1. Lily R. Jenkins and Diane E. Gan CSAFE Centre University of Greenwich 1

2.  Introduction  Background to this work  Overview of Tools  Experiments  Summary of Results  Legal implications  Recommendations  Conclusion C-SAFE - University of Greenwich 2

3.  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 C-SAFE - University of Greenwich 3

4.  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? C-SAFE - University of Greenwich 4

5. Twitter’s privacy policy  Clearly states that all user profiles and subsequent tweets are by default public  Also details how the information will be used through their services such as applications, websites and third parties C-SAFE - University of Greenwich 5

6.  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 C-SAFE - University of Greenwich 6

7. 7 Tracking a mobile phone

8. C-SAFE - University of Greenwich 8 Being Tracked on Public Transport

9. 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 ◦ 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 C-SAFE - University of Greenwich 9

10. 10 Twitonomy Showing Accounts From Two Different Users That Have Typical Working Days

11. 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 geo- location data can be retrieved  ‘Current Location Details’ gives ◦ 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 C-SAFE - University of Greenwich 11

12. C-SAFE - University of Greenwich 12

13.  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 geo- location settings turned on C-SAFE - University of Greenwich 13

14.  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 C-SAFE - University of Greenwich 14

15. C-SAFE - University of Greenwich 15 Filtered view of User C’s profile picture Shows up all over London

16.  Analyses the last four months’ worth of tweets User A ◦ 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 C-SAFE - University of Greenwich 16

17. C-SAFE - University of Greenwich 17 User A Last update - 9 minutes ago Tweet history Platforms used

18. 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 C-SAFE - University of Greenwich 18

19. C-SAFE - University of Greenwich 19 User B More tweets Significant increase in tweet history Platforms used

20. C-SAFE - University of Greenwich 20 User B’s Tweeting Habits

21. 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 C-SAFE - University of Greenwich 21

22. 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 C-SAFE - University of Greenwich 22

23. C-SAFE - University of Greenwich 23 User A

24. C-SAFE, University of Greenwich 24 The Giveaway Tweet

25. 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 C-SAFE - University of Greenwich 25

26. C-SAFE - University of Greenwich 26 User B

27. C-SAFE - University of Greenwich 27 User B’s Route to work

28. C-SAFE - University of Greenwich 28 Locating User B’s work place They actually only sent one tweet from work!

29. 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 C-SAFE - University of Greenwich 29

30.  User C visit’s his parent’s house in Southampton once per month C-SAFE - University of Greenwich 30

31. C-SAFE - University of Greenwich 31 User C’s Tweets, which establish a pattern of clusters around home and work

32. C-SAFE - University of Greenwich 32 Three times per week User C goes to this gym Week days between 7 and 10 Weekends between 1 and 3

33.  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 C-SAFE - University of Greenwich 33

34.  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 C-SAFE - University of Greenwich 34

35.  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 C-SAFE - University of Greenwich 35

36.  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  After conducting an exhaustive search of their Twitter account two tweets were found with pictures C-SAFE - University of Greenwich 36

37. Tweet 1  Posted while in hospital  Hospital ID tag revealed  their surname  their date of birth  NHS ID C-SAFE - University of Greenwich 37

38. 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 C-SAFE - University of Greenwich 38

39. Data Protection Act (1998)  states that the “data subject has given his consent to the processing” of personal data  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 C-SAFE - University of Greenwich 39

40.  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 C-SAFE - University of Greenwich 40 Ref: http://www.dailymail.co.uk/news/article-2312044/Paris-Brown-Foul- mouthed-youth-commissioner-quit-offensive-tweets-questioned-police- caution.html

41.  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 C-SAFE - University of Greenwich 41

42.  There are a huge number of tools that retrieve your information  All tools are freely available  StreamdIn, Twitonomy and Creepy were used for these experiments  Creepy was the most successful  It was the geo-location data AND the tweet contents that leaked information C-SAFE - University of Greenwich 42

43. C-SAFE - University of Greenwich 43 Lily JenkinsLilyRJenkins@gmail.com Diane GanD.Gan@gre.ac.uk