<nettime> On Big Data and Post-Truth

[Prem Chandavarkar <prem.cnt@gmail.com>]

   [1] https://premckar.wordpress.com/2017/01/28/on-big-data-and-post-truth/

   On Big Data and Post-Truth

   In his book "A Mathematician Reads The Newspaper", John Allen Paolos
   writes on how an understanding of mathematics can change your
   understanding of daily news.  One of the examples he offers is to
   imagine the following (and plausible) scenario on gun control in
   America.  Opinion polls show that up to 80% of the public support some
   form of gun control.  Yet all politicians are reluctant to touch this
   subject.  Why?

   Paolos argues that that we naively believe that in a democracy the
   majority opinion carries the day.  However, he argues that it is not
   the question of the comparative numbers in the majority or minority,
   but how these numbers break down among themselves.  In this scenario on
   gun control, among the 20% who oppose gun control, three-quarters of
   them (NRA members, etc.) are so fanatical on the issue that they will
   make a voting decision purely on this one issue.  Three-quarters of 20%
   is 15% of the electorate.  Of the 80% who favour gun control, most of
   them do so among a vast spectrum of ethical or libertarian issues, and
   only 5% of them (who may have been provoked as victims, or closeness to
   victims, of gun-related crime) will make a voting decision solely on
   this issue. 5% of 80% is 4% of the electorate.  In terms of
   single-cause constituencies, you have 15% of the electorate on the
   minority side and 4% on the majority side.  The 11% difference will
   swing most elections, the politicians realise this, and therefore the
   reluctance to touch the issue.

   This means that democratic politics does not like diffuse majorities,
   and prefers the commitment of fanatical minorities.  Campaigns depend
   on generic promises that aim at attracting a certain mass base, and
   then courting the swing votes provided by fanatical minorities.  There
   was a time, until a few years ago, when any political campaign had to
   make a choice on a small number of these single-cause constituencies it
   wished to court as the swing votes.  And given the narrow range of
   choices, the ideology of the campaigner had to be clear, logically
   consistent, and perceptible by the general public.

   Has this changed in this era of big data, especially the sophistication
   of the analytics that exploits big data?  It is significant that a
   single British firm, Cambridge Analytica, was involved in two of the
   most globally significant political campaigns of 2016: the Brexit
   Referendum and the US Presidential Election, and worked for the winning
   side in both campaigns:  the Donald Trump campaign, and [2]leave.eu
   (one of the major groups in favour of Brexit).  For more on this see an
   article in The Spectator[3], a translation of an earlier German
   article found on the online journal AntidoteZine[4], and the firm's
   own website[5].   Significant common features of the Brexit and
   Trump campaigns were that both produced results that were not predicted
   by traditional polling techniques, and were criticised for not caring
   whether their public postulations had any foundation in objective facts
   or truth.

   Cambridge Analytica examines large data sets that were not available
   earlier, such as Google searches and Facebook likes and posts, to
   construct a personality model described by the acronym OCEAN (openness,
   conscientiousness, extraversion, agreeableness, and neuroticism) that
   predicts with surprising accuracy the choices that a person may make.
   With enough data, the analytical model can predict choices with greater
   accuracy than someone very close to the person whose personality is
   being mapped; or if the data set is large enough, even more accurately
   than the person himself/herself may be able to comprehend.

   With these new analytical models, the geography of single-cause
   constituencies can be mapped with a hitherto unparalleled accuracy,
   offering a new opportunity for electoral campaigns.  The problem is
   that the richness and fine grain of the data analytics reveals a set
   constituencies that can be quite diverse, and may have insufficient
   overlap for the articulation of a consistent ideology.  If the campaign
   wishes to mobilise as many of these fanatical groups as possible, it
   cannot afford to articulate a logically consistent position, and must
   base itself on a language that centres on the spectre of fear, raising
   emotions to a pitch where there is little concern for seeking
   validation in facts.  Perhaps, that is why the Oxford Dictionary has
   had to declare "post-truth" as the word of the year for 2016.[6]

   _______________________

References

   1. https://premckar.wordpress.com/2017/01/28/on-big-data-and-post-truth/
   2. http://leave.eu/
   3. http://www.spectator.co.uk/2016/12/the-british-data-crunchers-who-say-they-helped-donald-trump-to-win/
   4. https://antidotezine.com/2017/01/22/trump-knows-you/
   5. https://cambridgeanalytica.org/
   6. https://en.oxforddictionaries.com/word-of-the-year/word-of-the-year-2016

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