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The Cultural Environment: Measuring Culture With Big Data



The rise of the Internet, social media, and digitized historical archives has produced a colossal amount of text-based data in recent years. While computer scientists have produced powerful new tools for automated analyses of such "big data," they lack the theoretical direction necessary to extract meaning from them. Meanwhile, cultural sociologists have produced sophisticated theories of the social origins of meaning, but lack the methodological capacity to explore them beyond micro-levels of analysis. I propose a synthesis of these two fields that adjoins conventional qualitative methods and new techniques for automated analysis of large amounts of text in iterative fashion. First, I explain how automated text extraction methods may be used to map the contours of cultural environments. Second, I discuss the potential of automated text-classification methods to classify different types of culture such as frames, schema, or symbolic boundaries. Finally, I explain how these new tools can be combined with conventional qualitative methods to trace the evolution of such cultural elements over time. While my assessment of the integration of big data and cultural sociology is optimistic, my conclusion highlights several challenges in implementing this agenda. These include a lack of information about the social context in which texts are produced, the construction of reliable coding schemes that can be automated algorithmically, and the relatively high entry costs for cultural sociologists who wish to develop the technical expertise currently necessary to work with big data.
The cultural environment: measuring culture with big
Christopher A. Bail
#Springer Science+Business Media Dordrecht 2014
Abstract The rise of the Internet, social media, and digitized historical archives has
produced a colossal amount of text-based data in recent years. While computer scientists
have produced powerful new tools for automated analyses of such big data,they lack the
theoretical direction necessary to extract meaning from them. Meanwhile, cultural sociolo-
gists have produced sophisticated theories of the social origins of meaning, but lack the
methodological capacity to explore them beyond micro-levels of analysis. I propose a
synthesis of these two fields that adjoins conventional qualitative methods and new
techniques for automated analysis of large amounts of text in iterative fashion. First, I
explain how automated text extraction methods may be used to map the contours of cultural
environments. Second, I discuss the potential of automated text-classification methods to
classify different types of culture such as frames, schema, or symbolic boundaries. Finally, I
explain how these new tools can be combined with conventional qualitative methods to trace
the evolution of such cultural elements over time. While my assessment of the integration of
big data and cultural sociology is optimistic, my conclusion highlights several challenges in
implementing this agenda. These include a lack of information about the social context in
which texts are produced, the construction of reliable coding schemes that can be automated
algorithmically, and the relatively high entry costs for cultural sociologists who wish to
develop the technical expertise currently necessary to work with big data.
Keywords Culture .Content analysis .Mixed-methods .Evolutionary theory
More data were accumulated in 2002 than all previous years of human history
By 2011, the amount of data collected prior to 2002 was being collected
every 2 days.
This dramatic growth in data spans nearly every part of our livesfrom
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DOI 10.1007/s11186-014-9216-5
International Data Corporation, The 2011 Digital Universe Study: Extracting Value from Chaos,June,
2011. See also Christopher R. Johnson, How Big is Big Data?Lecture at the University of Michigans
Cyber-Infrastructure Conference, November 7th, 2012.
C. A. Bail (*)
University of North Carolina at Chapel Hill, 225 Hamilton Hall, Chapel Hill, NC 27599, USA
gene sequencing to consumer behavior.
While most of these data are binary or
quantitative, text-based data are also being accumulated on an unprecedented scale. In
an era of social science research plagued by declining survey response rates and concerns
about the generalizability of qualitative research, these data hold considerable potential
(Golder and Macy 2011;King2011; Lazer et al. 2009). Yet social scientistsand cultural
sociologists in particularhave largely ignored the promise of so-called big data.
Instead, cultural sociologists have left this wellspring of information about the arguments,
worldviews, or values of hundreds of millions of people from Internet sites and other
digitized texts to computer scientists who possess the technological expertise to extract
and manage such data but lack the theoretical direction to interpret their meaning.
The most obvious explosion in text-based data coincided with the rise of the
Internet. Between 1995 and 2008 the number of websites expanded by a factor of
more than 66 million, recently surpassing 1 trillion.
Although sociologists were
understandably concerned about digital divides in years past, these inequalities appear
to be steadily decreasing (DiMaggio and Bonikowski 2008;Dimaggioetal.2001).
According to a 2012 survey, roughly half of all Americans visit a social media site such
as Facebook or Twitter each day, producing billions of lines of text in so doing.
trends are markedly higher among younger people, suggesting these trends may only
continue to grow over time.
Most of the text from social media sites is readily
accessible via simple computer programs.
Yet the outgrowth in text-based data on
the Internet is not limited to social media sites. Screen-scraping technologies can be
used to extract information from any number of Internet sites within time frames that
are only limited by digital storage capacity.
And the potential to collect such data is not
only tied to the future, but also the past. Since 1996, a non-profit organization known as
the Internet Archive has been storing all text from nearly every website on the Internet.
The outgrowth of text-based data is also not confined to the Internet. Thanks to new
digital technologies from fields as diverse as library science and communications, an
unprecedented amount of qualitative data is being archived. Google alone has already
created digital copies of nearly every single book ever written in collaboration with more
than 19 million libraries worldwide.
Academic data warehouses such as LEXIS-NEXIS or
ProQuest now contain digital copies of most of the worlds journals, newspapers, and
magazines. The Vanderbilt Television News Archive contains copies of most major
The US National Science Foundation invested more than $15 million in Big Data projects in 2012, and will
easily surpass this amount in upcoming years due to the development of new infrastructure for funding big
data projects in collaboration with Britains Economic & Social Research Council, the Netherlands Organi-
zation for Scientific Research, and the Canada Foundation for Innovation, among many others.
Jesse Alpert and Nissan Hajaj, We knew the web was big…” Official Google Blog, July 25th, 2008 (http:// accessed January 2012).
Pew Internet & American Life Project, February 1st, 2012.
Social Networking Popular Across Globe,Pew Research Global Attitudes Project, December 12, 2012.
Moreover, the US Library of Congress recently announced plans to release a database of every single Twitter
message ever made. Current estimates place the total number of tweets that might be archived at more than
170 billion.
Web-scraping technologies have facilitated the collection of remarkably large datasets. Golder and Macy
(2011), for example, recently conducted a study of more than 500 million Twitter messages produced in more
than 84 countries over a 2 year period.
Though access to the entire Google book archive is limited by pay walls designed to protect copyright
privileges, Google has released the entire dataset in ngramformat, which allows scholars to analyze them
via the automated text analysis tools discussed in further detail below.
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newscasts produced since 1998. An unprecedented amount of text-based data that describe
legislative debates, government reports, and other state discourse is also now available on
websites such as the National Archives of the United States and Great Britain. Qualitative
academic research is also being compiled within meta-dataarchives on an unprecedented
scalefrom in-depth interview data to field notes.
Continuing improvement in digital
speech recognition technologies has also facilitated even more text-based data from histor-
ical audio sources to local town hall meetings that are recorded and uploaded to websites for
posterity. Indeed, the remarkable growth in text-based data warrants a brief thought exper-
iment: what types of text or speech-based data are not currently being archived?
If the answer is littleor very littletext is not being archived, then cultural sociology
must have a reckoning with big data alongside those in other fields.
Political scientists
are currently exploring the potential of social media to explain political mobilization
(Hopkins and King 2010; Livne et al. 2011). Public health scholars use Twitter to identify
trends in disease (Paul and Dredze 2011), and communications scholars claim it can be
used to predict shifts within the stock market (Bollen et al. 2011). Even humanities
scholars have invented the vibrant new field of digital humanities (e.g. Gold 2012;
Moretti 2013; Tangherlini and Leonard 2013). By comparison, cultural sociologists have
made very few ventures into the universe of big data even thoughtexts are a central object
of study in the fieldin the form of primary documents, interview transcriptions, or field
In this article, I argue inattention to big data among cultural sociologists is doubly
surprising since it is naturally occurringunlike survey research or cross-sectional
qualitative interviewsand therefore critical to understanding the evolution of meaning
structures in situ. That is, many archived texts are the product of conversations between
individuals, groups, or organizations instead of responses to questions created by re-
searchers who usually have only post-hoc intuition about the relevant factors in meaning-
makingmuch less how cultural evolves in real time.
For all the promise of big data for cultural sociology, formidable obstacles remain.
First of all, the sheer volume of data can be overwhelming. Large corpora cannot be
coded by hand, and automated data mining techniques are of little utility if they are not
guided by theory. Second, big data is untidy. Although computer-assisted data classi-
fication and data reduction techniques have improved in the past decade, much big data
analysis remains computationally intensive and therefore out of reach for many cultural
sociologistsparticularly those without any background in statistics or computer
programming. Thirdand perhaps most importantlythere is much that is of interest
to cultural sociologists that is not easily reducible to text. The greatest challenge for
cultural sociologists interested in big data is to develop new techniques to measure the
unspoken or implicit meanings that occur in-between words. The preconscious cultural
scripts or frames that shape how people understand the world (e.g., DiMaggio 1997),
See, for example, the Dataverse Network, the Interdisciplinary Consortium for Political and Social
Research, and the United Kingdoms Qualidata archive.
The neologism big datahas come to refer to many different types of data. Here, I use the term to refer to
the increasingly large volume of text-based data that is often-though not always- produced through digital
sources. As the remainder of this manuscript describes, these data are also unique because they are naturally
occurring,unlike survey data which result from the intrusion of researchers into everyday life.
Exceptions described in additional detail below include Franzosi (2004), Lewis et al. (2008), Bail (2012),
Bail (forthcoming) and several other works in progress.
Real timerefers to the collection, presentation, or analysis of data at or very near the time it is being
produced by social actors.
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for example, are not always manifest in speech or text. Similarly, most big data eschews
the production of meaning through bodily interaction (e.g., Eliasoph and Lichterman
2003)though the future of big data may include new techniques to analyze the ever-
increasing volume of video on the Internet (Collins 2013; Lan and Raptis 2013).
This article does not offer solutions to each of these limitations of big data. Instead, it
provides a critical survey of recent developments within the big data movement and
links them to outstanding theoretical debates and measurement challenges within
cultural sociology. These include the measurement of cultural environments or meaning
systems such as discursive fields; the classification of cultural elements such as frames
or schema within such systems; and tracing cultural processes over long segments of
time. In describing the promise of big data for cultural sociology, I also detail how the
latter field may address some of the most vexing challenges of the former given its
foundational interest in the systematic study of meaning. I provide only limited
discussion of the technical and logistical issues that arise in working with big data
because these issues are currently being addressed within separate literatures referenced
I also do not review the promising field of quantitative narrative analysis
because it has been addressed elsewhere.
This article is thus an invitation to cultural
sociologists curious about the potential of big data and a call to shatter the disciplinary
silos that inhibit collaboration between this field and those who lead the big data
Mapping cultural environments
By and large, the central objects of study in cultural sociology have been confined to
micro-levels of analysis. For example, cultural elements such as symbolic boundaries
(e.g., Lamont 1992), cultural toolkits (e.g., Swidler 1986), cognitive schemas (e.g.,
DiMaggio 1997), and cultural frames (e.g., Benford and Snow 2003) have been defined
as judgments, classifications, or pre-conscious decisions that can only be measured
through close readings of texts such as interview transcripts, content analysis of key
texts, or ethnographic field notes. Yet as Swidler (1995)argues,the greatest unan-
swered question in the sociology of culture is whether and how some cultural elements
control, anchor, or organize others.
For example, how are cultural frames ordered
within vast discursive fields? Is there a space between such fields? How do cultural
frames shape the evolution of fields more broadly? Addressing such questions requires
meso- and macro-level analysis of the relationship between multiple cultural elements
or systems of meaning. One of the most promising dimensions of the big data
movement for cultural sociology is to enable new analyses at these larger levels of
analysis. As I describe below, one can now obtain every website, blog, social media
message, newspaper article, or television transcript on a given topic fairly easily.
The capacity to capture allor nearly allrelevant text on a given topic opens
exciting new lines of meso- and macro-level inquiry into what I call cultural
For a technical overview of techniques designed for analysis of Big Data, see Manning and Schuetze
For an overview, see Franzosi (2009).
See also Ghaziani and Baldassarri (2011).
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environments (Bail forthcoming). Ecological or functionalist interpretations of culture
have been unpopular with cultural sociologists for some timemost likely because the
subfield defined itself as an alternative to the general theory proposed by Talcott Parsons
(Alexander 2006). Yet many cultural sociologists also draw inspiration from Mary
Douglas (e.g., Alexander 2006;Lamont1992; Zelizer 1985), wholike Swidlerinsists
upon the need for our subfield to engage broader levels of analysis. For sociology to
accept that no functionalist arguments work,writes Douglas (1986,p.43),is like cutting
off ones nose to spite ones face.To be fair, cultural sociologists have recently made
several programmatic statements about the need to engage functional or ecological
theories of culture. Abbott (1995), for example, explains the formation of boundaries
between professional fields as the result of an evolutionary process. Similarly, Lieberson
(2000), presents an ecological model of fashion trends in child-naming practices. In a
review essay, Kaufman (2004) describes such ecological approaches to cultural sociology
as one of the three most promising directions for the future of the subfield.
The concept of discursive fields is perhaps the most promising theoretical construct
to advance an ecological approach to cultural sociology (Bourdieu 1975; Foucault
1970;Martin2003; Wuthnow 1993). Yet field theory is often castigated for being
tautological, or assuming the existence of invisible or intangible social forces that
reproduce structures of inequality or patterns of cultural differentiation without ever
directly observing them. The boundaries of fields are usually unobserved in empirical
studies because of the considerable methodological obstacles involved. Apart from
Eyal (2009), cultural sociologists have scarcely theorized the outer limits of cultural
fields, the spaces between them, or the relationships among multiple fields.
This is a
significant limitation since most field theory makes several assumptions that are
inherently ecological. For example, many studies assume that relationships between
actors or groups of actors within a field produce a polarity that sustains or reproduces
uneven power relationships or access to institutions (Bourdieu 1985; Fligstein and
McAdam 2011; Wuthnow 1993). Others borrow more directly from ecological or
evolutionary theory to explain the competition for attention or resources within fields
(Abbott 2001;Kaufman2004;Lieberson2000), or the ability of cultural entrepreneurs
to exploit niches within such environments (e.g., Mische 2008).
Despite the implicit ecological reasoning of field theory, most applications of this
framework rely upon micro- or meso-level measurement strategies. For example, many
studies identify key actors or institutions within fields and trace their influence over
other parts of the fields. Other studies focus upon conflict or classification struggles
within fields in order to identify such influential actors (Bourdieu 1990). As a result,
these types of studies only observe the consequences of field-level processes rather than
meso- or macro-level relationships between social actors and cultural elements that
most scholars believe create such social spaces.
These micro-level measurement
strategies are typically necessary because most discursive fields are so broad that an
entire team of researchers working for several years could only map a fraction of all the
texts, transcripts, or archives that define them. The size of most cultural fields has
become even more daunting with the rise of the Internet. Indeed, a researcher could
See also Mark (2003).
One exception is Evans and Kays(2008) study of field overlap.
Exceptions include Mohr and Guerra-Pearson (2010)andBail(2012).
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easily follow links between websites for hours only to forget where, when, or why they
shifted focus from one site to another.
The big data movement has made extracting all text from a discursive field easier
than ever before.
Massive databases already exist that classify texts into meaningful
social categories. For example, services such as LEXIS-NEXIS and Pro-Quest have
sophisticated searchable indexes that cover industries, geographical location, time, or
different types of text (e.g., newspapers, newswires, or television transcripts). Simple
Boolean operates such as ANDand ORcan be used to further specify meaningful
cultural environments within each of these sub-samples.
Yet perhaps the most
powerful innovation of the big data movement for the mapping of cultural environ-
ments has been screen scraping, or automated extraction of text from websites. Screen
scraping is typically used to mine text or other data from web pages, though it can also
be used to extract text from scanned images using Optical Character Recognition
(OCR) technologies. A variety of data archives have developed searchable indexes
based on such screen-scraping technologies. Google, for example, allows Boolean
searches of its archives of books, blogs, government documents, and major US
newspapers and magazines.
But new technologies produced by the big data movement have also advanced
automated extraction of text far beyond simple indexes, Boolean searches, and screen
scraping. In particular, new techniques have been developed to exploit the relational
nature of many sources of big dataparticularly those from the Internet. For example,
Gong (2011) recently introduced new software that fuses snowball sampling methods
with screen-scraping technologies. The user simply inputs a starting website and a
classifying rule such as a Boolean search term or one of the other classification
algorithms described in further detail below. The software then visits each site that is
linked to the starting website and uses the classifying rule to decide whether it should
be included in the sample. If so, the program extracts all text from the site and repeats
the process of spideringlinks across multiple waves that are only constrained by
computer memory processing power. Given a number of different starting sites and a
sufficient number of waves, the Snowcrawl software produces a total sample of all
websites pertaining to a given topic. Although this tool is currently limited to the
Internet, a number of other qualitative data archives store relational data that could
potentially be analyzed using similar automated snowball methods. What is more, the
majority of newspapers, television stations, journals, or other texts of interest to cultural
sociologists are now available on the web.
A second promising tool for extracting large amounts of data from the web or
qualitative data archives are Application Programming Interfaces (APIs). These web-
based tools provide an interactive interface with large data archives that are designed to
enable targeted data extraction. They were developed primarily for consumer pur-
posessuch as the creation of third party applications for social media sites such as
While automated data extraction methods are particularly useful for mapping the contours of discursive
fields, it is important to note that such techniques do not capture the deeper preconscious cultural elements that
undergird social fields as Bourdieu and others have theorized them (e.g., Bourdieu 1990; Fligstein and
McAdam 2011;Martin2003). I return to the question of whether big data techniques can be leveraged to
classify such cultural elements in the following section as well as my discussion and conclusion.
For example, one might define a discursive field by identifying all texts with a certain set of keywords or
within a certain search index offered by text archives.
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Facebook, Twitter, or Googlebut a number of academics have begun to use them as
data collection tools as well (Bail 2013a;GabyandCaren2012; Livne et al. 2011).
Even conventional media outlets such as the New York Times now offer APIs that enable
users to search and download articles or user comments from their website. APIs are
superior to other forms of data extraction not only because they enable more sophisti-
cated targeting of different types of textsuch as Twitter messages about the Arab
Springbut also because such sites typically record a vast array of information about
the users of their sites as well as their behavior online. For example, TwittersAPI
enables rapid extraction of information about the online social networks of individual
users. Facebook and Googles API enable direct interface with its massive archives of
web content as well, but also includes information about the size, geographic location,
and demographic characteristics of the audiences of each site as well.
Classifying culture
Obtaining total or near total samples of text on a given topic is a remarkable feat given
that it was nearly unthinkable only a decade ago. Yet such giant samples are of little
utility if they cannot be classified in a meaningful manner. Cultural sociology has been
fascinated with classification since its inception because it was largely inspired by the
Durkheimian idea of classification struggles (e.g., Barth 1969; Bourdieu 1975;Douglas
1966;Latour1988). For example, Gieryn (1999) highlights the critical role of social
classification in the evolution of scientific fields. Lamont (1992,2000)explainshow
class and racial boundaries shape the process of group formation. Finally, Espeland and
Stevens (1998) make a broader argument about the key role of commensuration in
producing social power.
Yet for all the theoretical interest in the process of classifi-
cation, cultural sociologists seldom discuss the appropriate way to measure social
categories (Lamont and White 2009). Most studies either rely upon in-depth interviews
or case studies that highlight the social construction of ranking within institutions. The
lack of consensus about how to classify data has even prompted some critics to accuse
cultural sociologists of the reification of social classifications according to their theo-
retical persuasion (e.g., Biernacki 2012).
To date, cultural sociologists have scarcely explored the promise of automated text
analysis to classify texts.
Where these techniques have been used they have been relatively
primitive approaches to automation that simply identify keywords or phrases. This approach
is severely limited because it requires the researcher to have an a priori sense of which terms
are well suited to address the theoretical question of interest. Moreover, it eschews the
broader context of words within sentences. One solution to this problem is to evaluate the
co-prevalence of words within sentences using Global Regular Expression Print (GREP)
commands available in qualitative software analysis programs such as Atlas.TI or
Facebooks API requires user-authentication to access these data. Therefore, one must either access only
publicly available data or obtain an authentication token from a Facebook pages owner. Elsewhere, I argue
that app-based technologies are the most promising data collection tools to overcome such challenges. See
Bail (2013b).
For a recent review of this literature, see Lamont (2012).
Notable exceptions discussed in further detail below include Mohr (1998), Franzosi (2004), Bearman et al.
(1999), Bearman and Stovel (2000), Smith (2007), and Bail (2012).
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WordStat. Yet these approaches nevertheless fail to recognize important nuances in the
use of language. For example, a GREP search for sentences with the terms President
and hatewould reveal both I hate the President,and Id hate to be President.
Recent technological advances within the fields of computer science, pattern identifica-
tion, and linguistics have produced a variety of superior alternatives. I begin by reviewing
unsupervisedtext classification techniques that rely exclusively on computer algorithms to
create meaningful groupings of texts. For example, recent studies have invoked a number of
different forms of multi-dimensional scaling or cluster analysis to classify texts (e.g.,
Grimmer and King 2011; Livne et al. 2011).
These techniques replace each unique word
in a document with a number and then use various metrics to calculate dissimilarities among
all texts in the sample. These measures may be plotted within multidimensional space in
order to identify meaningful groupings of documents. A substantial problem with cluster
analysis is that the results are highly sensitive to the researchers assumptions about the
number of possible clusters (k), as well as the mathematical distances employed within each
algorithm. These idiosyncrasies can be controlled, however, if multiple forms of cluster
analysis are used in tandem. Grimmer and King (2011), for example, have developed
software that applies all existing variants of cluster analysis to large text corpora. They
apply this powerful tool to thousands of political texts by or about US presidents in order to
classify their ideological position on a range of substantive issues.
Another promising development within the big data movement for cultural sociologists is
the burgeoning field of machine learningand specifically the field of topic modeling. This
new field resulted from collaboration between linguists and computer scientists designed to
identify hidden or latent themes within large corpora.
Topic Models identify such themes
using probabilistic models that evaluate the co-occurrence of words. The most popular form
of topic modeling is Latent Dirichlet Allocation (LDA), which assumes a random allocation
of words across a latent theme or topic and then uses a generative process of classification to
analyze the probability of a document containing information about a topic given the
distribution of words therein.
Dozens of studies have used LDA or related Bayesian
approaches to infer latent topics in scientific journals, news articles, or blog posts (e.g., Blei
and Lafferty 2007; Hopkins and King 2010; Quinn et al. 2010). Despite these advances,
topic models have several considerable limitations. For example, the method assumes that
the order of words in a document does not matter, as well as the order of documents within
the broader sample. Most topic models also required that each document be assigned to
mutually exclusive categories, and do not recognize relationships between topics them-
selves. Basic topic models also do not recognize that topics may shift or combine over time.
Finally, topic modelsnot unlike cluster analysismust be validated in order to verify the
appropriate number of topics within a corpus.
This is particularly difficult given that many
cultural sociologists are interested in analyzing broad, unstructured samples of text such as
those described in the previous section of this article.
Mohr (1998) made early calls for cultural sociologists to adopt these methods to classify meaning structures, yet
they were mostly ignored even as they become widely used by cognitive anthropologists (e.g., DAndrade 1995).
For an overview of this field, see Blei (2012).
For a technical overview of LDA, see Blei et al. (2003).
A number of scholars have proposed validity measures for LDA, most recently Blei (2012). Most of these
emphasize comparisons of topic models via log-likelihoods or harmonic means, yet most proponents of topic
modeling agree that they must also be validated via qualitative inspection of individual topics within subsets of
large samples.
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Proponents of topic modeling have already begun to develop a number of solutions
to these limitations of this method, though they are too technical to discuss here.
Among the more promising recent developments in the field is the advent of super-
visedtopic modeling (Blei and McAuliffe 2010). In this technique, a human coder
identifies topics within a subset of documents, and topic models use these assignments
to assess probability instead of assuming that the distribution of topics across docu-
ments is random. Supervised text classification was first introduced within social
science by Hopkins and King (2010), who used this approach to assess public opinion
of presidential candidates expressed upon thousands of political blogs during the 2008
Given a sufficient number of training documentsproduced through in-
depth coding, these authors argue that their technique classifies sentiment about
presidential candidates more reliably than human coders themselves.
While such
claims have not yet been widely validated, supervised learning techniques hold con-
siderable promise for the purpose of identifying cultural elements within texts and
further improving the snowball sampling methods described above.
Perhaps the most important question for cultural sociologists interested in employing
topic models is whether they can be used to classify cultural elements such as frames,
symbolic boundaries, or cultural toolkits. A number of current studies suggest topic
models may be used to capture such nuanced cultural elements. For example, Dimaggio
et al. (forthcoming) argue topic models can be used to identify frames about arts
funding. Polletta is currently using topic modeling to identify hidden frames in Internet
discussions about cap-and-trade.
Hopkins (2013) employs topic models to measure
frames about the Affordable Care Act. Yet a key issue remains whether cultural
framesas Goffman (1974) first defined themcan be represented by groups of
words. While the face-work that Goffman emphasized is clearly not measurable
through text, Goffman himself used texts extensively throughout his work, including
biographies, newspaper clippings, and transcripts of interactions.
Although Goffman
emphasized the absence of certain words as much as the presence of othersthese
omissions could be modeled effectively because they would shape the probability
distributions around groups of words that LDA analyzes to create classifications of
texts. Nevertheless, the quality of supervised topic modeling is only as good as the
codes developed by human coders themselves. Therefore, cultural elements that are
highly nuanced or situation-based are not easily captured via this technique because of
low inter-coder reliability.
For example, see Blei and Lafferty (2006), Wallach (2006), Chang et al. (2009), and Hopkins and King
See also Grimmer (2010) and Quinn et al. (2010).
In particular, Hopkins and King (2010) argue that coding more than 500 documents produces diminishing
returns in the reliability of automated text analysis.
For example, a supervised topic model can be used to determine whether websites should be included in a
directed web-crawl such as SnowCrawl to capture sites that discuss a theme or topic without using a single
See Baumer et al. (2013).
Consider, for example, the diaries analyzed in Goffman (1963) or the newspaper clippings in Goffman
(1974). Also, textual descriptions of face-work or other unspoken forms of bodily interaction in the form of
field notes could potentially be analyzed using topic models.
For a discussion of the challenges of achieving high levels of inter-coder reliability in cultural analysis, see
Krippendorff (2003).
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On the other hand, the meticulous coding definitions required by topic models may also
provide an opportunity for cultural sociologists to contribute new methodologies to the big
data movement. Indeed, the use of generative and multi-stage coding schemes has been a
key concern of cultural sociology in the form of thick description(e.g., Geertz 1973),
middle-range theory(Merton 1949), structural hermeneutics(Alexander and Smith
2001), and paradigmatic clusters(Weber 2005). Each of these approaches emphasizes
that researches should move back and forth between different levels of analysis to tune their
coding schemes and to assess the scope conditions of a particular finding. To this end, the
expertise of cultural sociologists may be applied to repeated stages of supervised topic
modelselaborating classification systems as if they were Russian Dolls, to borrow
Bourdieus metaphor. Mohr et al. (2014), for example, have advanced this technique in
their study of US National Security Statements over a 22 year period. By developing
increasingly precise codes from iterative qualitative analysis of small sub-sets of this large
corpus of text, these scholars have developed increasingly promising topic models that can
later be applied to the entire sample. We need further empirical validation of such techniques.
At the very least, however, such methods provide a systematic method of focusing
qualitative microscopes within the increasingly overwhelming world of big data.
Tracing the evolution of cultural environments
One of the most promising elements of the big data movement is that so much of the
qualitative data that has been collected is longitudinal. For example, the Library of
Congresss archive of all Twitter messages will enable unprecedented analysis of how
different issues rise and fall over time. The Internet Archive and screen-scraping
technologies could be used to map shifts in the discourses of different types of websites
over time. Likewise, the massive newspaper and television transcript archives now
available could be used to analyze similar issues over the past century. These longitu-
dinal data are particularly promising because so many of the most pressing questions in
cultural sociology concern change over time. While Swidlers(1986) toolkit analogy
has received extensive attention in recent decades, for example, her call for future
studies to examine the transition from unsettled to settled historical periods has been
mostly ignored.
While Sewells(1996) theory of events has inspired considerable
interest, few studies place such events in broader historical context.
Finally, Lamonts
(1992) work reveals considerable cross-national differences in the salience of symbolic
boundaries. Yet we urgently need broad historical analyses to identify how such
divergent meaning systems evolved over time. Each of these outstanding questions
requires methods capable of capturing broad-scale cultural change.
In addition to identifying cultural elements such as frames or symbolic boundaries,
automated text analysis can be used to differentiate social actors or key events within
But see Cerulo (1998), Wagner-Pacifici (2010), and Bail (2012).
Still, historical analyses with big data are limited by the availability of texts produced during this period that
were amenable to digitization. This presents a number of important limitations, including pervasive illiteracy
during early historical periods as well as the tendency for only elite accounts of historical events to survive the
passage of time. Still, comparative-historical sociologists face these problems regardless of whether they are
working with big data. Furthermore, primary documents obtained through archival analysis can be easily
digitized through photographs, scanning, and text-recognition technologies.
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large qualitative datasets.
Cultural sociologists can make huge strides towards
advancing theories of social change simply by mapping the relations among cultural
elements, actors, and events over time. The literature on quantitative narrative analysis
has already established how analysis of relationships between actors and events can be
used to map broad historical sequences (e.g., Bearman et al. 1999; Bearman and Stovel
2000; Franzosi 2004; Smith 2007). Incorporating cultural elements identified via topic
modeling into such methods would open exciting new lines of inquiry about the
interpenetration of culture and structure. If topic modeling can be used to identify
actors and organizations as well as the cultural elements they produce, for example,
social network relationships might be mapped onto cultural patternsor vice versa. At
a minimum, mapping the relationships among cultural elements, actors, and events
would help focus in-depth qualitative analysis of key historical shifts or turning
points(Abbott 1997) where meaning structures change.
One problem, of course, is that cultural elements themselves often change through-
out such broad-scale historical transformations. Sewell, for example, argues the very
concept of revolution was developing at the same time that murderous mobs stormed
the Bastillesetting off the French revolution, before they knew precisely what they
were doing. Topic models are ill equipped to capture such nuances unless human
coders calibrate them repeatedly across multiple time periods. Even then, slight shifts in
cultural elements may be difficult to code automatically because human coders may
struggle to achieve high inter-coder reliability. Here again, new tools for automated text
may prove useful. For example, several new methods have been developed to identify
dissimilarities between pairs of documents. Primitive forms of these techniques simply
count the number of words shared between the two documents. Yet recent advances in
plagiarism detection software employ word-mapsthat utilize data from thesaurus in
order to identify nearmatches between two documents as well (e.g., Bail 2012).
Once again, these document comparison tools will not identify cultural elements by
themselves. Yet they may be particularly powerful when combined with topic models
and micro-level qualitative analysis of key texts or transitional moments within history.
Another major advantage of big data is that much of it includes detailed information
about relationships between social actors. This is particularly true of social media sites
such as Twitter or Facebook, but advances in library science are also creating hyper-
links between texts within archival collections as well. Using TwittersApplication
Programming Interface, once can easily extract not only all the messages produced by a
single actor, but also the precise location of this actor within a broader social network
including measures of both inand outdegree. Livne et al. (2011) for example,
extracted 460,000 tweets from all candidates for US House, Senate, and Gubernatorial
elections between 2006 and 2010. Their data not only reveals the partisan networks of
such social actors, but also patterns in the similarity of the language they post on Twitter
via cluster analysis. Through this analysis, Livne et al. document the meteoric rise of the
If key actors or events are already known, simple key word searches or Global Regular Expression Print
(GREP) commands may also be used to identify them. If actors or events are not known, they can be identified
through keyword counts that remove common words such as theor and.Once actors or events are
defined, topic models may be used to identify them as well. A number of computer scripts have also been
recently developed to identify names within big data without such intermediary steps such as the Natural
Language Toolkit and the Stanford Parser.
See also Sewell (1996) and Wagner-Pacifici (2010).
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Tea Party in recent elections, and the realignment of mainstream conservative networks
that ensued. These and other datasets could be used toaddress a number ofkey questions
at the intersection of cultural sociology and network theory. For instance, Pachucki and
Breigers(2010) argument about cultural holeswithin networks, or Vaisey and
Lizardos(2010) theory that cultural worldviews influence network composition.
The potential to assemble large datasets that describe cultural elements, actors,
events, and social networks over time may also encourage critical advances in field
theory. Most of the most pressing questions in this literature are about the evolution of
fields over time (Fligstein and McAdam 2011; Padgett and Powell 2012). For instance,
a number of recent studies have begun to analyze the emergence of fields (e.g.,
Armstrong 2002;Bartley2007). By and large, these case studies are unable to
investigate a variety of broad cultural processes that may occur between discursive
fields. For example, do most fields emerge out of the dissolution of others? Or, do fields
develop when the space between any two pre-existing fields is sufficiently broad (Eyal
2009;Medvetz2012)? Big data may also enable analysis of a number of intriguing
questions within individual fields as well. For example, do discursive fields have
carrying capacities for new forms of culture? Do certain actors gain power within
discursive fields by exploiting niches between rival factions? Or, what is the relation-
ship between the core and periphery of discursive fields (e.g., Bail 2012)?
Another exciting feature of big data is that it often includes geo-coded data. For
example, Twitter and Facebook record the geographic location of their users. This
information is also often recorded on the comments sections of websites. Finally,
analytics or insightsdata often include the latitude and longitude of visitors to
different websites via Internet Protocol (IP) addresses or other geographic identifiers
such as city names. Political scientists have even mined visual data on ethnic conflict
from Google Earth (Agnew et al. 2008). The potential to look at the relationship
between Cartesian coordinates and cultural elements could create a new subfield within
cultural sociology that analyzes the geography of meaning. Such a field might examine
questions such as: 1) Do cultural frames or symbolic boundaries cluster at the national
level or supranational levels? 2) Does physical proximity breed more convergence of
worldviews than online interaction? Finally, does the answer to these two questions
change over time as the forces of globalization push people ever closer together?
Cultural sociology has long suffered from an imbalance of theory and data (Ghaziani
2009). Yet the big data movement may radically alter this equilibrium. The big data
movement began with the Internet and social media, but the future of the field will also
entail increasingly ambitious forays into the past. As digitized historical archives
continue to expand and social scientists coordinate new ways of organizing qualitative
meta data with rich detail about the evolution of meaning, cultural sociologists can no
longer afford to ignore the big data movement. Above, I argued that integration of in-
depth qualitative coding techniques pioneered by cultural sociologists and anthropol-
ogists can be leveraged to improve already powerful automated text analysis techniques
On the concept of cultural holes, see also Lizardo (in this issue).
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produced by computer scientists, linguists, and political scientists. This synthesis will
enable cultural sociologists to achieve theoretical progress on questions that were once
thought un-measurable. Proponents of big data may also gain key insight from cultural
sociologists about how to further hone their tools to map the contours of cultural fields,
classify cultural elements, and trace the evolution of culture over time.
Yet for all of my optimism about the marriage of cultural sociology and big data,
formidable obstacles remain. Perhaps the most vexing problem is that big data often
does not include information about the social context in which texts are produced
(Griswold and Wright 2004). Although we are able to collect millions of blog posts
about virtually any issue, these data typically include little or no information about the
authors of such postsor those who comment upon them. Twitter data are publicly
available, but provide very little information about the social context in which Tweets
are produced. Other sites such as Facebook collect massive amounts of data about
social context but are often unable to share them with researchers because of concerns
about user privacy. Sources of big data outside social media also often lack important
information about the social context in which texts are produced. Collecting every
newspaper article on a political topic is of marginal utility absent in-depth analysis of
the political and institutional processes that lead media to gravitate towards one issue
over another.
Yet these obstacles are not without solutions that might build upon the
progress of cultural sociologists in developing mixed-method research designs. For
example, qualitative or quantitative surveys of Twitter users could be conducted to
place their online behavior within broader context. Or, large-scale analyses of media
data or historical surveys might be used to identify compelling puzzles for comparative
historical analysis. In theory, big data could also be used to guide ethnographic
interventions as wellor at least help place the findings of ethnography within broader
cultural fields. In brief, big data methods should be viewed as a complementnot a
replacementfor the tried and tested techniques of cultural sociology.
A second major challenge is that computer-assisted coding can never be more
reliable than the codes themselves. Cultural sociologists seldom discuss coding criteria
or inter-coder reliability, in part because the definition of many of our core concepts is
highly contested (Biernacki 2012). One need only read the literature on framing, for
example, to witness significant disagreement about whether and how they should be
measured or operationalized.
While these debates will not be easily resolved, the
integration of big data and cultural sociology will depend critically upon our capacity to
converge upon several broadly accepted definitions of these core concepts. Yet big data
may actually facilitate such conversationssince conceptual vagueness among cultural
sociologists results in part from our paucity of shared datasets. Cultural sociologists are
also looking across disciplinary lines for guidance in making core concepts more
concrete. For example, Mohr et al. (2013) have fused the literatures on narrative from
It is also worth noting that texts that cannot be collected because they are not in the public domain may
ultimately have less impact upon the evolution of broader cultural domains precisely because they are hidden
from public view. This underlies a broader pragmatist argument about the need to focus attention upon
consequences of social action (e.g., Johnson-Hanks et al. 2011; Tavory and Timmermans 2013). An interesting
analogue is the debate about the social construction of ethnicity via the enumeration of different groups by the
US Census (cf. Loveman and Muniz 2007). I thank Andy Perrin for bringing this issue to my attention.
For a detailed analysis of conceptual and methodological ambiguities in the measurement of frames, see
Scheufele (1999).
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linguistics with studies of social networks and topic modeling from sociology and
computer science. Polletta is currently synthesizing linguistics and cultural sociology
using new visualization techniques that enable them to explore how making people
aware of their cultural schemas shapes their behavior during democratic deliberation.
Finally, Ignatow and Mihalcea (2013) propose a model for big data analysis that
synthesizes neuroscience and Bourdieusian practice theory.
A final concern for cultural sociologists is the relatively high entry cost for those
who wish to develop the technical expertise currently necessary to work with big data.
Although these costs are rapidly decreasing thanks to simple web-based tools for big
data analyses, formalizing these techniques for cultural sociology will require a new
generation of scholars with both technical expertise and theoretical ambition. For now,
the big data movement urgently requires the guidance of theoretically and qualitatively
oriented cultural sociologists. Little can be learned from big data without big thinking.
While data mining may reveal interesting patterns in large text corpora or compelling
visualizations, many pieces of hay have come to resemble needles.
Therefore, the
future of the big data movement hinges upon collaboration among cultural sociologists,
computer scientists, and others to teach computers to differentiate different types of
meaning and their shifting relationships over time.
Acknowledgments I thank Elizabeth Armstrong, Alex Hanna, Gabe Ignatow, Charles Kurzman, Brayden
King, Jennifer Lena, John Mohr, Terry McDonnell, Andy Perrin, and Steve Vaisey for helpful comments on
previous drafts. The Robert Wood Johnson Foundation and the Odum Institute at the University of North
Carolina provided financial support for this research.
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Christopher A. Bail is Assistant Professor of Sociology at the University of North Carolina, Chapel Hill. His
research interests include cultural sociology, political sociology, organizations, and mixed-method research
designs. He is currently completing a manuscript entitled Terrified:How Anti-Muslim Organizations Became
Mainstream. His other work has appeared in the American Sociological Review and the Revue Europeene des
Migrations Internationales.
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... In the first stage of the research we studied the 50 most followed Media Directors from Spain and analyzed the 50 accounts they started to follow as a network, from 2017 to 2019 (Israel-Turim and Micó-Sanz, 2021). We categorized these accounts and proceeded to do a quantitative data analysis as we crossed different variables of the data we collected and used visualization tools in the search for possible repetitions that could signify patterns or trends (Mahrt and Scharkow, 2013;Bail, 2014;Batrinca and Treleaven, 2015). We melded data analysis techniques combining computational and manual methods to preserve contextual implications while obtaining as much information and knowledge from the data (Lewis, Zamith and Hermida, 2013). ...
... Con el objetivo de contribuir con la profundización de la comprensión de las dinámicas y flujos de influencia online entre las élites de poder, analizamos a través de un software de machine learning, las 50 cuentas que la red de los directores de medios más seguidos en España comenzó a seguir, y las comparamos con las cuentas que comenzaron a seguir los medios que dirigen. Las categorizamos en tipos de cuentas, ubicación y género, y analizamos las repeticiones entre las cuentas que comenzaron a seguir, para luego trabajar con métodos de visualización de datos en busca de tendencias y patrones(Bail, 2014;Batrinca and Treleaven, 2015). Los resultados de esta investigación indican que algunos patrones de comportamiento difieren entre ambas redes, como el género y los tipos de cuentas que comenzaron a seguir, mientras que presentaron tendencias similares con respecto a la ubicación de las cuentas. ...
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Digital platforms have transformed the influence streams among media, journalists, politicians and the citizenship, as well as concerning gatekeeping and agenda setting (Guo and Vargo, 2017; Wallace, 2018; Casero-Ripollés, 2021). Nonetheless, homophilic tendencies among power groups continue to be reproduced online (McPherson, Smith-Lovin and Cook, 2001; Maares, Lind and Greussing, 2021). With the objective of contributing to the deepening of the understanding of the dynamics and influence flows online among power elites, we analyzed via a machine learning Software, the 50 accounts that the network of the most followed Media Directors in Spain began following and compared them with the accounts that the Media they manage started following. We categorized them in Types of accounts, Location and Gender, and analyzed the repetitions between the accounts they began to follow to subsequently work with data visualization methods in order to find trends and tendencies (Bail, 2014; Batrinca and Treleaven, 2015). The results of this research indicate that some patterns of behavior differ between both networks, such as the gender and types of accounts they began following, whereas the location presented similar trends. The year where we can see the highest similarities corresponds to 2018, an electoral year in Spain, where both networks started following a majority of Spanish male politicians.
... In fact, the codes were created when the data analysis took place, and the authors did not use any predetermined code. This way, the key components of the daytime tourism milieu were inductively derived from the data (Bail, 2014;Baumer et al., 2013). Secondly, the authors applied topic modelling, which is based on calculations and programming techniques for identifying similar and different elements of texts. ...
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In order to propose a repositioning toolkit, this research addresses the essence of the daytime tourism milieu of the Hungarian capital Budapest’s nightly party zone and formulates the following two research questions: (1) What are the available elements of the daytime tourism milieu of Budapest’s party zone? and (2) How can this milieu enhance tourist experience for leveraging a sense of place in a future post-Covid-19 era? The data for this research were collected with the help of 85 undergraduates, who were given the task of taking 3 photos, as if they were tourists, aiming to capture the best reflection of the daytime tourism milieu of Budapest’s party zone. A database of 255 photos was analysed through visual content analysis. Additionally, each image was assigned a location of the photo, five hashtags and a short description. The descriptions of the photos were analysed by Python programming and calculations. The findings show the most important research outcomes concerning Budapest’s party zone, focusing on the daytime values of the district. The research identified the “creative milieu”’, “Jewish heritage milieu” and “gastronomic milieu” as the most important daytime profiles of the party zone. Based on the findings, the authors propose a repositioning toolkit and a strategy which, on the one hand, will develop a stronger sense of place in the case of the tourism milieu of Budapest’s party zone and, on the other hand, will position the party zone not only as a place of nightlife but also as a venue of daytime tourism.
... An dieser Stelle kommen wir in den Bereich der automatisierten bzw. computergestützten, quantitativen Textanalyse (Bail 2014;Heiberger und Riebling 2016). Mit der Hilfe solcher Verfahren sind beispielsweise die Identifikation von wiederkehrenden Themen sowie der in der digitalen Kommunikation transportierten Stimmungen in einem ersten Schritt möglich. ...
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Das Open Access Buch bietet für Einsteiger*innen Erklärungen darüber, wie eine geeignete qualitative oder quantitative inhaltsanalytische Methode abhängig von a) Forschungsinteresse und b) Datenumfang ausgewählt werden kann. Teil 1 definiert Auswertungstechniken und zeigt Möglichkeiten und Grenzen sozialwissenschaftlicher Inhaltsanalysen auf. Teil 2 stellt digital unterstützte und teilautomatisierte Techniken, Teil 3 die automatisierten Techniken Korrespondenzanalyse, Sentiment Analyse und Topic Modeling vor. Alle Einführungen erfolgen mit Beispielen und Softwareanwendungen (AntConc, MAXQDA, Python, RStudio oder VosViewer).
... Pierwsze polegają na tym, że algorytm sam na podstawie dostarczonych mu danych określa, w jaki sposób można podzielić tekst. Z ich pomocą można eksplorować zależności, których istnienie jest niełatwe do przewidzenia (Bail 2014;Grimmer, Stewart 2013). Nie sprawdzą się one jednak, gdy liczba kategorii jest z góry ustalona (jak w analizie sentymentu) lub łatwa do określenia (Lin, He 2009). ...
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One of the critical decisions when manually coding text data is whether to verify the coders’ work. In the case of supervised models, this leads to a significant dilemma: is it better to provide the model with a large number of cases on which it will learn at the expense of verifying the correctness of the data, or whether it is better to code each case n-times, which will allow to compare the codes and check their correctness but at the same time will reduce the training dataset by n-fold. Such a decision not only affect the final results of the classifier. From the researchers’ point of view, it is also crucial because, realistically assuming that research has limited funding, it cannot be undone. The study uses a simulation approach and provides conclusions and recommendations based on 100,000 unique and hand-coded tweets.
... Therefore, technologically we live in the era of big data, but theoretical sciences (e.g., cultural sociology) generate theories without technical background. A connection between the interdisciplinary field of both is thoroughly described by Bail [106]. In his research work, he tries to narrow the gap between theoretical sciences and technological advances, both related to data. ...
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Is culture considered to be our past, our roots, ancient ruins, or an old piece of art? Culture is all the factors that define who we are, how we act and interact in our world, in our daily activities, in our personal and public relations, in our life. Culture is all the things we are not obliged to do. However, today, we live in a mixed environment, an environment that is a combination of “offline” and the online, digital world. In this mixed environment, it is technology that defines our behaviour, technology that unites people in a large world, that finally, defines a status of “monoculture”. In this article, we examine the role of technology, and especially big data, in relation to the culture. We present the advances that led to paradigm shifts in the research area of cultural informatics, and forecast the future of culture as will be defined in this mixed world.
... Tüketici tarafından üretilen içerik, işletmelere genel olarak pazardaki tüketicileri ve özellikle de kendi müşterilerini "dinleme/anlama" fırsatı sunmaktadır (Urban & Hauser, 2004). Son yıllarda yapılan çalışmalar incelendiğinde; fen ve sosyal bilimlerde biyoloji, tıp, ekonomi, sosyoloji, işletme, iletişim bilimleri olmak üzere birçok farklı alt disiplinde (Korhonen vd., 2012;Bail, 2014;Xiang vd., 2015;Tang & Guo, 2015;Warren vd., 2015;Simmons vd., 2016;Amado vd., 2018) büyük verilere dayalı farklı analiz yöntemleri kullanılarak çalışmalar yapıldığı görülmektedir. Bu çalışmada da benzer teknikler kullanılarak ekşi sözlük platformundaki veriler incelenmiştir ve tüketici görüşlerinin saptanması amaçlanmıştır. ...
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Yaşanan sosyo-kültürel gelişmeler insanların yaşam biçimlerini değiştirerek beslenme alışkanlıklarında hazır ve atıştırmalık gıda ürünlerine yönelmelerine neden olmuştur. Krem çikolatanın tüketicilerin gündelik yaşamında önemli yere sahip olmasına karşın marka tercihinde etkili faktörlere yönelik literatür tarandığında kapsamlı araştırma yapılmadığı gözlemlenmiştir. Araştırmadaki amaç tüketicilerin krem çikolata markaları üzerine olan görüş ve düşüncelerini saptayarak, tercihlerini etkileyen faktörleri belirlemektir. Araştırmada ekşi sözlükte krem çikolata markalarına yönelik 574 sözlük yazarının yorumuyla oluşan veri seti incelemeye alınmıştır. Tüketici istek ve ihtiyaçlarını belirlemede ve pazar araştırmalarında büyük verilerin kullanımı her geçen gün artış göstermektedir. Bu bağlamda ekşi sözlükteki yorumlar büyük veri seti olarak değerlendirilebilmekte ve krem çikolata pazarına yönelik öngörü sağlayabilmektedir. Veriler WebHarvy uygulaması ile Ekşi Sözlük’ten alınarak, Python programında karşılaştırmalı analiz yapılmıştır. MAXQDA programı kullanılarak sözlük yazarlarının yorumları içerik analizine tabi tutulmuştur. Araştırma sonucunda veri setinde 29 farklı marka tespit edilmiş; lezzet, sağlık, fiyat başta olmak üzere üç temel değişken belirlenerek önem dereceleri ve bu değişkenleri etkileyen faktörler saptanmıştır. Tüketici nezdinde önem derecesi en yüksek olan faktör fındık oranı iken kullanılan yağın içeriği, şeker miktarı ve katkı maddeleri ikinci sırada yer almaktadır. Menşei, yerellik, doğallık ve marka benzeştirmesi öne çıkan diğer önemli unsurları oluşturmaktadır.
The emergence of big data and computational tools has introduced new possibilities for using large-scale textual sources in sociological research. Recent work in sociology of culture, science, and economic sociology has shown how computational text analysis can be used in theory building and testing. This review starts with an introduction of the history of computer-assisted text analysis in sociology and then proceeds to discuss five families of computational methods used in contemporary research. Using exemplary studies, it shows how dictionary methods, semantic and network analysis tools, language models, unsupervised, and supervised machine learning can assist sociologists with different analytical tasks. After presenting recent methodological developments, this review summarizes several important implications of using large datasets and computational methods to infer complex meaning in texts. Finally, it calls researchers from different methodological traditions to adopt text mining tools while remaining mindful of lessons learned from working with conventional data and methods.
How do women’s business networks help to advance women’s freedom? Drawing on Zerilli’s freedom-centred feminism, our study sets out to answer this question at the intersection of freedom, feminism and work. Critics argue that women’s business networks promote a postfeminist view of freedom focusing on individual self-realisation and thus participate in rolling back collective, feminist efforts to dismantle structural inequalities. We reconceptualise women’s business networks as political arenas and argue that making claims about shared interests and concerns in such an arena constitutes a feminist practice of freedom. With an original, inductive and qualitative research design combining topic modeling and dialectical analysis, we examine the claims made in 1529 posts across four women’s business network blogs. We identify postfeminist claims and new forms of change and transformation that can help to advance women’s freedom across three ‘dialectics of freedom’: conformity and imagination; performative care and relational care; sameness and openness. Our findings show that uncertain and contradictory ways of defining and engaging with women’s freedom can emerge through claim-making in such arenas. The fragility of the process and its outcomes are, then, what can move feminism forward at work and beyond.
The motivation behind this examination is to explore the status and the development of the logical investigations for the impact of interpersonal organizations on enormous information and utilization of large information for displaying the interpersonal organizations clients' conduct. This paper presents a far reaching audit of the examinations related with enormous information in online media. The investigation utilizes Scopus information base as an essential web crawler and covers 2000 of profoundly refered to articles over the period 2012-2019. The records are genuinely broke down and feline egorized as far as various standards. The discoveries show that explores have developed dramatically since 2014 and the pattern has proceeded at generally stable rates. In view of the review, choice emotionally supportive networks is the catchphrase which has conveyed the most noteworthy densities followed by heuristics techniques. Among the most refered to articles, papers distributed by re-searchers in United States have gotten the most noteworthy references (7548), trailed by United Kingdom (588) and China with 543 ci-tations. Topical investigation shows that the subject almost kept a significant and well-devel-oped research field and for better outcomes we can combine our exploration with "huge information examination" and "twitter" that are significant points in this field yet not grew well.
Management and control of urban growth for the development of heritage and improvement of life in the city of Bethlehem a cura di / edited by Sandro Parrinello 3D BETHLEHEM Gestione e controllo della crescita urbana per lo sviluppo del patrimonio ed il miglioramento della vita nella città di Betlemme
Fertility rates vary considerably across and within societies, and over time. Over the last three decades, social demographers have made remarkable progress in documenting these axes of variation, but theoretical models to explain family change and variation have lagged behind. At the same time, our sister disciplines—from cultural anthropology to social psychology to cognitive science and beyond—have made dramatic strides in understanding how social action works, and how bodies, brains, cultural contexts, and structural conditions are coordinated in that process. Understanding Family Change and Variation: Toward a Theory of Conjunctural Action argues that social demography must be reintegrated into the core of theory and research about the processes and mechanisms of social action, and proposes a framework through which that reintegration can occur. This framework posits that material and schematic structures profoundly shape the occurrence, frequency, and context of the vital events that constitute the object of social demography. Fertility and family behaviors are best understood as a function not just of individual traits, but of the structured contexts in which behavior occurs. This approach upends many assumptions in social demography, encouraging demographers to embrace the endogeneity of social life and to move beyond fruitless debates of structure versus culture, of agency versus structure, or of biology versus society.
In several ways, Swidler provides a more developed analysis of the relationship between culture and social movements than does McAdam. First, she focuses on the ways culture shapes individual beliefs and desires. Thus, culture provides a means by which people make sense of the world. Second, Swidler examines the ways culture provides repertoires of public symbols that structure the kinds of expected responses that individuals develop from their social interactions. A handshake on first meeting a person could be seen as such a symbol: Failure to shake hands once another has been extended is a deliberate insult. Thus, once they have offered it, most people expect that their hand will be shaken. Such an expectation represents cultural knowledge that exists even when no handshake is ongoing. Such assumptions may shape how a social movement acts even if its members are ideologically divided and its contention with the broader society sharp. Third, Swidler pays attention to the ways social institutions shape movement activities: If official organizations and others try to integrate or co-opt a group, for example, the movement is likely to behave differently than if it faces aggressive, perhaps violent, repression. Culture, then, is more than just the private beliefs of individual group members, and it is more than a set of broad principles that can be used for group purposes. It involves a dynamic interaction that shapes private and public acts together.
In July 2010, Terry Jones, the pastor of a small fundamentalist church in Florida, announced plans to burn two hundred Qur'ans on the anniversary of the September 11 attacks. Though he ended up canceling the stunt in the face of widespread public backlash, his threat sparked violent protests across the Muslim world that left at least twenty people dead. In Terrified, Christopher Bail demonstrates how the beliefs of fanatics like Jones are inspired by a rapidly expanding network of anti-Muslim organizations that exert profound influence on American understanding of Islam. Bail traces how the anti-Muslim narrative of the political fringe has captivated large segments of the American media, government, and general public, validating the views of extremists who argue that the United States is at war with Islam and marginalizing mainstream Muslim-Americans who are uniquely positioned to discredit such claims. Drawing on cultural sociology, social network theory, and social psychology, he shows how anti-Muslim organizations gained visibility in the public sphere, commandeered a sense of legitimacy, and redefined the contours of contemporary debate, shifting it ever outward toward the fringe. Bail illustrates his pioneering theoretical argument through a big-data analysis of more than one hundred organizations struggling to shape public discourse about Islam, tracing their impact on hundreds of thousands of newspaper articles, television transcripts, legislative debates, and social media messages produced since the September 11 attacks. The book also features in-depth interviews with the leaders of these organizations, providing a rare look at how anti-Muslim organizations entered the American mainstream.
During the 1980s and 1990s, Brazil struggled to rebuild its democracy after twenty years of military dictatorship, experiencing financial crises, corruption scandals, political protest, and intense electoral contention. In the midst of this turmoil, Ann Mische argues in this remarkable book, youth activists of various stripes played a vital and unrecognized role, contributing new forms of political talk and action to Brazil's emerging democracy.Drawing upon extensive and rich ethnography as well as formal network analysis, Mische tracks the lives of young activists through intersecting political networks, including student movements, church-based activism, political parties, nongovernmental organizations, and business and professional organizations. She probes the problems and possibilities they encountered in combining partisan activism with other kinds of civic involvement. In documenting activists' struggles to develop cross-partisan publics of various kinds, Mische explores the distinct styles of communication and leadership that emerged across organizations and among individuals.