns and make sense of what was found in the larger
scheme of things. But often the assumption when it comes to large data
sets is that they are (a) intrinsically relevant, (b) holistic and complete in
describing phenomena that can be distinguished from other occurrences
disconnected to or at least not effected by them and (c) clean
meaning
that there are no corrupted data. This type of thinking, the underlying
assumption that all answers are to be found by looking at data alone has
been coined ‘dataism’.
While working towards my PhD, I remember sitting in a doctoral workshop at the University of Glasgow, during which, a senior scholar encouraged us to ‘trust our data’. For me, this meant trusting what I have
observed during times of ethnographic field work, taking seriously field
notes and what research participants had told me in interviews and focus
groups. Interpretations, of course, need thinking, re-thinking, questioning.
As in other areas of life (Turkle, 2011), there is a latent assumption that
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technology can do better than humans, that is technically generated or
mechanically selected data sets are more reliable than those collected by
personally and physically going to a field and gathering data. Dataism is an
expression of the tendency to value technically generated or selected data
higher, to view it as more objective and therefore more reliable, making
theory obsolete. The famous case of correlation between S&P 500 stock
index and butter production in Bangladesh (Leinweber, 2007) demonstrates
that everything data suggests is neither true nor necessarily significant.
As has been shown for example in the case of large datasets gathered
from Twitter, the data received is problematic. First of all, Twitter, like
Facebook, offers very limited archiving capacities (boyd & Crawford,
2012). Consequently, there is a bias towards working with fairly recent
data or data of the immediate past. Secondly, the data sets obtained are
not necessarily complete or selected in a traceable manner. For example, to
gather tweets and feed them into a data set, researchers work with an application programme interface (API). The majority of researchers have access
to about 10 per cent of public tweets. This is due to terms and conditions
set by Twitter Inc. So how are these 10 per cent of all public tweets
selected? ‘It could be that the API pulls a random sample of tweets or that
it pulls the first few thousand tweets per hour or that it only pulls tweets
from a particular segment of the network graph. Without knowing, it is
difficult for researchers to make claims about the quality of the data they
are analysing’ (boyd & Crawford, 2012, p. 669). For many data sets relevant to communication research, the quality and therefore the reliability of
the data is limited and access often depends on the goodwill of companies:
‘[O]nly social media companies have access to really large social data
especially transactional data. An anthropologist working for Facebook or
a sociologist working for Google will have access to data that the rest of
the scholarly community will not’ (Manovich, 2011).
Alongside questions of access and data reliability, it is doubtful that
research questions can always be answered in the best possible manner
purely because of researchers working with a large data set. Java et al.
(2007) found that people’s motivations for using Twitter were the need to
share and seek information as well as to sustain and conserve friendships.
These results were based on the analysis of 1.3 million tweets from 76,177
users. But as Marwick (2014) rightly points out, conducting qualitative
interviews and participant observation with Twitter users, is likely to bring
out a much more refined picture of motivations, human technology interactions, relationships and other issues at stake. The hype about big data
and methods including computational analysis should not mean a turning
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away from small data sets. They hold very valuable insights too (boyd &
Crawford, 2012). More often than not, the true promise of big data
research might become apparent in combining big data research with other,
perhaps especially, with qualitative research methods.
In the case of big data research on tweets, Axel Bruns and colleagues
(Bruns, 2012; Bruns & Burgess, 2012; Bruns, Burgess, Crawford, & Shaw,
2012) have, among others, used big data analyses to map the shape and
dynamics of large networks. While this is extremely useful for our understanding of the workings of large networks, such type of analyses tell us little about the meaning of networks, tweets, platforms in people’s everyday
life. By purposefully taking a small data approach, Stephanson and
Couldry (2014) demonstrate that great insights can be gained on Twitter’s
influence on community and (collective) identity by combining a number of
methods and by analysing a relatively small and context-specific number of
tweets. The aim here is not to praise the virtue of one kind of research
in contrast to the shortcomings of another but to acknowledge that each
and every one method and approach comes with advantages as well as
shortcomings.
Drawing on the work of Florian Znaniecki on ‘the human coefficient’,
Christians and Carey (1989, p. 360) remind us that ‘data always belong to
somebody, that they are constructed in vivo and must be recovered accordingly’. Capturing data in vivo is of course a challenge in and of itself and
it is certainly not essential for every type of research question. However,
Christians and Carey’s (1989) point reminds us of two important aspects of
data: For one, every insight gained through big data analysis gives information about the past. This is not specific to big data all forms of content
analyses do not provide first-hand information on how data was produced
in vivo (e.g. in newsroom, in living rooms, on the go with mobile devices).
However, when it comes to big data because of the sheer amount of users
considered
we know little about individual circumstances in which data
was produced. Answering the question of whether we can use our understanding of the past to predict the future goes beyond the remits of this
contribution. But nevertheless, with only a rudimentary understanding or a
good estimate of what goes on ‘on the ground’ where data originates, the
quality of predictions and even of the analyses are likely to decline.
The second point raised by Christians and Carey (1989) relates back to
dataism. At times there seems to be an unconscious detachment regarding
the origin of data. As social and cultural researchers, we are generally interested in data directly or indirectly generated by humans or through human
technology interaction. Big data research in the field of communication
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makes use of people’s digital footprints or data trails. However, the
question of ownership of these data is highly contentious. The recent
verdict of the European Court of Justice forced Google Inc. to delete
certain information about a Spanish citizen (Travis & Arthur, 2014). In
a similar vein, the ‘right to be forgotten’
a concept originally coined
by Victor Mayer-Schönberger (2009) and taken up by policy makers as
has been diswell as NGOs and civil liberties groups (Rosen, 2012)
cussed widely and, in fact, is a concern to many users. From an ethical
perspective, big data then does not happen in a void. Can we imagine a
scenario where permissions to use tweets have to be sought from each
and every single user in a large data set? For medical records, that is
certainly the case. But access to and power over data is not straightforward. Will we allow companies such as Twitter, Facebook and Google
to negotiate ethical concerns or even to simply ignore them?
The huge promises of big data are therefore accompanied by a number
of serious challenges. The following section will approach challenges big
data poses in light of discussions surrounding digital ethnography and the
aims of qualitative research more generally.
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RECONSIDERING BIG DATA AND DIGITAL
ETHNOGRAPHY FROM A QUALITATIVE
PERSPECTIVE
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From a communication scholar’s perspective, digital ethnography and big
data are both linked to processes of digitisation and mediatisation. We live
with what Couldry (2011) has called a ‘media manifold’ in which the
majority of highly diverse aspects of everyday life are directly or indirectly
mediated (Hepp, 2010; Livingstone, 2009). The dynamic configurations of
mobile and more or less stationary technical devices form part of everyday
life and allow for a ‘connected presence’:
We can now, if we wish, be permanently open (and potentially responsive) to content
from all directions. Many writers see the practice (or even compulsion) of continuous
connectivity as characteristic of the ‘digital native’ generation. […] Keeping all channels
open means permanently orienting oneself to the world beyond one’s private space and
the media that are circulated within it. (Couldry, 2012, p. 55)
Communication devices are either at the centre of our actions and attention or on the periphery. Most significantly though, they are ubiquitous
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(Hand, 2012) and they intersect, influence, form and arrange aspects of our
material world. I will return to this point in greater detail below.
With this in mind, researching media and communication is a highly
complex undertaking and several methods have been developed to adhere to
research questions and capture the needed data. Along interviews, focus
groups, surveys
all methods common to the social sciences more generally, there are some which are more specific to media and communication
research, such as different forms of content analysis and media ethnography. Media ethnography is used to gather data on websites or digital
media more generally. Of course ethnographies as well as large datasets
are possible outside of the digital realm; examples could be large datasets
on television viewing habits in a pre-Internet era and ethnographies of newspaper readers. But it cannot be ignored that both of these approaches to
research
media ethnography and big data analyses
have gained
momentum in the digital era. After introducing digital ethnography in more
detail, the chapter will move on to consider in which ways big data research
might benefit by considering some of the challenges which digital ethnographic researchers have had to face.
Digital ethnography1 is based on the anthropological and sociological
approach of treating a certain space as a field. In traditional anthropology, this was generally speaking a certain locale which the researcher
would travel to and make him or herself ‘at home’
as far as that was
possible
in order to gather data. An exemplary anthropologist was
supposed to ‘go native’, live just like or at least alongside the ‘tribe’ she
was researching and, once substantial amounts of data were gathered,
return home to interpret field notes, recorded conversations and so on.
A pivotal characteristic of this type of research is the close, embodied
and personal relationship between researcher and researched (see Coffey,
1999). Interestingly, and perhaps in contrast to what one might come to
expect, field relations do not end with the researcher leaving the field. A
very common experience of ethnographic work is that the field turns out
to be ‘sticky’ as it stays present on the researcher’s mind much longer
than could be expected. Okely (1994, p. 32) eloquently describes this
process:
[T]he experience of anthropological material is, like fieldwork, a continuing and creative
experience. The research has combined action and contemplation. Scrutiny of the notes
offers both empirical certainty and intuitive reminders. Insights emerge also from the
subconscious and from bodily memories, never penned on paper. […] The author is
not alienated from the experience of participant observation, but draws upon it both
precisely and amorphously for the resolution of the completed text.
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Following this approach, ethnographic research consists of a mix of
methods, including interviews as well as informal chats with people encountered in the field, focus groups and (participant) observation. While the
individual methods employed might vary strongly depending on the field
and the research questions, the main commonality of ethnographic studies
is that the researcher makes a conscious effort of understanding the field
and the people he or she researches from their perspective. In an ideal scenario, the researcher simultaneously manages to keep a certain level of
objectivity and a critical capacity of what he or she encounters which is not
easy as field relations quickly become complex and multi-dimensional
(Lohmeier, 2014).
Among others, Christine Hine must be acknowledged as one of the pioneers of media or virtual ethnography. Like ‘regular’ ethnography, media
ethnography is a mix of method (see for example Hine, 2000) which has
gained ever higher levels popularity in communication research. The opportunity to examine communities and interactions in social information and
communication technologies (SICT) has led to a steep increase on studies
focusing on communication practices online.
In traditional ethnographies, scholars distinguish between emic and etic
approaches to the field. While the former indicates that the researcher is
part of the community he or she investigates, the latter implies that the
researcher is in fact an intruder who has not been socialised in the context
s/he now examines. Both types of field relations have advantages and disadvantages. An emic researcher, for example a person researching the community he or she has been brought up in, might be highly familiar with
certain behavioural patterns and structures encouraging or hindering certain actions. In this case, the researcher will need a lot less time of familiarising himself or herself with the field and with what is at stake. Then again,
the fact of belonging somewhere and being seen as ‘one of us’ in the widest
sense by research participants, might also have certain disadvantages. If,
the field in question is highly polarised, research participants are likely to
assign the researcher to a ‘side’. Whether this is justified or not, is another
matter.
Imagine a research project on the memories of the Troubles in Northern
Ireland. Clearly, an etic researcher, who in an ideal scenario even comes
from outside of the United Kingdom and Ireland, might have more success
of building rapport with informants than someone who is perceived as
biased right from the start. On the other hand, there might be complexities
and intricacies of the field that the etic researcher might completely miss
out on because certain phenomena which are relevant in this particular field
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are not familiar from her own background. Similarly, there might be
prejudices among informants about a researcher coming from a different
background. So both ways of doing research, emic and etic, have benefits
as well as drawbacks. But whether one or the other, good research ends
with insights, understanding and in all likelihood more questions to answer
and follow up on. The term ‘understanding’ is often linked back to qualitative or ‘soft’ science. However, as Wax (1971, pp. 10 11) points out,
understanding is not meant in the sense of empathy:
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Understanding does not refer to a mysterious empathy between human beings. Nor
does it refer to an intuitive or rationalistic ascription of motivations. Instead, it is a
social phenomenon
a phenomenon of shared meanings. Thus a fieldworker who
approaches a strange people soon perceives that this people are saying and doing things
which they understand but he does not understand. One of the strangers may make a
particular gesture, whereupon all the other strangers laugh. They share in the understanding of what the gesture means, but the fieldworker does not. When he does share
it, he begins to ‘understand’. He possesses a part of the insider’s view.
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The distinction between emic and etic field relations forms part of practising reflexivity. In ethnographic work, this conscious reflection of field
relations and potential blind spots and biases is clearly encouraged. In
the case of digital ethnographies, it is not common to make explicit one’s
relationship to the subject of study.
But what could be gained by doing so, by reflecting on the researcher’s
relation the subject? What is striking when considering digital ethnography
as well as big data, is the prominence of data in our relating to it. But
would it not make sense to also consider how we relate to this data at the
start and throughout the research process? This is not meant to encourage
a normative stance in researchers, labelling something as good or bad.
What I’m aiming for here is a subjective perspective of the data analysed. If
we stick with an analysis of tweets, short messages published through
Twitter as described above, does it make a difference if the researcher uses
Twitter himself or herself ? Does it matter if he enjoys using it or not?
Obviously, for crunching numbers in quantitative analyses, this might not
matter so much as the actual calculation seems fairly standardised. But just
like in a digital ethnography, the researchers’ insights about the way
Twitter can be used and put to use for individuals, has an influence on the
sort of research questions she might ask.
A bit more than a decade ago, Marc Prensky (2001) coined the concept
of ‘digital natives’ and ‘digital immigrants’. In communication research, the
distinction of those having grown up with digital technologies and gadgets
as opposed to those who have learned how to live with these technologies
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at a later point in life has been useful. When considering digital data, be it
in the form of digital ethnography or big data, the distinction where a
researcher stands could be useful too. Drawing on the work of Lash and
Lunenfeld, Beneito-Montagut (2011, p. 720) emphasises the following with
regard to (digital) ethnography in today’s world:
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Ethnography in this dynamic arena eventually necessitates a ‘technologized’ researcher
(Lash, 2002; Lunenfeld, 2000). Moreover, paradoxically, in order to achieve reflexive,
critical, precise descriptions of internet phenomena we need both to ‘speed-up’ to follow
our fast-moving objects of analysis and to ‘slow down’ to understand them properly.
[…][Some studies] are more concerned with the features of the technology than with the
forms and meaning of social interaction online.
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The danger is indeed that a focus on technologies and data becomes an
end in itself. As researchers we are at times so enthralled with the wealth of
digital data and what could possibly be done with it, that there is a danger
is to forget what the most pressing research questions are. Moreover, being
critical and reflective of a researcher’s relation to technology can be highly
useful. Returning to the case of Twitter as an example, boyd and Crawford
(2012, p. 669) remind us that, for one, Twitter does not ‘represent “all
people”’ and it is wrong to assume that ‘“people” and “Twitter users” are
synonymous’ as some users might have multiple accounts
and some
accounts have multiple users. In addition, some accounts are so-called
‘bots’ which ‘produce automated content without directly involving a person’. Some ‘users’ might never establish a Twitter account but ‘listen in’ via
the web (Crawford, 2009). What do definitions of ‘user’, ‘participation’ and
‘active’ mean in this context? Understanding the technical side of Twitter
and its affordances, that is how this technology is and can be used, is absolutely essential when considering the results that come out of big data analyses. This background information is not only highly useful but also
essential in making sense of the results.
A second challenge digital research has to face is a re-focusing on contexts. In what has become known as the material turn, researchers are
encouraged to pay attention to how objects and the physicality as well as
different spaces of life interact with what was originally called the virtual
life. What we are experiencing are two simultaneous but highly related
developments; for one, there is the increasing mediatisation of everyday
life; it seems that for some individuals, all areas of life are mediated and
life without media seems unthinkable. Secondly, the material turn in the
humanities reminds us that despite digitisation and the mediatisation of
everyday life, objects and the physicality of what surrounds us is still highly
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significant and should not be neglected in our conceptualisations. The challenge of course lies in creating research methods, which capture online and
offline life and their intersections.
Studies relying solely or to a great extent on big data or digital ethnography alone, run the risk of being disconnected from social reality. In
other words, according to proponents of the material turn, these kinds of
studies tell us only about a very limited interaction which research subjects
engage with in their everyday life. What it takes, is a multi-sited and userfocused way of research, that does not hold data and thereby datafication
in a more esteemed sense than social reality. In the case of digital ethnography, Beneito-Montagut and others (2011, p. 730; see also Christine
Hine on the University of Surrey Youtube Channel, 2013) argue for what
Beneito-Montagut calls an extended or ‘expanded ethnography’ which
goes beyond looking at single-media use and even viewing the digital
world as a field in itself:
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[A]n extended ethnography is multi-situated, user centred, flexible and multimedia. It
requires highlighting again that the strength of expanded ethnography lies in its capacity to analyse in-depth complex interactions, avoiding artificial divisions of linked
social phenomena and problems for their analysis. Meanwhile, it needs to be considered
that such a user-centered approach requires a clear ethic guideline.
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Following this criticism and the re-focusing of communication research
in digital times, we need theories and research methods which place people
and their social practices at the heart of research activities. In times of
digital/big data, online and offline spaces overlap to such a great extent
and they are so vastly interdependent, that the next big challenge is for
research to develop methodologies which allow us to capture these realities:
Social practices change as digital spaces become embedded in a culture. People may feel
anxious if a smart phone is lost or an internet connection gets disrupted, and making a
New Year’s resolution or celebrating Lent may involve forgoing access to electronic
devices. (Hallett & Barber, 2014, p. 310)
The challenge for digital ethnography has been to move away from the
one-dimensionality of data. For convenience sake, online activity has often
been viewed as an isolated action. Online ethnographies of one particular
site are still a legitimate way of gathering data and
depending on the
research question they can indeed bring new insights. However, there is
also a strong calling to not view certain media practices as isolated events
but see them in the context of a wider media ecology (Hoskins &
O’Loughlin, 2010) in which individuals use, read, consume, produce, contribute, collect, share, comment, like, link, create and so on, and in which
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collectives come together, grow, decline and disintegrate over the space of
time. Even with the promises of big data analyses, the challenges will be
similar to the ones that digital ethnographers have to address and are still
in the process of solving.
CONCLUSION
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After an overview of big data use in communication research, this chapter
addressed some of the myths and thinking surrounding big data. The
criticism of processes coined ‘dataism’ and ‘datafication’ is a reminder to
refocus and to not get carried away by the sheer availability of relatively
large data sets. The never-ending field to be found by the (digital)
researcher does not make all data and the results they yield relevant or
every sample desirable for analysis. The challenge remains to find methodologies that capture, record and analyse the complexities of media
practice as opposed to reducing them.
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1. Depending on the time and context of writing, the term used might also be
‘virtual’ or ‘media’ ethnography.
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