Follow the Green: Growth and Dynamics on Twitter Follower Markets
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The users of microblogging services, such as Twitter, use the count of followers of an account as a measure of its reputation or influence. For those unwilling or unable to attract followers naturally, a growing industry of “Twitter follower markets” provides followers for sale. Some markets use fake accounts to boost the follower count of their customers, while others rely on a pyramid scheme to turn non-paying customers into followers for each other, and into followers for paying customers. In this paper, we present a detailed study of Twitter Followers Markets, and we show that it is possible to detect users that purchased followers on Twitter.
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Follow the Green: Growth and Dynamics on Twitter Follower Markets
- 1. Follow the Green: Growth and Dynamics in Twitter Follower Markets Gianluca Stringhini, Gang Wang, Manuel Egele*, Christopher Kruegel, Giovanni Vigna, Ben Y. Zhao, Haitao Zheng UC Santa Barbara *Carnegie Mellon University
- 2. Twitter Followers = Perceived Reputation Services that measure the Twitter influence of an account (such as Klout) take the number of followers into account, together with a number of other indicators Building a network of followers is difficult! Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 2
- 3. Shortcuts to Success Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 3
- 4. Can One Really Buy Followers? Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 4
- 5. Twitter Follower Markets Different types of followers for sale • Fake accounts (Sybils) • Compromised accounts • Pyramid schemes Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 5
- 6. Pyramid Markets Free Subscriber Paid Subscriber • Free subscribers → Victims • Paid subscribers → Customers Twitter’s ToS forbids users to participate in Twitter Follower Markets Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 6
- 7. Our Contributions • We study the Twitter Follower Market phenomenon • We analyze the characteristics of market customers and victims • We can detect accounts that bought followers Twitter could block such accounts Twitter Follower Markets would go bankrupt Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 7
- 8. Outline of the Talk • Collection of Twitter Follower Market Data • Characteristics of Victims and Customers • Detecting Market Customers Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 8
- 9. Outline of the Talk • Collection of Twitter Follower Market Data • Characteristics of Victims and Customers • Detecting Market Customers Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 9
- 10. Active Twitter Follower Markets Market (sorted by order of returned results) Different price, depending on the type of followers sold: real followers are more expensive Newfollow.info $216 YES Bigfolo.com $91.99 YES Bigfollow.net $70 YES Intertwitter.com $65 NO (fake accounts) Justfollowers.in $95 YES Twiends.com $169 NO (fake accounts) $49 NO (fake accounts) Devumi.com $64 NO (fake accounts) Hitfollow.info $214 YES Plusfollower.info $214 YES Buyactivefans.com • We queried search engines looking for Twitter Follower Markets • We developed a classifier to determine whether a website is actually selling followers Pyramid? Socialwombat.com We studied the top-five ranked markets $ for 10K Followers $40 NO (fake accounts) Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 10
- 11. Market Sizes We look at tweets advertising the top five markets 10% of the all public tweets (3.3 billion tweets), collected over a period of four months Market Tweets Victims BigFollow 662,858 90,083 BigFolo 4,732,016 611,825 JustFollowers 302 257 NewFollow 77,865 38,341 InterTwitter 0 0 Total 5,473,041 740,506 Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 11
- 12. Detecting Market Victims We purchased followers from the most popular five markets Whoever followed us is a victim In total, we identified 69,222 victims Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 12
- 13. Detecting Market Customers Get more followers! Get more followers! Get more followers! Get more followers! Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 13
- 14. Detecting Market Customers We signed up 180 newly-created accounts as market victims We identified 2,909 market customers Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 14
- 15. Outline of the Talk • Collection of Twitter Follower Market Data • Characteristics of Victims and Customers • Detecting Market Customers Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 15
- 16. Customer Characteristics We compared our set of customers to a set of two million regular users picked at random Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 16
- 17. Customer Follower Dynamics Inflation period Deflation period Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 17
- 18. Customer Follower Dynamics During an observation period of one week: • Spike in Followers ≥ 50 over an hour: 50% Customers, 0.4% Regular • Steady decrease of followers for ≥ 10 consecutive hours: 60% Customers, 0.05% Regular • Change of number of followers ≈ 0: 0% Customers, 30% Regular Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 18
- 19. Victim Characteristics Different strategies for operating markets • Some markets form dense cliques of victims • Some market’s victims follow many customers Common characteristics of victim accounts: • Victims follow each other • A small fraction of victim accounts (≈20%) gets suspended Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 19
- 20. Outline of the Talk • Collection of Twitter Follower Market Data • Characteristics of Victims and Customers • Detecting Market Customers Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 20
- 21. Follower Dynamics Detection We developed a classifier to detect customers in the wild Three types of features (calculated over a week) •Increase features (1,000 features) Number of times spike of d followers during an hour •Decrease features (168 features) Number of times steady decrease of followers for d consecutive hours •Stationary features (168 features) Number of times followers remained constant for d consecutive hours Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 21
- 22. Follower Dynamics Detection Ground truth: Set of 2,909 customers and 10,000 regular accounts (monitored for a week) Classifier: Support Vector Machines 10-fold cross validation: 98.4% true positive rate 0.02% false positive rate Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 22
- 23. Detecting Customers in the Wild We monitored our set of two million regular accounts for two weeks We detected 684 customers •Observed only two million accounts •Purchase needs to happen during our observation Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 23
- 24. Analysis of the Identified Customers The detected accounts have the expected characteristics of customers •They belong to wanna-be celebrities and small businesses •They do not post interesting content Buying followers does not help in becoming influential (median Klout 45) • A customer with 103,000 followers → same Klout score as me (57) Twitter fails in detecting customers: 2 out of 684 were suspended Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 24
- 25. Discussion Our proposed approach to detect and block market customers could undermine the foundations of Twitter Account Markets Market operators could adapt, and try to evade detection •Provide followers slowly •Have no control over the unfollow behavior! Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 25
- 26. Conclusions • We performed a large-scale study of Twitter Follower Markets • We propose techniques to detect market customers • We advocate for Twitter to adopt similar techniques Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 26
- 27. Questions? gianluca@cs.ucsb.edu @gianlucaSB Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 27
- 28. Problem: the Dynamic Classifier is Demanding In the paper, we propose two alternative methods: •A static filter, to discard as many candidates as possible •A static classifier, that uses static profile information to detect customers System TP rate FP rate Static Filter 93.7% 63% Static Classifier 91% 3.3% Dynamic Classifier 98.4% 0.02% Gianluca Stringhini – Follow the Green: Growth and Dynamics in Twitter Follower Markets 28