Social Media and Protest in
Algorithmic Societies
b
Jakob Svensson
Malmö University
Algorithms on the Agenda
Facebook manually controlling their algorithms (Tufekci, 2015) When firing its trending team weird outcomes (Thielman, 2016).
Is Amazon is homophobic? (Striphas, 2015)
Is Google is racist? (Allen, 2016)
Microsoft’s chat program Tay (Neff & Nagy, 2016) Gender biases in image search algorithms (Kay et al, 2015) Blacks are not recognized as humans in face-recognition (Sandvig et al, 2016) Twitters trending algorithm has for example been discussed in relation to the Occupy protests (whether Twitter/ their trending algorithm censored OWS)
the "Algorithmic
Turn"
Urrichio, 2011; Napoli, 2014
Algorithms replace editors (DeVito, 2016) to journalists (van Dalen, 2012), act as information intermediaries (Bozdag, 2013).
Facebook is the number one source of news about government and politics for a majority of the so-called “millennials” (Diakopoulus, 2016). Steiner (2012) argues that algorithms control financial markets, the music that reaches our ears, and even how we choose a partner.
Algorithms are responsible for selecting the information that reaches us (Gillespie, 2014), which has consequences for the shaping of our social and economic life (Kitchin, 2017).
the Algorithmic
turn
Algorithmically generated news feeds influence the issues on our agenda and how these issues are framed (Just & Latzer, 2016), which in turn influences our decisions, preferences and even election results (Tufekci, 2015).
Algorithm?
A modern myth (Barocas et al., 2013), term is sloppily used (Sandvig et al., 2016) and it is expanding (Gurevich 2012), "in front the screen imaginary" (Mansell, 2012)?
Algorithms = problem-solving technologies, calculate in steps (Kowalski 1979)
"arithmos” = number “al-jabr” = calculation
Algorithms as socio-material processes
a) input, the designing/programming (based around problems that need to be solved), which
b) leads to the formulation of one (or several) calculations (sort, filter, rank, profile users, weigh, Bozdag, 2013) which operate in a big-data context,
the Context
Big data-context: more than its size data that can be searched, aggregated
and triangulated with other sets of data (Shorey and Howard, 2016: 5033). “trace data” (Jungherr et al., 2016)
the Algorithmic Process / Situation
Input Calculation
Output
behind the screen in front of the screen big data
context
the Algorithmic Process
Algorithmic output is thus biased, not neutral even if the calculation itself is conducted with an allure of detached data speaking for itself
Algorithms embody social norms, values, imaginations, perceptions, rules, processes and are encoded with human intentions that may or may not be fulfilled (they are informed by logics on different levels)
Silicon Valley is “incredibly white and male” (Yarow, 2015) Hacker culture / ethics (Levy, 2010)
Algorithmic Society?
Algorithmic societies are full of algorithmic processes / situations
Algorithmic societies are societies in which we produce data / leave data behind that can be mined/ calculated for different purposes
But algorithms have to be formulated / engineered. At the core of this are problems that there is a believe that big data calculations /mining (i.e. algorithms) can solve
Who formulates these, how to formulate such problems?
Social media's algorithms seek to project the future / project users needs (on the basis of past behavior/traces) to serve advertisers, informing the business models of the social media companies who owns the algorithms.
... and activism?
the pessimist's story:
Everything is commercially driven
We don't even know the algorithm, have get to it by "reverse engineering"
By using social media, activists feed the monster and contribute to big/ thick/ trace data that can be
mined to serve the commercial logics of social media The push for updating lead to activism centered
around expressing identities leads to activism that is mostly the expressive and performative
... and activism?
the optimist's story:
Information spread – not only for the sake of identity negotiation/
expression/ performance, but also for mobilizing, connecting and engaging other groups and networks. A new type of connective action and new forms of activist collectives
the Battle for the
Bathhouse
middle class / bohemian bourgeosie / red wine left / green-party strong hold
Values:
of location bound communitys
of being active (proactive rather than reactive)
of connectivity and responsiveness Core-periphery positons:
who engaged/ updated others?
who was updated/ engaged by others Habitus:
Anlimal rights movements, cinema Tellus, student councils/nations, scout movement, artist collectives, belief in change "another world is possible"
In-group polarisation – filter bubble ≠ deliberation (attract/ mobilise like-minded and mock opponents)
The algorithmic bias towards the popular: stratifying / making clear hierarchies in the group
Bias towards the new
favouring the expressive "I was here" kind of participation – update and show that you were there ("participation capital" Svensson 2014)
Connection Information/Communication Action
Being "mobilisable" – on standby (Amnå & Ekman, 2013) and able/ allowed to mobilise (mobilising capital, Svensson 2014)
bridging – switching – creation of alliances – intermediaries ("connecting and engaging" capital, Svensson 2014)
Concluding toughts
opportunity structures (Kitchelt / Cammaerts) algorithmic opportunities for activists? "algorithmic activism"
data activism (Milan)
data justice (Dencik, 2015, Dencik et al., 2016) techno-politics
cyber detournement – 15 M spain – Twitter maffia Technological awareness – algorithmic literacy
a) crack the algorithm
