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Summary Data Mining
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Applied Data Science Utrecht University (UU): data mining of textual and visual corpera.
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Manovich (2020) – How to See One Billion ImagesOpinion: only possible way to study the patterns, trends, and dynamics of contemporary
culture at that scale is to use data science methods
1. Looking at Culture with Computers
Public (big) data about cultural events: this perspective should allow us to create much more
detailed maps and timelines of contemporary culture than what is provided in existing studies
of culture industries or lists of cultural institutions → looking for social patterns
2. Cultural Analytics: Five Ideas
Cultural analytics includes:
- Practical: using methods from computer science, data visualization, and media art to
explore and analyse types of contemporary media / user interactions with them
- Theoretical: we asked how the use of such methods and large datasets of cultural
media challenges our existing modern ideas about culture and methods to study it
Five ideas of cultural analytics:
- Cultural analytics refers to the use of computational and design methods (e.g. data
visualization, media and interaction design, statistics, and machine learning) for
exploration and analysis of contemporary culture at scale
o Come up with new theoretical concepts appropriate for the scale, speed,
diversity, and connectedness of contemporary global digital culture
o New concepts should be not only theoretical but also qualitative: thinking
about the limits of such quantification and be sensitive to dimensions and
aspects of culture that existing measurements do not capture
- Use of numerical representation and data analysis and visualization methods offers a
new language for describing cultural artifacts, experiences, and dynamics
o Numbers and visualization also give us a language to represent gradual and
continuous temporal changes
o Numerical representations can better capture analog dimensions that
natural languages cannot describe adequately, such as motion or rhythm
- Particular attention to visual media, which still is a growing field
- Intention of cultural analytics is to augment our human abilities by providing new
interfaces and techniques for observing massive cultural datasets and flows
- Cultural analytics includes not only the application of currently available
computational methods for data analysis to cultural datasets and flows, but also critical
examination of these data science methods and their assumptions
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, 3. Cultural Analytics: Twelve Research Challenges
How to tackle quantitative versus qualitative data and how should measurements be put into
categories or types for generalisation and analysis → how is big data not overly deterministic
Paradigm: should we aggregate and reduce data or will this cause to much loss of diversity,
variability, and differences among numerous artifacts, behaviours, and individuals
4. What Cultural Analytics Is Not
Not only social media: also, numerous websites belonging to individual designers, cultural
centres, publications, art schools, museums, and analysing the content of culture-related blogs
5. Cultural Analytics, Media Theory, and Software Studies
Computational methods and large datasets do not automatically guarantee more objectivity
and inclusion → However, help us to confront our assumptions, biases, and stereotypes
Instead of trying to measure all through sampling from the population, cultural analytics
should focus on smaller sub-groups, specific geographic areas, or focused phenomena
- Not filter on any hashtags, take the top-likes, or separate categories
o Theorisation stems from narrow qualitative research questions, for example,
aiming on a certain paradigm which should be uncovered
6. Using this Book in Classes
Consider the workflow for doing a research, design, or artistic project with data:
- Think of how some subjects can be analysed or represented quantitatively
- Research what suitable data is available or how to generate it
- Assemble the data
- Use visual methods to explore this data
- Analyse the data using methods from statistics and data science
- Optionally, create interactive visualization tools for others to explore this data
2
, Piper (2016) – There Will Be Numbers
Computation plus culture: why is has data science to study culture become so important?
- Not simply computational science applied to culture → rethinking of methods
Currently four gaps (problems) reside into cultural analysis (CA):
1. Evidence Gap, New Generality
The point is not to single out any one study or discipline or theoretical school, but merely to
point out that absent computation all of these studies have a fundamental limitation. They are
all exiled from an understanding of the representativeness of their own evidence
What we see (in culture) is not a representation of reality, but it is represented reality →
Reality is always mediated through construction (how people percept the reality)
Generalisation: people are limited by intellectuality and therefore generalise with the
consequence that representation is always sub-optimal to wholistic representation
Therefore, as researcher we should reflect upon the representativeness of our evidence →
cultural analysts are self-conscious about being implicated in knowledge created in the world
In social science, representativeness is related to sample and bias
2. Theory Gap, New Explicitness
The gap in theory is not a gap of theory, of too little, but a gap from theory, of what comes next
We cannot know something at the general level as complexly as we can at the local level →
inverse relationship between number of things considered and complexity what can be known
Complexity is the aggregation of lower-level phenomena
Cultural analytics is much more abstract and objective than humanity studies, therefore
conclusions drawn are more top-down (objectivism) than bottom-up (interpretivism)
3. Self-Reflexive Gap, New Recursivity
In being more explicit, in documenting and theorising our practices more extensively, the
cultural analyst become more aware of hidden assumptions and buried beliefs
- Cultural analyst marks out the terrain of what one knows and thus does not know
4. Relevance Gap, New Impactfulness
Cultural analytics argues that there is no comfortable outside from which one can neutrally
observe culture; there is no space where on is not implicated → however, cultural analyst work
more transparent than cultural criticist, since criticist judge from their own representativeness
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