What  counts  in  social  media?  

Politics of Big Data – Conference & Masterclass Kings College, May 08 2015

Dr. Carolin Gerlitz - University of Amsterdam

Which data matters? •  Data critique often focuses on

calculation (Callon & Muniesa 2005): the recombination of data-points.

•  Second order metrics: scores, recommendations, rankings, sentiment, derrivatives, dashboards.

•  But what do the first order metrics that feed such composite metric make countable and comparable in the first place?

•  Based on joint work with Bernhard Rieder.

Becoming data-point

•  Empirical research: ex-post classification.

•  Digital media come with specific grammars of action (Agre 1994) which invite & capture user action in a standardised form.

•  Grammars naturalise distinct use practices into comparable data points, making heterogeneous qualities countable and commensurable (Espeland & Stevens 1998).

•  Activities can come with different intentions (Gerlitz & Helmond 2013).

•  Interpretative flexibility build into platforms (van Dijck 2012) allows for resignification & transformation.

•  Multiple meanings may lead to more data.

One number, multiple meanings

•  Platforms are increasingly being accessed through clients, automators, mobile interface or cross-syndication practices.

•  Platform-interoperability (Bodle 2012) & programmability: allow for various ways of engaging with and producing content.

One number, many platforms

Repurposing digital methods

•  What lures behind social media metrics and what animates them?

•  How to use digital research methods not to repurpose but to re-embed first order metrics?

•  Example: Twitter.

•  Twitter Capture & Analysis Toolkit (DMI-TCAT).

1% sample

•  Ongoing project on 1% random Twitter sample with Bernhard Rieder (2013).

•  Retrieved via Twitter Streaming API.

•  1% sample as cross-section on Twitter practices.

Links

Hashtags

The Data Set1% Random 1% sample 14-20. June 2014

Mentions

Retweets

Replies

16.8

15.8

58.1

32.9

18.2

Tweets

Users

31.707.162

14.313.384

Decomposing metrics

•  Starting point: source metric.

•  Proliferation of access points to Twitter: web, mobile, clients, automators, cross-syndication, custom clients.

•  72.000 sources in our sample.

iPhone

Tweetdeck

Instagram Tribez

Tweetadder

Web

Hashtags per source

iPhone

Instagram

Tweetadder

De- & recomposing metrics #iraq

De- & recomposing metrics #gameinsight

De- & recomposing metrics #love

•  More nuanced account of non-human activity beyond the notion of ‘bots’ (Wilkie et al. 2014).

•  Organic & automated content: cross-syndication, scheduled tweets, in-game tweets, automated action, bots accounts.

•  Approach to automatisation beyond data-cleaning.

Dealing with the non-human

•  Sources allow for different regimes of being on Twitter: alternative use practices, grammars & politics.

•  Data-formats/practices of Twitter informed by data-formats of third parties.

•  Platform-interoperability (Bodle 2012) & -programmability: technique of commensuration.

Dealing with platform ecologies

The happening of commensuration

•  Commensuration not only a media or metric effect.

•  Distributed accomplishment: use practices, platform interoperability, hijacking, spam, humans, bots.

•  ‘Happening’ (Lury & Wakeford 2012): relational, dynamic, distributed.

Lively metrics •  What a metric counts is not

predefined by comparable grammars of action.

•  Subject to distributed accomplishment, invite users & third parties to write themselves into them.

•  Lively metrics: realised differently, subject to change, happening.

•  What counts? Non-objective, dynamic & situated.

•  What can be counted counts (Badiou 2008): need for debates on commensuration.

Thank you.

c.gerlitz@uva.nl