Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Platforms and Analytical Gestures

4,424 views

Published on

Talk at the Digital Methods Summer school 2013 on June 24.

Published in: Education, Technology, Business
  • Be the first to comment

  • Be the first to like this

Platforms and Analytical Gestures

  1. 1. Platforms and Analytical Gestures Social Media Data Analysis with Digital Methods Bernhard Rieder Universiteit van Amsterdam Mediastudies Department
  2. 2. Introduction Social media are taking an important place in contemporary life. Besides other things, they are discussed as data collections – in relation to terms like big data, computational social science, digital methods, database marketing, surveillance, social sorting, etc. Many disciplines have begun to study social media, applying various methodologies, but there is an explosion in data-driven research. The promise is (cheap and detailed) access to what people do, not what they say they do; to their behavior, exchange, ideas, and sentiments.
  3. 3. This presentation How do we talk about social media data? How do we analyze them? What is our frame of thought? How do we go further in terms of methodological imagination and expressivity? Instead of a totalizing search for a "logic" of data analysis, we could inquire into the rich vocabulary of analytical gestures that constitute the practice of data analysis. Social media data analysis using digital methods (Rogers 2013): 1. Characteristics of social media 2. Analytical gestures 3. Some examples
  4. 4. Very large numbers and variety in users, contents, purposes, arrangements, etc.
  5. 5. Social media are built on simple point-to-point principles; this allows for a wide variety of topological arrangements to emerge over time. There is no average Twitter user. But every account is also the same.
  6. 6. Platforms like Twitter provide opportunities for creating connections between defined types of entities (users, messages, hashtags, resources, etc.). They formalize and channel expression, exchange, and coordination. "You cannot reply to a hashtag."
  7. 7. The Web vs. Social Media The Web "natively" only knows one type of entity (the web page) and one type of connection (the hyperlink). "The Web does not know what a blog is." Technical formalization is very unspecific in terms of user practices.
  8. 8. The Web vs. Social Media Social media define sets of distinct entities as well as distinct types of connection. Technical formalization is explicitly related to specific use practices. "Social media are formal, the Web is conventional." "Facebook knows that a song is."
  9. 9. The Web vs. Social Media The open Web is difficult to study because of the separation between technical markers and meaning. A link is not a like. The more detailed the formalization, the more salient the data. Social media platforms are essentially large databases.
  10. 10. Social media are built on simple point- to-point principles; but elements are dynamically aggregated into lists. Social media platforms organize exchange around market forms of interaction; topological arrangements result from histories of exchange and technological mediation /
  11. 11. Standardization and formalization enhance calculability. Platforms modulate visibility by using various ways of processing formalized entities to produce aggregates.
  12. 12. If outcomes are based both on platform characteristics and historically developed arrangements marked by diverse practices, how do we understand these objects / practices / dynamics?
  13. 13. Social media produce detailed data traces; data pools in social media are centralized and searchable. Access to these proprietary data is governed by technical (API) and legal means (EULA). Structure of APIs is closely related to given formalizations.
  14. 14. Data analysis for social media, first recommendation: in order to select, process, and interpret data in a meaningful way, we need to understand the platform: entities, relations, modes of aggregation.
  15. 15. “facts and statistics collected together for reference or analysis. See also datum. - Computing: the quantities, characters, or symbols on which operations are performed by a computer, being stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media. - Philosophy: things known or assumed as facts, making the basis of reasoning or calculation.” (Oxford American Dictionary) Define: data Reasoning (OAD): "think rationally", "use one's mind", "calculate", "make sense of", "come to the conclusion", "judge", "persuade", etc. Reasoning as "giving reasons": What counts as a finding? As a valid argument or method? What is "good" knowledge? How do we reason on the basis of data?
  16. 16. What styles of reasoning? Hacking (1991) builds the concept of "style of reasoning" on A. C. Crombie’s (1994) "styles of scientific thinking": ☉ postulation and deduction ☉ experiment and empirical research ☉ reasoning by analogy ☉ ordering by comparison and taxonomy ☉ statistical analysis of regularities and probabilities ☉ genetic development These are styles of "giving reasons", styles of making truth and knowledge. What kind of reasoning are we mobilizing in data analysis? Is this simply quantitative empiricism, counting facts?
  17. 17. Quality / quantity "One of my favorite fantasies is a dialogue between Mills and Lazarsfeld in which the former reads to the latter the first sentence of The Sociological Imagination: 'Nowadays men often feel that their private lives are a series of traps.' Lazarsfeld immediately replies: 'How many men, which men, how long have they felt this way, which aspects of their private lives bother them, do their public lives bother them, when do they feel free rather than trapped, what kinds of traps do they experience, etc., etc., etc.'." (Maurice Stein, cit. in Gitlin 1978) Theory vs. empiricism, macro vs. micro, qualitative vs. quantitative, inductive vs. deductive, confirmatory vs. exploratory, understanding vs. explaining, etc. The promise of data analysis, applied to exhaustive (and cheap) data, is to bridge the gap between different epistemic stances, e.g. "quali-quanti" (Latour 2010). We need to think creatively about new analytical gestures rather than slavishly follow sterile methodological paradigms.
  18. 18. Flusser (1991) describes gestures as having convention and structure, but also as different from reflexes, because translating a moment of freedom. It is an "art of doing" (de Certeau 1980), a movement of the body that has no sufficient causal explanation. We investigate the structure of data by creating "views" of the data. The notion of gesture indicates that data does not speak for itself, we approach it with particular epistemic techniques (methods) related to a sense of purpose, a "will to know" (Foucault 1976). Analytical gestures Data analysis for social media, second recommendation: in order to select, process, and interpret data in a meaningful way, we need to be clear about our
  19. 19. Where are analytical gestures? Analytical gestures produce orderings, lists, tables, charts, etc. that are considered to be saying something about the data / phenomenon. Analytical gestures mobilize the analytical capacities of: ☉ The platforms (formalization, aggregation) and their users (appropriation, use) ☉ The analytical tools and methods we use ☉ The researchers and their imagination, knowledge, and skill We need to think all three levels together.
  20. 20. There are counts everywhere, but anything here can be exploited for analysis.
  21. 21. Social media platforms are full of analytics: counts, rankings, trends, recommendations, groupings, similarities, and so on. How can we repurpose them for research? Example 1: the platform
  22. 22. Step 1: Retrieve list of friends from Facebook account Step 2: Check for friendship between each pair of users Step 3: Project as network – use friendship as link Step 4: Spatialize network (use structure to arrange nodes) Step 5: Detect communities (use structure to distinguish groups) Step 6: Size and color nodes Step 7: Interpret Friendship analysis of my personal FB network: Nodes: users / Links: "being friends" Example 2: the methods and tools
  23. 23. Co-like analysis of my personal FB network: Nodes: users / Links: "liking the same thing" Step 1: Retrieve list of friends from Facebook account. Step 2: Retrieve liked entities for every user. Step 3: Project as network – if two users like the same object, create a link; for every other mutual like add weight to connection. Step 4: Spatialize network (use structure to arrange nodes) Step 5: Detect communities (use structure to distinguish groups) Step 6: Size and color nodes Step 7: Interprete Example 3: our imagination
  24. 24. Analytical gestures Methodological plasticity requires imagination, rigor, and self-criticism. Example: the arithmetic mean (most common form of average) is supposed to reveal a "central tendency" in a distribution:
  25. 25. Three (more) things to consider about data The technical shape of data: data in relation to the social media platform ☉ Variety of "units": users, accounts, pages, groups, lists, messages, hashtags, words, etc. (different forms of materiality, different analytical opportunities, etc.) The social / cultural shape of data: data in relation to lived experience ☉ Demographic (age, sex, income, etc.) ☉ Post-demographic (taste, expression, etc.) ☉ Behavioral (trajectories, interaction, etc.) The modes of analysis: the tools / methods (and their mathematics) ☉ Statistical (case centered) perspective ☉ Relational (structure centered) perspective Data analysis for social media, third recommendation: in order to select, process, and interpret data in a meaningful way, we need to be knowledgeable about methods and the concepts they are based on.
  26. 26. Two kinds of mathematics Statistics Observed: objects and properties ("cases") Data representation: the table Visual representation: quantity charts Inferred: relations between properties Grouping: class (similar properties) Graph-theory Observed: objects and relations Data representation: the matrix Visual representation: network diagrams Inferred: structure of relations between objects Grouping: clique (dense relations)
  27. 27. Facebook Page "ElShaheeed", June 2010 – June 2011, (Poell / Rieder, forthcoming) 7K posts, 700K users, 3.6M comments, 10M likes (tool: netvizz), work in progress!
  28. 28. Date captured from an API can be easily imported into standard statistical tools that come with many analytical gestures built in (e.g. R, Excel, SPSS, Rapidminer, …). Statistics Data analysis for social media, fourth recommendation: in order to select, process, and interpret data in a meaningful way, we need to be able to use tools skillfully and with a degree of awareness of how they work.
  29. 29. Facebook Page "ElShaheeed", June 2010 – June 2011 page posts by type, per month
  30. 30. Facebook Page "ElShaheeed", June 2010 – June 2011 comment timescatter
  31. 31. Facebook Page "ElShaheeed", June 2010 – June 2011: comment timescatter, log10 y scale, likes on
  32. 32. Facebook Page "ElShaheeed", June 2010 – June 2011 comparison timeline: comments, posts, comments per post
  33. 33. Calculating relationships between variables Quetelet 1827, Galton 1885, Pearson 1901 "Erosion of determinism" (Hacking 1991)
  34. 34. Facebook Page "ElShaheeed", June 2010 – June 2011 scatterplot comments / likes, with standard error
  35. 35. Facebook Page "ElShaheeed", June 2010 – June 2011: scatterplot comments / likes, per post type
  36. 36. Two kinds of mathematics Statistics Observed: objects and properties Inferred: relations Data representation: the table Visual representation: quantity charts Grouping: class (similar properties) Graph-theory Observed: objects and relations Inferred: structure Data representation: the matrix Visual representation: network diagrams Grouping: clique (dense relations)
  37. 37. 3 / The mathematics of structure Graph theory has a long prehistory; social network analysis starts in the 1930s with Jacob Moreno's work. Graph theory is "a mathematical model for any system involving a binary relation" (Harary 1969); it makes relational structure calculable.
  38. 38. Three different force-based layouts of my FB profile OpenOrd, ForceAtlas, Fruchterman-Reingold
  39. 39. Non force-based layouts Circle diagram, parallel bubble lines, arc diagram
  40. 40. Facebook Page "We are all Khaled Said", June 2010 – June 2011: bipartite network: posts (heat scale) / users (grey / black)
  41. 41. Facebook group membership network European extreme right
  42. 42. Nine measures of centrality (Freeman 1979)
  43. 43. Facebook Page "Stop Islamization of the World" How to place this into relation with other pages?
  44. 44. Facebook page like network Seed: Stop Islamization of the World Crawl depth: 2
  45. 45. Facebook page like network Seed: Stop Islamization of the World Crawl depth: 2
  46. 46. Facebook page like network Seed: Stop Islamization of the World Crawl depth: 2
  47. 47. Facebook page like network Seed: Stop Islamization of the World Crawl depth: 2
  48. 48. Twitter 1% sample, 24 hours: 4.3M tweets, 3.4M users, 2M accounts mentioned, 227K unique hashtags (Gerlitz / Rieder 2013)
  49. 49. Twitter 1% sample, co-hashtag analysis 227,029 unique hashtags, 1627 displayed (freq >= 50) Size: frequency Color: modularity
  50. 50. Size: frequency Color: user diversity Twitter 1% sample, co-hashtag analysis 227,029 unique hashtags, 1627 displayed (freq >= 50)
  51. 51. Size: frequency Color: degree Twitter 1% sample, co-hashtag analysis 227,029 unique hashtags, 1627 displayed (freq >= 50)
  52. 52. Twitter 1% sample Co-hashtag analysis Degree vs. wordFrequency
  53. 53. Degree vs. userDiversity Twitter 1% sample Co-hashtag analysis
  54. 54. Recommendations Data analysis for social media requires (in my view): ☉ Robust understanding of the social media platform ☉ A sense of purpose ☉ Conceptual understanding of methods and analytical gestures ☉ Knowledge of software tools for data analysis Also: from the simple to the more complex, start out with platform metrics and counting; prefer simple visualizations to complex ones. Finally, let's not forget domain expertise!
  55. 55. Conclusions There is a lot of excitement about social media data analysis, but our understanding of styles and analytical gestures is still very poor. We need interrogation and critiques of methodology that are developed from engagement and historical / conceptual investigation. We need analytical gestures that are more closely tied to concepts from the humanities and social sciences. Visualization and simpler tools are very interesting but require technical and conceptual literacy to deliver more than (deceptive) illustrations.
  56. 56. Thank You rieder@uva.nl https://www.digitalmethods.net http://thepoliticsofsystems.net "Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise. Data analysis must progress by approximate answers, at best, since its knowledge of what the problem really is will at best be approximate." (Tukey 1962)

×