Cyber Crime and Cyber Terrorism

CHAPTER
175
14
Social media and Big Data
INTRODUCTION
Social media represent an increasing and fundamental part of the online environment 
generated by the Web 2.0, in which the users are authors of the contents and do not 
receive passively information, but they create, reshape and share it. In some cases, 
the interaction among users based on social media created communities, virtual 
worlds (e.g., Second Life, World of Warcraft) or crowdsourcing projects (Wikipedia). 
Although there are significant differences in the nature of these outputs, two aspects 
are always present and are relevant in the light of this contribution: large amount of 
information, user generated contents.
The social media platforms aggregate huge amounts of data generated by users, 
which are in many cases identified or identifiable (
Ohm, 2010; United States General 
Accounting Office, 2011
; See also 
Zang and Bolot, 2011; Golle, 2006; Sweeney,
2000b; Sweeney, 2000a; Tene and Polonetsky, 2013
).
1
 This contributes to create a 
peculiar technological landscape in which the predictive ability that distinguishes 
Big Data (
The Aspen Institute, 2010; Boyd and Crawford, 2011
; see also 
Marton 
et al., 2013
)
2
 has relevant impact not only in terms of competitive advantage in the 
business world (identifying in advance emerging trends, business intelligence, etc.) 
but also in terms of implementation of social surveillance systems by states and 
groups of power.
From this perspective, the following pages consider these phenomena of con-
centration of digital information and related asymmetries, which put in the hands of 
few entities a large amount of data, facilitating the attempts of social surveillance 
1 
See below par. 3.
2 
The creation of datasets of enormous dimension (Big Data) and new powerful analytics make it pos-
sible to draw inferences about unknown facts from statistical occurrence and correlation, with results 
that are relevant in socio-political, strategical and commercial terms; Despite the weakness of this 
approach, more focused on correlation than on statistical evidence, it is useful to predict and perceive 
the birth and evolution of macro-trends, that can be later analyzed in a more traditional statistical way 
in order to identify their causes.