In Foursquare, one of the currently most popular online location based social networking sites (LBSNs), users may not only check-in at specific venues but also post comments (or tips), sharing their opinions and previous experiences at the corresponding physical places. Foursquare tips, which are visible to everyone, provide venue owners with valuable user feedback besides helping other users to make an opinion about the specific venue. However, they have been the target of spamming activity by users who exploit this feature to spread tips with unrelated content. In this paper, we present what, to our knowledge, is the first effort to identify and analyze different patterns of tip spamming activity in Foursquare, with the goal of developing automatic tools to detect users who post spam tips - tip spammers. A manual investigation of a real dataset collected from Foursquare led us to identify four categories of spamming behavior, viz. Advertising/Spam, Self-promotion, Abusive and Malicious. We then applied machine learning techniques, jointly with a selected set of user, social and tip's content features associated with each user, to develop automatic detection tools. Our experimental results indicate that we are able to not only correctly distinguish legitimate users from tip spammers with high accuracy (89.76%) but also correctly identify a large fraction (at least 78.88%) of spammers in each identified category.

1. Detection of Spam
Tipping Behaviour on
Foursquare
Anupama Aggarwal¶, Prof P. Kumaraguru “PK”¶ ,
Prof J. Almeida*
¶
Indraprastha Institute of Information Technology (IIIT-Delhi, India)
* Universidade Federal de Minas Gerais (UFMG, Brazil)
1

2. Foursquare 101
‣ Location Based Social Network
‣ 33 Million Users *
‣ 3.5 Billion checkins *
‣ 31% of mobile social media users use Foursquare *
* As of January 2013
2

3. Location
Sharing
OSN
Foursquare 101
Friends Activity
Your Last
Checkin
Venue
Friends
Suggestions
Venue
Suggestions
Tip : Suggested Activity
for a Venue
Tip can be Liked or Saved

4. Spam Tips
Advertising / Marketing
‣ Tips unrelated to Venue
Scam / Phishing
4

5. Spam
according to
Foursquare ToS
‣ Tips with links to websites selling software, realtor contact
info, a listing for your business, or other promotion
‣ Tips with inappropriate language or negativity directed at
another person
‣ Unauthorized or unsolicited advertising, junk
5

6. Contributions
‣ Characterizing irregular user behaviour
‣
We observed different categories of spam users
‣
We characterize features distinguishing these spam users
‣ Automatic detection of spammers
‣
Distinguish between spam and legitimate Foursquare users
‣
Cluster spam users into different categories according to their behaviour
6

7. Data Crawling
2,400,594 tips
613,298 users
7

8. Observed Categories of
Spam Users
‣ Marketing : These users post tips to promote and
advertise a specific product/ brand / venue / external URL
‣ Malicious : Such Foursquare users post external URLs in
Tips which direct to spam / phishing / malware websites
‣ Abusive / Derogatory: These users try to deface or
bad-mouth another person
‣ Self Promotion: These users try to draw attention to
themselves
8

9. Ground Truth Data
Annotation Portal
2,000 Legitimate users
1,900 Spammers
9

10. Features used to
detect Spammers
‣ User Attributes
‣
Properties of the Foursquare user profile and his checkins
‣ Social Attributes
‣
Friends network of the Foursquare user under inspection
‣ Content Attributes
‣
Details about Tips posted by the Foursquare user
10

11. Features used
Category
χ2 rank
Feature
User
Attributes
1
3
4
5
11
12
15
Number of Tips
Ratio of Check-ins and Tips
Number of Check-ins
Number of Badges
Number of Mayorships
Ratio of Check-ins and Badges
Number of Photos posted
Social
Attributes
6
Number of Friends
2
Similarity score of Tips
7
8
9
10
13
14
Number of URLs posted
Average number of words in Tips
Average number of characters in Tips
Ratio of number of likes and number of Tips
Average number of spam words in Tips
Average number of phone-numbers posted in Tips
Content
Attributes
11

12. Few Observations
‣ Spammers post same/similar Tips on multiple venues
‣ A large fraction of spam Tips contain URLs
‣ Spam Tips may also have phone numbers
‣ Legitimate users have more Friends
‣ Spammers have very few Friends but large number of Tips
12

13. Relation b/w Tips and
Checkins
Tips
Irregular User Behaviour
Check-ins

14. Tips Distribution
Legitimate users
Spammers
14

15. Classification Results
Classification
Algorithm
Precision
(Spam)
Precision
(Safe)
Recall
(Spam)
Recall
(Safe)
Accuracy
KNN
83.2%
86.6%
86.3%
83.5%
84.89%
Decision
Tree
88.1%
89.2%
88.3%
85.8%
89.53%
Random
Forest
89.3%
90.2%
88.3%
90.3%
89.76%
15

16. Detection of Spam Classes
‣ Expectation-Maximization (EM) clustering
‣ Spammers Categories ‣
Advertising / Marketing
‣
Self Promotion
‣
Abusive
‣
Malicious
16

17. Detection of Spam Classes
‣ Clustering Accuracy for spammer categories -
Advertising
88.23%
Self-Promotion
87.23%
Abusive
78.88%
Malicious
0%
17

18. Conclusion
‣ Analyzed spammers behaviour on Foursquare
‣ We obtained an accuracy of 89.76% with Random Forest
classifier to distinguish spammers from legitimate users
‣ We classified the spammers into four broad categories
‣ We were able to to detect users belonging to Advertising,
Self-promotion and Abusive categories with an accuracy of
88.23%, 87.23% and 78.88%
18

19. Future Work
‣ Refine our methodology by use of other classification
algorithms
‣ Use multiclass classification to detect users in any of the
spam categories
‣ Correlation of content and the URLs posted by different
users can help us in identifying several spam campaigns on
Foursquare
19

20. Thank You!
Questions ?
20

21. For any further information, please write to
pk@iiitd.ac.in
precog.iiitd.edu.in
21