platforms and analytical gestures

Platforms and Analytical GesturesSocial Media Data Analysis with Digital Methods

Bernhard RiederUniversiteit van AmsterdamMediastudies Department

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.

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

Very large numbers and variety in users, contents, purposes, arrangements, etc.

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.

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."

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.


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."


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.

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 / formalization.

Standardization and formalization enhance calculability.

Platforms modulate visibility by using various ways of processing formalized entities to produce aggregates.

If outcomes are based both on platform characteristics and historically developed arrangements marked by diverse practices, how do we understand these objects / practices / dynamics?

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.

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.

“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?

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?

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.

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 purpose.

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.

There are counts everywhere, but anything here can be exploited for analysis.

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

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

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

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:

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 .

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)

Facebook Page "ElShaheeed", June 2010 – June 2011, (Poell / Rieder, forthcoming)

7K posts, 700K users, 3.6M comments, 10M likes (tool: netvizz), work in progress!

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.

Facebook Page "ElShaheeed", June 2010 – June 2011 page posts by type, per month

Facebook Page "ElShaheeed", June 2010 – June 2011 comment timescatter

Facebook Page "ElShaheeed", June 2010 – June 2011: comment timescatter, log10 y scale, likes on comments

Facebook Page "ElShaheeed", June 2010 – June 2011 comparison timeline: comments, posts, comments per post

Calculating relationships between variables

Quetelet 1827, Galton 1885, Pearson 1901

"Erosion of determinism" (Hacking 1991)

Facebook Page "ElShaheeed", June 2010 – June 2011 scatterplot comments / likes, with standard error

Facebook Page "ElShaheeed", June 2010 – June 2011: scatterplot comments / likes, per post type

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)

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.

Three different force-based layouts of my FB profile

OpenOrd, ForceAtlas, Fruchterman-Reingold

Non force-based layouts

Circle diagram, parallel bubble lines, arc diagram

Facebook Page "We are all Khaled Said", June 2010 – June 2011: bipartite network: posts (heat scale) / users (grey / black)

Facebook group membership network

European extreme right

Nine measures of centrality (Freeman 1979)

Facebook Page "Stop Islamization of the World"

How to place this into relation with other pages?

Facebook page like network

Seed: Stop Islamization of the World

Crawl depth: 2

Twitter 1% sample, 24 hours: 4.3M tweets, 3.4M users, 2M accounts mentioned, 227K unique hashtags (Gerlitz / Rieder 2013)

Twitter 1% sample, co-hashtag analysis

227,029 unique hashtags, 1627 displayed (freq >= 50)

Size: frequency

Color: modularity

Size: frequency

Color: user diversity



Size: frequency

Color: degree



Twitter 1% sample

Co-hashtag analysis

Degree vs. wordFrequency

Degree vs. userDiversity

Twitter 1% sample

Co-hashtag analysis

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!

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.

Thank You

[email protected]

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)

platforms and analytical gestures

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