  Recently, light has been shed on the trend of personalization, which comes
into play whenever different search results are being tailored for a group of
users who have issued the same search query. The unpalatable fact that myriads
of search results are being manipulated has perturbed a horde of people. With
regards to that, personalization can be instrumental in spurring the Filter
Bubble effects, which revolves around the inability of certain users to gain
access to the typified contents that are allegedly irrelevant per the search
engine's algorithm.
  In harmony with that, there is a wealth of research on this area. Each of
these has relied on using techniques revolving around creating Google accounts
that differ in one feature and issuing identical search queries from each
account. The search results are often compared to determine whether those
results are going to vary per account. Thereupon, we have conducted six
experiments that aim to closely inspect and spot the patterns of
personalization in search results. In a like manner, we are going to examine
how the search results are going to vary accordingly. In all of the tasks,
three different metrics are going to be measured, namely, the number of total
hits, the first hit, and the correlation between hits. Those experiments are
centered around fulfilling the following tasks. Firstly, setting up four VPNs
that are located at different geographic locations and comparing the search
results with those obtained in the UAE. Secondly, performing the search while
logging in and out of a Google account. Thirdly, searching while connecting to
different networks: home, phone, and university networks. Fourthly, using
different search engines to issue the search queries. Fifthly, using different
web browsers to carry out the search process. Finally, creating and training
six Google accounts.
