Page 55 - AC/E Digital Culture Annual Report

sense, they can be even seen as a product of the Social Media platforms, which have user data and established communication links to the end users, and know a lot about users’ behavior.
Big Social Data, algorithms and artificial intelli- gence rely on one fundamental thing: collecting and analyzing user information coming from activities of private citizens. The EU’s new GDPR will give citizens more insight into and control of their digital information. It could also increase users’ trust in digital services and create a level playing field for companies that responsi- bly monetize consumers’ data.
The constant production and storing of large amounts of personal data in the Social Media platforms also has a sinister counterpart. Governments are used to monitoring citizens in order to ensure their security. Nothing new here, this is a part of history – remember that being watched existed before Social Media platforms. But in the modern state people know that they are watched yet, at the same time, they reveal even their deepest secrets by posting their statuses on Facebook.
Having large amounts of data and effective algo- rithms, analytical and statistical methods, do we need theories, models, hypothesis, sampling and surveys of small groups? According to Anderson, in the era of the PetaByte scientific methods are obsolete (Anderson, 2008). According to many others, it is hard to believe that analyzing large amounts of data could make scientific models
useless. In the business world, the idea of “Da- ta-driven Enterprises” has already been accepted with so-called Business Intelligence tools being applied to internal data (all kind of enterprise data) and external data (coming from external sources including Social Media platforms). Enterprises are being forced to reconsider their organisation and business processes taking into account the availability of internal and external data, which could be transformed into a compet- itive advantage in a data-driven market.
The constant production and storing of large amounts of personal data in the Social Media platforms also has a sinister counterpart.
What about Societal Intelligence applied to Big Social Data? How can handling the large amount of Big Social Data, gathered by Social Media platforms, in a sort of Big Social Data Lake help us gain a better understanding of our society, our general principles and patterns and our culture, and help us improve (for good) our lives?
A group of authors (De Mauro A., Greco M., Grimaldi M., 2016) analyzed definitions of “Big Data” provided by various researchers and major technology companies (e.g. Oracle, Intel, IBM, Microsoft). They observed that the definitions provided so far might be classified according
to four groups, depending on where the focus has been put in describing the phenomenon. The first group concentrate on attributes of data – classic definitions based on Vs – Volume, Velocity, Variety, Veracity and Value, the second and third on technology needs and overcoming thresholds, and the fourth on social impact. In general, the definitions combine two important ideas: storage of a large volume of data and ana- lyzing this data quantitatively and finding pat- terns and predicting conduct. The volume-based definition of Big Social Data only makes sense if we consider the social media to be a source of data and not a “place for data” (Lake) where it
is possible to aggregate otherwise banal experi- ences into a quantifiable entity.
AC/E DIGITAL CULTURE ANNUAL REPORT 2018
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Digital Trends in Culture