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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.
• 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.