Lily R. Jenkins and Diane E. Gan CSAFE Centre University of Greenwich

Lily R. Jenkins and Diane E. Gan
CSAFE Centre
University of Greenwich
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Introduction
Background to this work
Overview of Tools
Experiments
Summary of Results
Legal implications
Recommendations
Conclusion
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Most teenagers today have at least one
“profile”
They reveal a lot of personal information about
themselves that anyone can see
Their location and identity are turned on by
default
Twitter users have the ‘handle’ (username) on
all their social media sites
Makes it easy to identify and follow them
through cyber space
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Twitter first appeared on in March 2006
Currently has 200 million active users who send
over 400 million tweets per day
Added the geo-location function to user profiles
in 2009
Many users are not aware that they are
exposing their private information
Enables followers to know exactly where an
individual was tweeting from
The question is – do users know how to use this
feature or how to protect themselves?
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Twitter’s privacy policy
 Clearly states that all user profiles and
subsequent tweets are by default public
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Also details how the information will be used
through their services such as applications,
websites and third parties
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Investigated a range of tools and selected:StreamdIn, Twitonomy and Creepy
StreamdIn
 Application for both android and iOS
 Displays tweets on Google Maps using the
geo-location details attached to each tweet
 User’s profile picture is displayed on a map
 Grouped by location
 View numerous real-time tweets coming in
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Tracking
a mobile
phone
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Being Tracked on
Public Transport
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Twitonomy
 Web based analytics tool
 Allow monitoring, managing and tracking your
own or another person’s activities
 Main feature - overall statistics of a user
 Includes
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how often they retweet
time of day they tweet
avg number of tweets sent per day
gives location details
Mentions Map - displays where in the world the most
mentions are coming from
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Twitonomy Showing Accounts From Two Different Users That
Have Typical Working Days
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Creepy
 Aggregation program
 Gathers geo-location information from Twitter,
Instagram and Flickr
 Requires authentication with each social networking
site supported
 Users can be added to a target list and their geolocation data can be retrieved
 ‘Current Location Details’ gives
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social media platform
time and date
location of the tweet
context of the tweet.
Using this feature it is possible to identify their
current location on the map
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Subjects - three users who are prolific
tweeters
Objective was to see how much information
can be retrieved using freely available tools
The users will be referred to as User A, User B
and User C
All have been asked to tweet with their geolocation settings turned on
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User A and User B did
not have any tweets
appear on the
StreamdIn map
User C’s profile
picture popped up all
over London
Filtered results
display only one
user’s tweets
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Filtered view of User C’s
profile picture
Shows up all over London
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Analyses the last four months’ worth of tweets
User A
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showed information about where they tweet from
mostly use Twitter to re-tweet or reply
most activate during the winter months
no indication whether this user has a job
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User A
Last update - 9 minutes ago
Tweet history
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User B
◦ re-tweets and replies which suggests they use
Twitter to stay in touch with fellow users
◦ no indication as to where User B worked or lived
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User B
More tweets
Significant increase in tweet
history
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User B’s Tweeting Habits
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User C
 revealed a distinctive pattern of usage
 suggests this user has a Monday to Friday job
 most tweets are outside of the hours of 9 to 5
 it can be seen that this person has an iPhone
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User A
 clusters of tweets can be identified
 single tweets showing journey information
between the clusters
 home address was identified by reading the
tweet content
 Google street easily found the house
 Also every Monday they attend ‘Movie Night’
at the same time and place
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The Giveaway Tweet
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User B
 clusters of pins identified their place of work
and their home address
 home residence was given away by tweets
that specifically mention the word ‘home’
 Gives longitude and latitude co-ordinates
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User B’s Route to work
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Locating User B’s work place
They actually only sent one tweet from work!
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User C
 always took the same route to work
 analysing the route to work showed that the second
half of the journey home may change if they needed
to go to the supermarket
 they never mentioned work or home in their tweets
 however, they were in the area of Southwark week
days between 9 and 5 only
 analysing each tweet and pin drop showed that they
were in Southwark every week day
 but never at weekends
 also a fixed monthly pattern - every month they
travelled to visit their parents
 revealed by through their tweets
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User C visit’s her parent’s house in
Southampton once per month
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User C’s Tweets, which establish a pattern of
clusters around home and work
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Three times per week User C goes to this gym
Week days between 7 and 10
Weekends between 1 and 3
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How much did each users’ Tweeting expose the
rest of their social media “presence”?
Did the three users have accounts on Facebook,
LinkedIn, Foursquare and Instagram?
User A gave no indication that they had any
other social media accounts
A Google search revealed their Facebook page
The profile pictures confirmed this
Logging into a Facebook account that is not
“friends” with User A gave a small number of
their pictures, as well as where they were living
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User A also had a profile
on Instagram
using Instagram24.com
and User A’s profile
name it was possible to
locate their pictures
including some pictures
that they had “liked”
Also found them on
LinkedIn
Google Street View
located their front door
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A Google search for User B found their
Linkedin, Facebook and Google+ accounts
Using these profiles, it was possible to confirm
◦ where they worked
◦ the city they live in
◦ where they were studying
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User C was the easiest to identify with Twitter
But the most difficult to locate on other social
media sites
Only Foursquare revealed their location
Back to Twitter
Conducted an exhaustive search of their
Twitter account, which revealed two tweets
with pictures
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Tweet 1
 Posted while in
hospital
 Hospital ID tag
revealed
 their surname
 their date of
birth
 NHS ID
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Tweet 2
 e-ticket showed
their full name
(including a middle
name)
 airports they will
pass through
 how long they will
be stopping at each
location
 A gift to a burglar
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Data Protection Act (1998)
 states that the “data subject has given his
consent to the processing” of personal data
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does not offer any conclusive reasoning as to
how social networking sites users are protected
by signing up to these sites and using them in a
public manner the user has given their consent
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Employers may check
your personal life using
social networks
Example - Kent Police
Commissioner’s Youth
Advisor Paris Brown
forced to withdraw when
her twitter content was
made public
Ref: http://www.dailymail.co.uk/news/article-2312044/Paris-Brown-Foulmouthed-youth-commissioner-quit-offensive-tweets-questioned-policecaution.html
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Reduce your risk
Do not tweet where you live, even if it is only the city
Do not provide your phone number
Avoid using full names
Avoid using a profile picture
Set your profile to private ‘Protect my Tweets’
Remove geo-location tagging on tweets
Remove “Let others find me by my email address”
Do not connect your Twitter account to any other
social media sites
◦ Limit the amount of apps that have access to your
profile
◦ Be very selective about what you put in your tweets
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All tools are freely available
StreamdIn, Twitonomy and Creepy
Creepy was the most successful
It was the geo-location data AND the tweet
contents that leaked information
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Lily Jenkins
[email protected]
Diane Gan
[email protected]
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