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Twitter Games: How Successful Spammers Pick Targets

Twitter Games: How Successful Spammers Pick Targets. Vasumathi Sridharan , Vaibhav Shankar, Minaxi Gupta School of Informatics and Computing, Indiana University ACSAC2012. Introduction

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Twitter Games: How Successful Spammers Pick Targets

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  1. Twitter Games: How Successful Spammers Pick Targets VasumathiSridharan, Vaibhav Shankar, MinaxiGupta School of Informatics and Computing, Indiana University ACSAC2012

  2. Introduction - DATA COLLECTION - TWEET TYPES • STRATEGIES FOR PICKING TARGET • DISCUSSION - Posting methodology - Unbinned Spam Profiles - Gathering followers • RELATE WORK • CONCLUSION OUTLINE

  3. Email spam has been a problem for decades • As email spam filtering programs have improved, with many claiming 99% or higher accuracies • Spammers have looked for other avenues • Online social networks (OSNs) Introduction

  4. WHY Twitter ? - Twitter alone boasted 140 million users as of March 2012 [20] - Fighting spam on OSNs requires new types of filtering techniques • New topic of spam on OSNs (Classifiers) we do not know how spammers pick their targets OSN: TWITTER

  5. Twitter’s streaming API (collect tweets)(samples) • November 2011 • 19,991,050 tweets / 7,078,643 profiles • we visited http://www.twitter.com/<username> • looked for suspended profiles (SPAM?) • 82274 suspended profiles DATA COLLECTION

  6. http://www.twitter.com/<username>82274 suspended profiles

  7. Eliminated languages other than English 82274 -> 53083 (suspended profiles) • 10 tweets within five days • successful spam profiles (14230) • unsuccessful spam profiles DATA COLLECTION

  8. 70% of unsuccessful spam profiles and 15% of successful spam profiles get suspended on the first day • [16] 77% of spam profiles were suspended on the first day and 92% within three days> [16] Thomas, K., Grier, C., Song, D., and Paxson, V. Suspended accounts in retrospect: an analysis of twitter spam. In ACM/USENIX Internet Measurement Conference (IMC) (2011)

  9. regular tweet Attack : Sender’s follower • reply tweet Attack : anyone • mention tweet Attack : anyone • Retweet Attack : Sender’s follower TWEET TYPES

  10. 1. Regular Tweets: Successful spam > Unsuccessful spam 2.Replies Tweets :Successful spam < Unsuccessful spam Twitter is known to suspend accounts which send large numbers of replies or mentions [19] 3. Mention Tweets: Successful,Unsuccessful : 1/5 ,1/4 Thomas et al. a year ago [16] found that 52% of spam profiles made use of mention tweets. we conclude that Twitter spammers have evolved their strategies in the last one year

  11. We find that over 3/4 of successful spam profiles exclusively used only one type of tweet • Spammers vs Other-user 2/3 14% 3/4

  12. 1.Spamming Ones Own Followers • 2.Spamming Followers of Popular Profiles • 3.Spamming based on Keywords in Tweets • 4.Trending Topics Hijacking • 5.Targeting Own Followers by Reweets STRATEGIES FOR PICKING TARGET

  13. Nearly 40% of unsuccessful spam profiles have zero followers and a total of 2/3 (66%) have less than 10. 1/3 of successful spam profiles have over a 100 followers spammers become smarter Spamming Ones Own Followers Thomas et al. noted in their work that 89% of spam profiles have less than 10 followers. (1 year before)

  14. 14230 profiles >> ten regular tweets withlink >> 7704 >> 80% Url same Domain >> 6630 • 6630 <> 559 different domains - t.co (1822) - Amazon.com (1741) Affiliate ID

  15. Top five All profiles using the same affiliate ID were clearly part of the same campaign. Amazon.com Profiles across multiple IDs belongedto a spam campaign

  16. Ex. Basketball lovers , <Target Michael Jordan> • Reply or Mention tweets • ( >4 user receive same spam & 50% follow same person ) • 14230 >> reply or mention >> 4086 • >> 877 (26) Spamming Followers of Popular Profiles

  17. Spammers can also pick their targets based on the content of tweets from Twitter users. • ex: search “bumbler” “justinbieber” • Reply or Mention tweets (TF-IDF[8] 7 million words(spam tweets) -> 50K words) • 1004 (1)(150) source tweet: Wow ip5~ Spamming based on Keywords in Tweets Spam reply tweet: Here ip5 0rz.tw/ab

  18. Hashtag (圖) • Ex. #bumbler • Spammers have been known to hijack trending topics to increase the visibility of their spam campaigns [16] • Various types of tweets (#iphone5) • 4327 (spam,#) >> top 200 hashtag >> 1043 (523)(14)(3) Trending Topics Hijacking

  19. Reweets • 1230used retweets • 1230 >> 10 tweets with url >> 28 • 26 retweeting from omgwire (promoting) Targeting Own Followers by Reweets Overall 5 methods 8805 / 14230 (61.9%)

  20. DISCUSSION - Posting methodology - Unbinned Spam Profiles - Gathering followers

  21. Posting methodology

  22. Twitterfeed : sucessful spammer tweets 2/3Web : profiles

  23. 92% 80% 60% Others *organic profiles use several different apps, where as spammers have fewer dedicated apps.

  24. Overall 5 methods 8805 / 14230 (61.9%) • 10 url tweets , 80% same domain (5 url , 50%) • 61.9 % >> 72% • TweetAdder, based on their geographical location and language • Not spamer (ex. violence) Unbinned Spam Profiles

  25. 1. communities (encourage following back) #InstantFollowBack(#IFB) • 2. Buy Gathering followers

  26. fiverr

  27. YOUTUBE [2] video spam on Youtube and employ machine learning techniques to identify spammers on YouTube • FaceBook [5] involves detecting and characterizing spam campaigns on Facebook. RELATE WORK

  28. youtube

  29. We analyzed strategies of successful Twitter spammers • Particularly as they relate to picking spam target • The spammers themselves evolved in a mere mattter of one year(Thomas [16]) • Need more data CONCLUSION

  30. THANKS End

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