Cyber Crime and Cyber Terrorism

CROWD-SOURCING WITH A COLLECTIVE INTELLIGENCE PLATFORM
Crowd-sourcing data are of great significance in crisis situations. Crowd-sourcing 
enables information to flow quickly and efficiently between emergency manage-
ment specialists and the public. There are a number of tools, such as Pathfinder 

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CHAPTER 15
Social media and its role for LEAs: Review and applications
(
Luther et al., 2009
), 
Sense.us
 (
Heer et al., 2007
) and Many Eyes (
Viegas et al., 
2007
), that are used for the analysis of crowd-sourced information. Platforms such 
as Ushahidi (
http://www.ushahidi.com/
) and Google crisis maps (
http://www.
google.org/crisisresponse/
) are already used to crowd-source information in di-
saster response situations. Crowdsafe (
Shah et al., 2011
), a mobile application that 
allows users to input crime data to help identify hotspots also helps users to plot 
routes home that avoid them. As well as using crowd-sourcing to coordinate the 
relief effort, LEAs may also wish to crowd-source information during and after 
a crisis event to provide both situational awareness and to piece together the true 
nature of the events, as trawling manually through this data is nigh on impossible. 
Involving LEAs in the crowd-sourcing loop is also necessary but, as the Boston 
Bombings in 2013 showed; crowd-sourced, public data alone does not necessarily 
lead to the correct investigative conclusions (
Lee, 2013
).
Similar to crowd-sourcing, collective intelligence (
Bonabeau, 2009
) is the result 
of the collective and collaborative efforts of a number of people with a common aim 
or goal. A collective intelligence platform combines data from a number of different 
sources (e.g., open-source intelligence repositories). Data received through crowd-
sourcing appeals via social media and closed data that is not exposed to the public is 
then combined with the domain-specific knowledge of LEA officers, domain experts 
and analysts in order to produce actions, outcomes, or knowledge building blocks. 
The results of these analyses may go back out into the public domain to refine and 
re-organize the actions of scenario stakeholders based on the intelligence provided 
by LEAs. This type of platform would not only be useful in a crisis management situ-
ation but also to track events such as organized crime involving arms trading, drug 
trafficking, and money laundering gangs (see Chapters 3 and 10).
A number of technologies can be utilized and integrated within a collective intel-
ligence platform. Formal Concept Analysis is one such example of this and may be 
used for the analysis of data generated by social media that is potentially related to 
criminal incidents.
In FCA an object can usually only be placed at a certain hierarchy level if it 
contains all the attributes that are present at that given level. When analyzing textual 
data in particular, the wide range of expressions someone can use to explain exactly 
the same situation is problematic. Two potential ways of tackling this problem are 
to use a lexical database such as Wordnet (
Miller, 1995
) to map synonyms for each 
of the attributes and the second is to introduce fault tolerance for FCA. That is to 
accept objects at a particular level of the hierarchy even if they do not match all the 
attributes but match a number of them beyond a predefined threshold. This prevents 
near misses slipping through FCA's metaphorical net. This means the collective in-
telligence platform can be refined as more information is added and further analytical 
techniques such as machine learning, clustering and additional classification can also 
be applied to further enhance and refine the results.
The dynamics of a crisis situation mean that events can change rapidly. Example 
technologies such as FCA could mean a constant re-evaluation of the number of 
objects appearing at different positions in the hierarchy and the introduction of new 

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