Becoming data point
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Becoming datapoint Transmediale 2015 – Capture All Carolin Gerlitz - University of Amsterdam (based on joint work with Bernhard Rieder UvA)
Transcript of Becoming data point
Becoming data-‐point
Transmediale 2015 – Capture All
Carolin Gerlitz - University of Amsterdam (based on joint work with Bernhard Rieder UvA)
Which data matters? • Data capture critique focuses on
calculation (Callon & Muniesa 2005): the recombination of data-points.
• Not individual data-points matter, but the relations that can be created between them (Mackenzie 2012).
• But what do the initial data-points make countable and comparable in the first place?
Making life commensurable
• First order metrics (Power 2004) : likes, tweets, shares, pins, comments.
• Second order metrics: scores, recommendations, rankings, sentiment, dashboards.
• Commensuration allows to transform non-comparable qualities into common metric (Espeland & Stevens 1998).
• Similarity of data is not a property.
Delegating commensuration
• 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.
• But countability ≠ equivalence.
Empirical data-point critique
• How to use digital research methods not to repurpose but to re-embed data-points?
• Ongoing project on 1% random Twitter sample with Bernhard Rieder (2013, 2014).
• Metrics are epistemic devices.
• What do metrics not show? What are they animated by?
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 hashtags
• Hashtags can take on different functions: shout-out, frame (Gerlitz & Rieder 2013); can be used by different social formations (Bruns & Stieglitz 2013).
• Understudied metric: device/source.
• Device as possible intervening variable (Gerlitz & Rieder 2014)?
• 1.iPhone, 2.Android 3.Web
• Specific devices cater to specific hashtags in 1% sample.
iPhone
Tweetdeck
Instagram Tribez
Tweetadder
Web
Hashtags per device
iPhone
Tweetadder
De- & recomposing metrics #iraq
De- & recomposing metrics #callmecam
De- & recomposing metrics #gameinsight
De- & recomposing metrics #love
The happening of commensuration
• Commensuration is not enacted by the metric itself.
• Distributed accomplishment: use practices, platform interoperability, hijacking, spam, humans, bots.
Conclusion: Lively metrics
• We do not count hashtags, we calculate (detach and order) them (Callon & Muniesa 2005).
• Social media first order metrics like hashtags or tweets are lively metrics that invite users to write themselves into them.
• Animated by distributed actors.
• Data-point critique: public debate about what metrics make similar and calculable.
