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

Twitter Games: How Successful Spammers Pick Targets

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

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Page 1: Twitter Games:  How  Successful Spammers Pick Targets

Twitter Games: How Successful Spammers Pick TargetsVasumathi Sridharan, Vaibhav Shankar, Minaxi GuptaSchool of Informatics and Computing, Indiana University ACSAC2012

Page 2: Twitter Games:  How  Successful Spammers Pick Targets

OUTLINE

• Introduction- DATA COLLECTION- TWEET TYPES

• STRATEGIES FOR PICKING TARGET• DISCUSSION- Posting methodology- Unbinned Spam Profiles- Gathering followers

• RELATE WORK• CONCLUSION

Page 3: Twitter Games:  How  Successful Spammers Pick Targets

Introduction• 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)

Page 4: Twitter Games:  How  Successful Spammers Pick Targets

OSN: TWITTERWHY 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

Page 5: Twitter Games:  How  Successful Spammers Pick Targets

DATA COLLECTION• 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

Page 6: Twitter Games:  How  Successful Spammers Pick Targets

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

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DATA COLLECTION

• Eliminated languages other than English82274 -> 53083 (suspended profiles)

• 10 tweets within five days - successful spam profiles (14230)- unsuccessful spam profiles

Page 8: Twitter Games:  How  Successful Spammers Pick Targets

• 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)

Page 9: Twitter Games:  How  Successful Spammers Pick Targets

TWEET TYPES• regular tweetAttack : Sender’s follower• reply tweetAttack : anyone• mention tweetAttack : anyone• RetweetAttack : Sender’s follower

Page 10: Twitter Games:  How  Successful Spammers Pick Targets

1. Regular Tweets: Successful spam > Unsuccessful spam2. Replies Tweets :Successful spam < Unsuccessful spamTwitter is known to suspend accounts which send large numbers of replies or mentions [19]

3. Mention Tweets: Successful, Unsuccessful : 1/5 ,1/4Thomas 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

Page 11: Twitter Games:  How  Successful Spammers Pick Targets

• We find that over 3/4 of successful spam profiles exclusively used only one type of tweet

• Spammers vs Other-user

3/4 2/3 14%

Page 12: Twitter Games:  How  Successful Spammers Pick Targets

STRATEGIES FOR PICKING TARGET

• 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

Page 13: Twitter Games:  How  Successful Spammers Pick Targets

Spamming Ones Own Followers

Nearly 40% of unsuccessful spam profiles have zero followers and a total of 2/3 (66%) have less than 10.

Thomas et al. noted in their work that 89% of spam profiles have less than 10 followers. (1 year before)

1/3 of successful spamprofiles have over a 100 followers

spammers become smarter

Page 14: Twitter Games:  How  Successful Spammers Pick Targets

• 14230 profiles >> ten regular tweets with link >> 7704>> 80% Url same Domain >> 6630

• 6630 <> 559 different domains- t.co (1822) - Amazon.com (1741) Affiliate ID

Page 15: Twitter Games:  How  Successful Spammers Pick Targets

Amazon.com

All profiles using the same affiliate ID were clearly part of the same campaign.Profiles across multiple IDs belonged to a spam campaign

Top five

Page 16: Twitter Games:  How  Successful Spammers Pick Targets

Spamming Followers of Popular Profiles

• 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)

Page 17: Twitter Games:  How  Successful Spammers Pick Targets

Spamming based on Keywords in Tweets• Spammers can also pick their targets based on the contentof 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)Spam reply tweet:Here ip5 0rz.tw/ab

source tweet:Wow ip5~

Page 18: Twitter Games:  How  Successful Spammers Pick Targets
Page 19: Twitter Games:  How  Successful Spammers Pick Targets

Trending Topics Hijacking• 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)

Page 20: Twitter Games:  How  Successful Spammers Pick Targets

Targeting Own Followers by Reweets• Reweets• 1230 used retweets• 1230 >> 10 tweets with url >> 28• 26 retweeting from omgwire (promoting)

Overall 5 methods 8805 / 14230 (61.9%)

Page 21: Twitter Games:  How  Successful Spammers Pick Targets

• DISCUSSION

- Posting methodology- Unbinned Spam Profiles- Gathering followers

Page 22: Twitter Games:  How  Successful Spammers Pick Targets

Posting methodology

Page 23: Twitter Games:  How  Successful Spammers Pick Targets

Twitterfeed : sucessful spammer tweets 2/3Web : profiles

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Others

*organic profiles use several different apps,where as spammers have fewer dedicated apps.

92% 80% 60%

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Unbinned Spam Profiles

• 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)

Page 26: Twitter Games:  How  Successful Spammers Pick Targets

Gathering followers

• 1. communities (encourage following back)#InstantFollowBack(#IFB)

• 2. Buy

Page 27: Twitter Games:  How  Successful Spammers Pick Targets

fiverr

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RELATE WORK• YOUTUBE [2] video spam on Youtube and employ machine learningtechniques to identify spammers on YouTube

• FaceBook[5] involves detecting and characterizing spamcampaigns on Facebook.

Page 29: Twitter Games:  How  Successful Spammers Pick Targets

youtube

Page 30: Twitter Games:  How  Successful Spammers Pick Targets

CONCLUSION

• 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

Page 31: Twitter Games:  How  Successful Spammers Pick Targets

End

• THANKS