TY  - GEN
N2  - A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data.How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture--the terabytes of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Sound Cloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data, with a particular focus on visual media. Drawing on more than a decade of research and projects from his own lab, Manovich--the founder of the field of cultural analytics--offers a gentle, nontechnical introduction to selected key concepts of data science and discusses the ways that our society uses data and algorithms.Manovich offers examples of computational cultural analysis and discusses the shift from new media to more media; explains how to turn cultural processes into computational data; and introduces concepts for exploring cultural datasets using data visualization as well as other recently developed methods for analyzing image and video datasets. He considers both the possibilities and the limitations of computational methods, and how using them challenges our existing ideas about culture and how to study it. Cultural Analytics is a book of media theory. Arguing that before we can theorize digital culture, we need to see it, and that, because of its scale, to see it we need computers, Manovich provides scholars with practical tools for studying contemporary media.
AB  - A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data.How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture--the terabytes of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Sound Cloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data, with a particular focus on visual media. Drawing on more than a decade of research and projects from his own lab, Manovich--the founder of the field of cultural analytics--offers a gentle, nontechnical introduction to selected key concepts of data science and discusses the ways that our society uses data and algorithms.Manovich offers examples of computational cultural analysis and discusses the shift from new media to more media; explains how to turn cultural processes into computational data; and introduces concepts for exploring cultural datasets using data visualization as well as other recently developed methods for analyzing image and video datasets. He considers both the possibilities and the limitations of computational methods, and how using them challenges our existing ideas about culture and how to study it. Cultural Analytics is a book of media theory. Arguing that before we can theorize digital culture, we need to see it, and that, because of its scale, to see it we need computers, Manovich provides scholars with practical tools for studying contemporary media.
T1  - Cultural Analytics /
CN  - HV551.2
ID  - 1386293
KW  - Culture
KW  - Culture
KW  - Mass media
KW  - Mass media
KW  - Information visualization.
KW  - DIGITAL HUMANITIES & NEW MEDIA/General
KW  - DIGITAL HUMANITIES & NEW MEDIA/Software Studies
KW  - SOCIAL SCIENCES/Media Studies
SN  - 9780262037105
SN  - 0262037106
SN  - 0262360640
SN  - 9780262360647
TI  - Cultural Analytics /
LK  - https://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/11214.001.0001?locatt=mode:legacy
LK  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
UR  - https://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/11214.001.0001?locatt=mode:legacy
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -