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What Does Data Want? (2019-2020)

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Dr. Lev Manovich

Professor of Computer Science, The Graduate Center, City
University of NewYork / Director, Cultural Anal...

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- How to use big cultural data without aggregation and
summarization?

- How to think without (traditional) categories?

-...

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1960 - Born in Moscow


1981 - came to NYC

1982-1985: BA (NYU Film School)



1986-1988: MA inVision Science

1989-1993: ...

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What Does Data Want? (2019-2020)

  1. 1. Dr. Lev Manovich
 Professor of Computer Science, The Graduate Center, City University of NewYork / Director, Cultural Analytics Lab
 lab.culturalanalytics.info 
 email: manovich.lev@gmail.com What Does Data Want? (answer: help humans to think without categories) 

  2. 2. - How to use big cultural data without aggregation and summarization?
 - How to think without (traditional) categories?
 - How to learn from computers to understand the world differently? - How to work with big data without numbers?
  3. 3. 1960 - Born in Moscow 
 1981 - came to NYC
 1982-1985: BA (NYU Film School)
 
 1986-1988: MA inVision Science
 1989-1993: PhD inVisual Culture
 1992-2012: Professor of Digital Art
 
 2013 - Professor of Computer Science
  4. 4. 1973 - started art lessons 1975 - started learning computer programming
 1978 - how images are structured? How they communicate?
 1984 - started working in GGI 
 1986 - took classes in computer vision
 2005 - the idea of cultural analytics
 2007 - established Cultural Analytics Lab (UCSD)
 2020 - Cultural Analytics book is published
  5. 5. Using data science to analyze contemporary culture:
 
 Companies - marketing research, consumer preferences, new product development, analysis of online and physical behaviors Non-profits - museums, universities, etc. AAI , Network science, many areas of Computer Science
 Computational social science
 Communication studies
 Political science, Psychology, Sociology
 Urban planning, Urban Studies Data visualization, data design, data art
 Digital Humanities (sometimes)
  6. 6. Research examples:
 
 "Cultural diffusion & trends in Facebook photographs" (2017)
 "StreetStyle: Exploring world-wide clothing styles from millions of photos" (2017)
 "Why the songs of the summer sound the same"(2018) "Neuroaesthetics in Fashion: Modeling the Perception of Fashionability”(2015) Every Noise at Once (2013) "Quantifying reputation and success in art" (2018)

  7. 7. What is “Culture”? Humanities vs Cultural Anthropology
 Humanities: culture is material artifacts, texts, and media objects created by small number of authors
 Cultural Anthropology: culture is behaviors, symbols, rituals, values, beliefs; looking at society as a whole
 
 For contemporary culture, it seems easy to combine both perspectives using data - e.g. SM is both artifacts & behaviors. But how informative are these behaviors?
  8. 8. Analyzing Culture: Digital Humanities vs Computer Science
 DH (mostly): 
 - analyzes the historical artifacts by professional creatives 
 Computer Science (mostly):
 - analyzes contemporary artifacts & behaviors of ordinary people (e.g. SM posts, images, video, online and physical behaviors by billions of “normal”users)
  9. 9. Cultural analytics: using data methods to see contemporary global culture (2005-): inspiration: cientometrics, evolutionary biology, GIS Research goals:
 1) What are the themes, styles, behaviors and their patterns in contemporary global culture? 2) Where are they active? (spatial distributions)
 3) When they emerge, how they diffuse, change over time?
 
 We now have enough data to map some of this at relatively high resolution - but -
  10. 10. The main challenge in studying contemporary culture with data science (as I see it): - Shall we aggregate big cultural data and reduce it to small set of patterns - frequently occurring ideas, themes, styles, patterns, behaviors frequent in the data? (Statistical paradigm - standard today in data science and data-driven research). 
 
 - This paradigm focuses on what is common between a number of objects; does not include what occurs infrequently.
  11. 11. - Or shall we refuse this dominant paradigm instead focusing on diversity, variability & differences (including tiny ones)? - i.e. work on big cultural data without aggregation)?
 
 - include everything
 - pay attention to infrequent (but not outliers)
 - identify small cultural islands (that usually disappear when researchers use dominant paradigm) - question similarity (categories, clusters, dimension reduction, etc.)
  12. 12. High-resolution data allows us to think outside of the dominant intellectual paradigm of the modern period: aggregation, reduction, categorization
 Individualization paradigm in the media/data industry: - Ad platforms making predictive models for each user (even for different times of the day) & custom recommendations - Search engines indexing every webpage they can find - Spotify and other companies analyzing every music track (40M+)
  13. 13. Examples of the dominant paradigm in data-driven culture analysis:
 
 Cultural Diffusion andTrends in Facebook Photographs (2014): “We are interested in recognizing many different types of cultural lifestyles or activities in photographs…we select the most common concepts” “…asked annotators to describe the main visible concepts of images using a few keywords” “After pruning infrequent keywords..”
  14. 14. 
 “Faces Engage Us: Photos with Faces Attract More Likes and Comments on Instagram” (2014): 
 
 “Our dataset consists of 23 million Instagram photos and over 3 million Instagram users…we randomly selected 1 million photos from this data set.“ "the existence of a face in a photo significantly affects its social engagement.This effect is substantial, increasing the chances of receiving likes by 38% and comments by 32%. "
  15. 15. “Exploring world-wide clothing styles from millions of photos” (2017)
 
 Paper goals: “- Identify common, visually correlated combinations of these basic attributes (e.g., blue sweater with jacket and wool hat). - Identify styles that appear more frequently in one city versus another or more frequently during particular periods of time. - Identify finer-grained, visually coherent versions of these ele- ments (e.g., sports jerseys in a particular style)."
  16. 16. Examples of style clusters
  17. 17. “GeoStyle: Discovering Fashion Trends and Events (2019)
 
 “Most attribute combinations are uninteresting because of their rarity: e.g., pink, short-sleeved, suits. We want to focus on the limited set of attribute combinations that are actually prevalent in the data.”“
  18. 18. Supervised vs unsupervised machine learning for seeing culture:
 
 -supervised machine learning use for classification: start with existing categories (defined by experts, or by “common sense”) and then classify the rest of the data / new data using these categories. Using neural nets only makes this problem bigger.
  19. 19. Cultural analytics - how my vision changed over time 
 - We want to challenge existing categories; ask if rigid categories make sense for a particular cultural field; discover its structure (2007)
 - Unsupervised machine learning is well suited for these goals; but success depends on how we represent a phenomenon as data, what features we use (2010) - But unsupervised machine learning also requires aggregation, as classical statistics - how to avoid this? (2016)
  20. 20. - Data paradigm offers a new language for describing and thinking about culture
 - Numerical (continuous) scales instead of (verbal categories) - Representing continuous change over time - Representing differences between cultural artifacts and actors as numerical distances in feature space - Detecting clusters instead of starting with already existing categories - In a cluster any object has a particular distance to the center (in traditional categories its either/or membership) - But there are still key challenges -
  21. 21. The problems with representing cultural artifacts using numerical features: - how do we know we have the right features? - we don’t know how brain combines visual features - gestalt theory - the whole is not a mechanical part of the parts - many images may be identical from statistical point of view, and yet they have crucial differences for a human observer - tiny differences that make a difference
  22. 22. - Next slides: examples of Instagram photography (2005-2016) - Can data science and AI today capture all the differences between these artifacts - between authors’ visions and the differences all individual photographs? (in content, visual language, mood, emotions - and all of this together for each image - because this is how many people see) - Every person may see each artifact differently depending on her background, education, knowledge of codes, what she seen before, etc. Can data approach capture such variability? (recommendation engines research?)
  23. 23. - Cultural Analytics vision (2007-2008) - Examples of projects from our lab (2009-2015)
  24. 24. Elsewhere project (2018-) - Instead of using social networks data (posts by individuals), we use information about cultural events shared by organizations on different platforms. - During last 20 years, the numbers of these places and events have become so large that we can now to treat them as “big data.” - Examples of data sources: TEDx events, e-flux archive, Meetup, Behance. Our dataset: 4.5 million events
  25. 25. Elsewhere project (2018-) - using locations, categories, dates and text descriptions of millions of cultural events 1) What is the presence of some of contemporary culture (CC) - as represented by our data sources - on a world map today? What is the density and depth of this presence in different places? Are there still big white spots? 2) What is the temporal growth and diffusion of CC (1990 -) ? 3) What are the topics, concepts and themes in CC? What occurs everywhere, what is only somewhere, what is elsewhere (outside of top cities), what is unique?
  26. 26. Thank you!
 Questions, comments, collaboration:
 manovich.lev@gmail.com
 
 Projects and publications:
 lab.culturalanalytcs.info
 manovich.net 
 


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