Session 2: Approaches to analyzing cultural data: digital humanities, computer science, and cultural analytics Lev Manovich The Graduate Center, CUNY Computer Science I have defined the concept of "cultural analytics" in 2007. Eight years later, many thousands of researchers have already published hundreds of thousands of papers analyzing patterns in massive cultural datasets. First of all, this is data describing the activity on most popular social networks (Flickr, Instagram, YouTube, Twitter, etc.), user created content shared on these networks (tweets, images, video, etc.), and also users’ interactions with this content (likes, favorites, reshares, comments). Second, researchers also have started to analyze particular professional cultural areas and historical periods, such as website design, fashion photography, 20th–century popular music, 19th–century literature, etc. This work is carried out in two newly developed fields – Social Computing and Digital Humanities. What are the differences in methods and results between Social Computing and Digital Humanities. How do the key questions in computational culture analysis are being addressed? What does it mean to represent “culture” by “data”? What are the unique possibilities offered by computational analysis of large cultural data in contrast to qualitative methods used in humanities and social science? How to use quantitative techniques to study the key cultural form of our era – interactive media? How can we combine computational analysis and visualization of large cultural data with qualitative methods, including "close reading”? (In other words, how to combine analysis of larger patterns with the analysis of individual artifacts and their details?) How can computational analysis do justice to variability and diversity of cultural artifacts and processes, rather than focusing on the "typical" and "most popular"?