Follow the green
Open accessAbstract
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 follower markets, report in detail on both the static and dynamic properties of customers of these markets, and develop and evaluate multiple techniques for detecting these activities. We show that our detection system is robust and reliable, and can detect a significant number of customers in the wild.
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Cites
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652 · see citing works
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809 · see citing works
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501 · see citing works
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022 · cited 1× · see citing works
“As Twitter use grows, the influence and reputation of a person or business entity are increasingly associated with their number of Twitter followers [8,18].”
BackgroundAugust, W. U., Moore, D., Voelker, G. M., & Savage, S. (2004) · IEEE Security & Privacy · doi:10.1109/msp.2004.9 · Cited by 1,519 · see citing works
Jagatic, T. N., Johnson, N. A., Jakobsson, M., & Menczer, F. (2007) · Communications of the ACM · doi:10.1145/1290958.1290968 · Cited by 1,156 · see citing works
User interactions in social networks and their implications
Wilson, C., Boe, B., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2009) · Proceedings of the 4th ACM European conference on Computer systems · doi:10.1145/1519065.1519089 · Cited by 879 · see citing works
Self-citationDetecting spammers on social networks
Stringhini, G., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 26th Annual Computer Security Applications Conference · doi:10.1145/1920261.1920263 · Cited by 791 · cited 2× · see citing works
“On the other hand, legitimate accounts are more attractive as followers to cybercriminals, because these accounts have real followers, share content, and add real value to customers seeking a higher reputation.”
BackgroundSelf-citationYu, H., Kaminsky, M., Gibbons, P. B., & Flaxman, A. D. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1151659.1159945 · Cited by 734 · see citing works
Krishnamurthy, B., Gill, P., & Arlitt, M. (2008) · Proceedings of the first workshop on Online social networks · doi:10.1145/1397735.1397741 · Cited by 697 · see citing works
Magno, G., Rodrigues, T., & Almeida, V. A. F. (2010) · Cited by 649 · see citing works
Uncovering social spammers: social honeypots + machine learning
Lee, K., Caverlee, J., & Webb, S. (2010) · Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1835449.1835522 · Cited by 619 · see citing works
Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866311 · Cited by 558 · cited 2× · see citing works
“On the other hand, legitimate accounts are more attractive as followers to cybercriminals, because these accounts have real followers, share content, and add real value to customers seeking a higher reputation.”
BackgroundAll your contacts are belong to us
Bilge, L., Strufe, T., Balzarotti, D., & Kirda, E. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526784 · Cited by 551 · see citing works
Design and Evaluation of a Real-Time URL Spam Filtering Service
Thomas, K., Grier, C., Ma, J. H., Paxson, V., & Song, D. X. (2011) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2011.25 · Cited by 470 · see citing works
Detecting and characterizing social spam campaigns
Gao, H., Hu, J. S., Wilson, C., Li, Z., Chen, Y., & Zhao, B. Y. (2010) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1879141.1879147 · Cited by 420 · see citing works
Self-citationUnderstanding and combating link farming in the twitter social network
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187846 · Cited by 341 · see citing works
Twitter: social communication in the Twitter age
(2013) · Choice Reviews Online · doi:10.5860/choice.50-5424 · Cited by 338 · see citing works
An analysis of underground forums
Motoyama, M., McCoy, D., Levchenko, K., Savage, S., & Voelker, G. M. (2011) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2068816.2068824 · Cited by 311 · cited 1× · see citing works
“In this paper, we perform a comprehensive study of Twitter fol-lower markets.”
MethodYou are how you click: Clickstream analysis for Sybil detection
Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H.-T., & Zhao, B. Y. (2013) · Cited by 256 · see citing works
Self-citationCOMPA: Detecting Compromised Accounts on Social Networks.
Egele, M., Stringhini, G., Krügel, C., & Vigna, G. (2013) · Cited by 250 · see citing works
Self-citationTrafficking fraudulent accounts: the role of the underground market in Twitter spam and abuse
Thomas, K., McCoy, D., Grier, C., Kolcz, A., & Paxson, V. (2013) · Cited by 239 · see citing works
Die Free or Live Hard? Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers
Yang, C., Harkreader, R. C., & Gu, G. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-23644-0_17 · Cited by 235 · see citing works
Kwak, H., Chun, H., & Moon, S. B. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979104 · Cited by 148 · see citing works
A longitudinal study of follow predictors on twitter
Hutto, C. J., Yardi, S., & Gilbert, É. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2470771 · Cited by 140 · cited 1× · see citing works
“Unfortunately, none of these techniques are efficient enough for users who need large numbers of followers fast [16].”
BackgroundStringhini, G., Egele, M., Kruegel, C., & Vigna, G. (2012) · doi:10.1145/2342549.2342551 · Cited by 74 · see citing works
Self-citationSocial Turing Tests: Crowdsourcing Sybil Detection
Wang, G., Mohanlal, M., Wilson, C., Wang, X., Metzger, M. J., Zheng, H.-T., & Zhao, B. Y. (2012) · Cited by 40 · see citing works
Self-citationThe Latent Community Model for Detecting Sybils in Social Networks.
Cai, Z., & Jermaine, C. (2012) · Network and Distributed System Security Symposium · Cited by 32 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Coldewey , D. Romney twitter account gets upsurge in fake followers, but from where? NBC News ( 2012 ). Coldewey, D. Romney twitter account gets upsurge in fake followers, but from where? NBC News (2012).
- Considine , A. Buying their way to twitter fame. The New York Times ( 2012 ). Considine, A. Buying their way to twitter fame. The New York Times (2012).
- King , R. How companies use Twitter to bolster their brands . In BusinessWeek Online ( 2008 ). King, R. How companies use Twitter to bolster their brands. In BusinessWeek Online (2008).
- Perlroth , N. Fake twitter followers become multimillion-dollar business. The New York Times ( 2013 ). Perlroth, N. Fake twitter followers become multimillion-dollar business. The New York Times (2013).
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Fame for sale: Efficient detection of fake Twitter followers
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 461 · see citing works
“In another work [26], the same research team provides more details about the account markets, analyzing additional properties and characteristics of their customers (e.”
BackgroundMethodInfluentialThe Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion · doi:10.1145/3041021.3055135 · Cited by 381 · see citing works
“Recent investigations anecdotally highlight how new waves of social spambots are rising [16, 48].”
BackgroundSpontaneous Collective Action: Peripheral Mobilization During the Arab Spring
Steinert-Threlkeld, Z. C. (2017) · American Political Science Review · doi:10.1017/s0003055416000769 · Cited by 229 · see citing works
Online Social Networks: Threats and Solutions
Fire, M., Goldschmidt, R., & Elovici, Y. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2321628 · Cited by 359 · see citing works
A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114006 · Cited by 279 · see citing works
Towards Detecting Anomalous User Behavior in Online Social Networks
Viswanath, B., Gummadi, K. P., Bashir, M. A., Krishnamurthy, B., Crovella, M. E., Guha, S., & Mislove, A. E. (2014) · Cited by 236 · see citing works
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Cresci, S., Pietro, R. D., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · doi:10.48550/arxiv.1701.03017 · Cited by 237 · see citing works
“Recent investigations anecdotally highlight how new waves of social spambots are rising [16, 48].”
BackgroundA review on social spam detection: Challenges, open issues, and future directions
Rao, S., Verma, A. K., & Bhatia, T. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115742 · Cited by 225 · see citing works
Black Trolls Matter: Racial and Ideological Asymmetries in Social Media Disinformation
Freelon, D., Bossetta, M., Wells, C., Lukito, J., Xia, Y., & Adams, K. (2020) · Social Science Computer Review · doi:10.1177/0894439320914853 · Cited by 191 · see citing works
“The practice of purchasing perceived influence indicators such as followers, likes, and retweets has long been studied by computer science and information scientists (e.g., Aggarwal & Kumaraguru, 2015; Singh et al., 2016; Stringhini et al., 2013).”
BackgroundTowards Detecting Compromised Accounts on Social Networks
Egele, M., Stringhini, G., Kruegel, C., & Vigna, G. (2015) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2015.2479616 · Cited by 185 · see citing works
“More details, including a parameter sensitivity analysis on the threshold value, are presented in our previous work [19], [20].”
Self-citationTwitter spam detection: Survey of new approaches and comparative study
Wu, T., Wen, S., Xiang, Y., & Zhou, W. (2017) · Computers & Security · doi:10.1016/j.cose.2017.11.013 · Cited by 174 · see citing works
Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2681672 · Cited by 168 · see citing works
Zannettou, S., Caulfield, T., Setzer, W., Sirivianos, M., Stringhini, G., & Blackburn, J. (2019) · ACM Web Science Conference (WebSci) · doi:10.1145/3292522.3326016 · Cited by 135 · see citing works
Self-citationTowards understanding and detecting fake reviews in app stores
Martens, D., & Maalej, W. (2019) · Empirical Software Engineering · doi:10.1007/s10664-019-09706-9 · Cited by 137 · see citing works
“10,000 Twitter followers can be bought for a price of $4 (Stringhini et al., 2013).”
BackgroundIntegro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs
Boshmaf, Y., Logothetis, D., Siganos, G., Leria, J., Lorenzo, J., Ripeanu, M., & Beznosov, K. (2015) · Proceedings 2015 Network and Distributed System Security Symposium · doi:10.14722/ndss.2015.23260 · Cited by 134 · see citing works
Credibility in Online Social Networks: A Survey
Alrubaian, M., Al‐Qurishi, M., Alamri, A. M., Al-Rakhami, M. S., Hassan, M. M., & Fortino, G. (2018) · IEEE Access · doi:10.1109/access.2018.2886314 · Cited by 116 · see citing works
“However, users can easily obtain thousands of followers within minutes from Twitter follower markets [79], [80].”
BackgroundBragg, M. A., Pageot, Y. K., Amico, A., Miller, A. N., Gasbarre, A., Rummo, P. E., & Elbel, B. D. (2019) · Pediatric Obesity · doi:10.1111/ijpo.12606 · Cited by 102 · see citing works
“Further, we were unable to discern how many of the nearly 1 billion followers that were publicly listed on the accounts might be bots (ie, non-human accounts purchased to inflate numbers of followers).(49) Although posts featuring adolescents were more likely to include interactive tools than posts featuring adults, o…”
BackgroundProfile characteristics of fake Twitter accounts
Gurajala, S., White, J. S., Hudson, B., Voter, B. R., & Matthews, J. N. (2016) · Big Data & Society · doi:10.1177/2053951716674236 · Cited by 86 · see citing works
“The ground truth users have a median friends-to-followers ratio of (cid:2) 1, consistent with findings of other researchers (e.g. Stringhini et al., 2013).”
BackgroundResultDe Cristofaro, E., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663729 · Cited by 85 · see citing works
“Note that Twitter follower markets differ from Facebook like farms as Twitter entails a follower-followee relationship among users, while Face-book friendships imply a bidirectional relationships.”
BackgroundThomas, K., Li, F., Grier, C., & Paxson, V. (2014) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2660267.2660282 · Cited by 77 · see citing works
“Alternative monetization strategies rely on tiered pricing between free membership and premium membership to fake follower rings [25].”
BackgroundVoteTrust: Leveraging Friend Invitation Graph to Defend against Social Network Sybils
Yang, Z. C., Xue, J., Yang, X., Wang, X., & Dai, Y. (2015) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2015.2410792 · Cited by 74 · see citing works
“Moreover, as long as the victims’ links have nonzero weights, Sybils could still increase their scores by befriending even more victims.”
BackgroundA hybrid approach for spam detection for Twitter
Malik, M., Iqbal, M. A., Aleem, M., & Islam, M. A. (2017) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast.2017.7868095 · Cited by 79 · see citing works
“[10] proposed a technique for detection of compromised account.”
MethodE-commerce Reputation Manipulation
Xu, H., Liu, D., Wang, H., & Stavrou, A. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741650 · Cited by 71 · see citing works
“Several works studied the underground economy related to Twitter, including markets for selling fraudulent accounts [35, 36] and Twitter followers [33].”
BackgroundGurajala, S., White, J. S., Hudson, B., & Matthews, J. N. (2015) · doi:10.1145/2789187.2789206 · Cited by 68 · see citing works
“The presence of fake followers can: bias an individual or organization’s popularity based on follower number count [4]; alter the characteristics of the audience [5]; or create a legitimacy problem for individuals/organizations [1].”
BackgroundÍntegro: Leveraging victim prediction for robust fake account detection in large scale OSNs
Boshmaf, Y., Logothetis, D., Siganos, G., Leria, J., Lorenzo, J., Ripeanu, M., Beznosov, K., & Halawa, H. H. (2016) · Computers & Security · doi:10.1016/j.cose.2016.05.005 · Cited by 67 · see citing works
“Analyzing Victim Accounts While we are the first to use potential victims to separate fakes from real accounts, others have analyzed victim accounts as part of the larger cyber criminal ecosystem in OSNs [47].”
BackgroundVoteTrust: Leveraging friend invitation graph to defend against social network Sybils
Xue, J., Yang, Z. C., Yang, X., Wang, X., Chen, L., & Dai, Y. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6567045 · Cited by 63 · see citing works
“Limitation: Recent works [25], [26] have found that miscreants start to sell legitimate accounts that have been compromised in Twitter.”
BackgroundLOBO: Evaluation of Generalization Deficiencies in Twitter Bot Classifiers
Echeverra, J., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Stringhini, G., & Zhou, S. (2018) · Proceedings of the 34th Annual Computer Security Applications Conference · doi:10.1145/3274694.3274738 · Cited by 61 · see citing works
Self-citationKarami, M., Park, Y., & McCoy, D. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883004 · Cited by 58 · see citing works
“In this respect our work is more along the lines of studies focused on criminal support services, such as email spam delivery [13, 28], fake social links [29] and fake account creation [32].”
BackgroundTrends and lessons from three years fighting malicious extensions
Jagpal, N., Dingle, E., Gravel, J.-P., et al. (2015) · USENIX Security Symposium · Cited by 54 · see citing works
A Game-Theoretic Analysis of Adversarial Classification
Dritsoula, L., Loiseau, P., & Musacchio, J. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2718494 · Cited by 52 · see citing works
SimilCatch: Enhanced social spammers detection on Twitter using Markov Random Fields
El-Mawass, N., Honeiné, P., & Vercouter, L. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102317 · Cited by 53 · see citing works
“The work of Thomas et al. Thomas et al. (2011, 2013) and Stringhini et al. Stringhini et al. (2013, 2012) on the155 spam underground and communities are notable in this domain.”
BackgroundMethodInfluentialTrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter
Zhang, J., Zhang, R., Sun, J., Zhang, Y., & Zhang, C. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2494059 · Cited by 48 · see citing works
“As stated before, since sybil users could easily get many legitimate followers [23], [42], [43], the following connections fail to achieve the sybil resilience and hence should be ruled out for the influence measurement.”
BackgroundLing, C., Stringhini, G., Balci, U., & Blackburn, J. (2021) · IEEE Security & Privacy · doi:10.1109/msec.2021.3127392 · Cited by 57 · see citing works
Self-citationCrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks
Song, J., Lee, S., & Kim, J. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813661 · Cited by 50 · see citing works
“Stringh-ini et al. [24] analyze Twitter follower markets.”
BackgroundQuit Playing Games with My Heart: Understanding Online Dating Scams
Huang, J., Stringhini, G., & Yong, P. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-20550-2_12 · Cited by 48 · see citing works
“Other systems look at the social network structure of accounts, or at how the messages posted by them spread, looking for anomalies indicative of a malicious profile [8, 9, 15, 32, 38, 41].”
BackgroundSelf-citationWho Let The Trolls Out? Towards Understanding State-Sponsored TrollsPreprint
Zannettou, S., Caulfield, T., Setzer, W. N., Sirivianos, M., Stringhini, G., & Blackburn, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03130 · Cited by 46 · see citing works
Self-citationFake Twitter followers detection by denoising autoencoder
Castellini, J., Poggioni, V., & Sorbi, G. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3106489 · Cited by 44 · see citing works
“Many people try to rapidly gain fame by exploiting this mechanism, buying fake following profiles on these online markets [23], [21], [24], [3], [2].”
BackgroundInfluentialSingh, M., Bansal, D., & Sofat, S. (2018) · Cybernetics & Systems · doi:10.1080/01969722.2017.1412866 · Cited by 44 · see citing works
“Furthermore, Stringhini et al. (2013) developed techniques to detect market customers and victims.”
BackgroundFollow spam detection based on cascaded social information
Jeong, S., Noh, G., Oh, H., & Kim, C.-K. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.033 · Cited by 45 · see citing works
“Stringhini et al. [34] examined real follower markets and detected fake accounts by identifying those for which the number of followees increased suddenly but did not decrease any further.”
BackgroundStrength in Numbers: Robust Tamper Detection in Crowd Computations
Viswanath, B., Bashir, M. A., Zafar, M. B., Bouget, S., Guha, S., Gummadi, K. P., Kate, A., & Mislove, A. (2015) · Proceedings of the 2015 ACM on Conference on Online Social Networks · doi:10.1145/2817946.2817964 · Cited by 43 · see citing works
“There are thriving un-derground markets for launching such tampering attacks on the crowd-sourcing sites mentioned above [27, 32, 35]; typically, the more popular a site, the greater the frequency and magnitude of such attacks.”
BackgroundCharacterizing and automatically detecting crowdturfing in Fiverr and Twitter
Lee, K., Webb, S., & Ge, H. (2015) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0241-1 · Cited by 41 · see citing works
Retweet Us, We will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services
Dutta, H. S., Chetan, A., Joshi, B., & Chakraborty, T. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508801 · Cited by 41 · see citing works
“Most of the prior works showed how fake followers in social media help in promoting a certain user [19].”
BackgroundLee, K., Webb, S., & Ge, H. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14528 · Cited by 39 · see citing works
“(Stringhini et al. 2013) and Thomas et al.”
BackgroundND-Sync: Detecting Synchronized Fraud Activities
Γιάτσογλου, Μ., Chatzakou, D., Shah, N., Beutel, A., Faloutsos, C., & Vakali, A. I. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18032-8_16 · Cited by 36 · see citing works
“username pattern, age) have been shown to fail to catch more sophisticated fraud schemes that exploit real users’ accounts, such as the pyramid follower markets [22] and account compromization [1].”
BackgroundBhattacharya, M., Roy, S., Chattopadhyay, S., Das, A. K., & Shetty, S. S. (2022) · Security and Privacy · doi:10.1002/spy2.275 · Cited by 39 · see citing works
Overview of the crowdsourcing process
Nassar, L., & Karray, F. O. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1235-5 · Cited by 37 · see citing works
TRank: Ranking Twitter users according to specific topics
Montangero, M., & Furini, M. (2015) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2015.7158074 · Cited by 35 · see citing works
“Similarly, Stringhini et al. [12] showed that a growing industry of Twitter follower markets provides followers for sale and some markets use fake accounts to boost the follower count of their customers.”
BackgroundInfluentialToward Detecting Collusive Ranking Manipulation Attackers in Mobile App Markets
Chen, H., He, D., Zhu, S., & Yang, J. (2017) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3052973.3053022 · Cited by 33 · see citing works
“[31] leverages anomalous increases in the follower number of an account to identify collusive follower markets on Twitter.”
BackgroundLingam, G., Rout, R. R., Somayajulu, D. V. L. N., & Das, S. K. (2020) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3320269.3384770 · Cited by 32 · see citing works
Detection of fake Twitter followers using graph centrality measures
Mehrotra, A., Sarreddy, M., & Singh, S. (2016) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i.2016.7918016 · Cited by 31 · see citing works
“, [4] have revealed a very interesting observation where vendors, termed as Pyramid Merchants, have”
BackgroundCharacterizing key stakeholders in an online black-hat marketplace
Farooqi, S., Jourjon, G., Ikram, M., et al. (2017) · APWG Symposium on Electronic Crime Research (eCrime) · doi:10.1109/ecrime.2017.7945050 · Cited by 30 · see citing works
“, creating honeypot accounts to identify the fake accounts used for providing likes/followers [7], [21].”
BackgroundOn helping broadcasters to promote TV-shows through hashtags
Michele, R. D., Ferretti, S., & Furini, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6510-7 · Cited by 30 · see citing works
“) [35, 38], but recently it has been shown that these approaches do not provide satisfactory results for many different reasons: the mere number of followers is not a good indicator because there is a high chance that many tweets will never be read and because it is easy to acquire”
Thwarting Fake OSN Accounts by Predicting their Victims
Boshmaf, Y., Ripeanu, M., Beznosov, K., & Santos‐Neto, E. (2015) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2808769.2808772 · Cited by 28 · see citing works
“While we are the first to leverage potential victims to thwart fake accounts, others have analyzed victim accounts in order to understand the larger cyber criminal ecosystem for OSN abuse [46].”
BackgroundWhat they do in shadows: Twitter underground follower market
Aggarwal, A., & Kumaraguru, P. (2015) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2015.7232959 · Cited by 30 · see citing works
“We also show that merchant popularity does not have any correlation with the quality of service provided by the merchant to its customers.”
BackgroundMethodInfluentialShah, N., Lamba, H., Beutel, A., & Faloutsos, C. (2017) · Proceedings · doi:10.1109/icdm.2017.140 · Cited by 32 · see citing works
“[12] compares growth rates of accounts with legitimate and fraudulent followers.”
BackgroundMeasuring, Characterizing, and Detecting Facebook Like Farms
Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2017) · ACM Transactions on Privacy and Security · doi:10.1145/3121134 · Cited by 27 · see citing works
“In a similar study, Stringhini et al. [43, 46] The difference in F1-Score obtained when all like farm users coordinate and mimic sets of lexical and non-lexical features of baseline users.”
ResultThe Art and Craft of Fraudulent App Promotion in Google Play
Rahman, M., Hernandez, N., Recabarren, R., Ahmed, S. I., & Carbunar, B. (2019) · Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3319535.3345658 · Cited by 24 · see citing works
“[82] studied Twitter follower markets by purchasing followers from di erent merchants, and discovered patterns and detected market-controlled accounts in the wild.”
BackgroundBlackmarket-Driven Collusion Among Retweeters–Analysis, Detection, and Characterization
Dutta, H. S., & Chakraborty, T. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2953331 · Cited by 23 · see citing works
“[17] examined”
Making up Audience: Media Bots and the Falsification of the Public Sphere
Santini, R. M., Salles, D., Tucci, G., Ferreira, F., & Grael, F. (2020) · Communication Studies · doi:10.1080/10510974.2020.1735466 · Cited by 22 · see citing works
“Hence, a supply and demand business opportunity has given rise to a “ Twitter follower market ” to arti fi cially increase audience metrics on the platform (Stringhini et al., 2013).”
BackgroundDetecting and Analyzing Collusive Entities on YouTube
Dutta, H. S., Jobanputra, M., Negi, H., & Chakraborty, T. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3477300 · Cited by 24 · see citing works
“el to detect spammers in online review stores. [43] proposed an automated approach to detect collusion behavior in online question-answering systems. Other studies identified fake followers on Twitter [44, 45, 46, 47]. [48] and [49] are some of those who used network-centric properties to detect fake followers. Fake Fo…”
MethodChetan, A., Joshi, B., Dutta, H. S., & Chakraborty, T. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291010 · Cited by 22 · see citing works
The Pod People: Understanding Manipulation of Social Media Popularity via Reciprocity Abuse
Weerasinghe, J., Flanigan, B., Stein, A., McCoy, D., & Greenstadt, R. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380256 · Cited by 21 · see citing works
“Stringhini et al. [25] investigates Twitter follower markets which sell Twitter follows using automated methods.”
CATS: Cross-Platform E-Commerce Fraud Detection
Weng, H., Ji, S., Duan, F., et al. (2019) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2019.00203 · Cited by 21 · see citing works
“…cannot be directly applied to detect e-commerce frauds, simply because they are either dedicated to one specific platform [4], which cannot be easily extended to other platforms, or designed for other application domains, such as click fraud [5], content fraud [6] and account fraud [7], [8].”
BackgroundSecurity and Privacy of Cloud-Based Online Online Social Media: A Survey
Yadav, U. S., Gupta, B. B., Peraković, D., García‐Peñalvo, F. J., & Cvitić, I. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-90462-3_14 · Cited by 21 · see citing works
Measuring and mitigating oauth access token abuse by collusion networks
Farooqi, S., Zaffar, F., Leontiadis, N., & Shafiq, Z. (2017) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3131365.3131404 · Cited by 20 · see citing works
“For example, Twitter implements OAuth 1.0a6 while Facebook, YouTube, Instagram, and SoundCloud implement OAuth 2.0.”
BackgroundInfluentialTravailleurs du like , faussaires de l’e-réputation
Beauvisage, T., & Mellet, K. (2016) · Réseaux · doi:10.3917/res.197.0069 · Cited by 18 · see citing works
Mariconti, E., Onaolapo, J. O., Ahmad, S. S., Nikiforou, N., Egele, M., Nikiforakis, N., & Stringhini, G. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052589 · Cited by 18 · see citing works
Self-citationGuided socialbots: Infiltrating the social networks of specific organizations’ employees
Elyashar, A., Fire, M., Kagan, D., & Elovici, Y. (2014) · AI Communications · doi:10.3233/aic-140650 · Cited by 17 · see citing works
“…data that disclose personal and sensitive information about users may cause security risks including identity theft [5]; inference attacks [33]; spreading spam [22,26]; privacy threats [7,38]; malware [2]; fake profiles or sybils [10,48]; socialbots [7,8,17,18]; and sexual harassment [54,56].”
Pay me and i'll follow you: detection of crowdturfing following activities in microblog environment
Liu, Y., Liu, Y., Zhang, M., & Ma, S. (2016) · International Joint Conference on Artificial Intelligence · Cited by 16 · see citing works
Exploiting abused trending topics to identify spam campaigns in Twitter
Antonakaki, D., Polakis, I., Athanasopoulos, E., et al. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0354-9 · Cited by 16 · see citing works
“Either as a paid service or a free one, these ‘‘get-more-followers’’ campaigns (Stringhini et al. 2013) consist a serious threat since it is a multi-million dollar scheme (Perlroth 2013).”
Reputation-Based Approach Toward Web Content Credibility Analysis
Mahmood, S., Ghani, A., Daud, A., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2943747 · Cited by 16 · see citing works
“These features are very critical since users can easily obtain thousands of followers by twitter marketers [13], [14].”
BackgroundUnveiling Fake Accounts at the Time of Registration: An Unsupervised Approach
Liang, X., Yang, Z., Wang, B., Hu, S., Yang, Z., Yuan, D., Gong, N. Z., Li, Q. L., & He, F. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467094 · Cited by 16 · see citing works
A twitter recruitment intelligent system: association rule mining for smoking cessation
Hamed, A. A., Wu, X., & Rubin, A. S. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0212-6 · Cited by 16 · see citing works
“Stringhini et al. (2013) analyzed the reputation of Twitter accounts using the count of followers.”
BackgroundDiffusion Dynamics of Energy Saving Practices in Large Heterogeneous Online Networks
Mohammadi, N., Wang, Q., & Taylor, J. E. (2016) · PLoS ONE · doi:10.1371/journal.pone.0164476 · Cited by 16 · see citing works
Discovery of the Twitter Bursty Botnet
Echeverría, J., Besel, C., & Zhou, S. (2018) · Security science and technology · doi:10.1142/9781786345646_007 · Cited by 16 · see citing works
Followers or Phantoms? An Anatomy of Purchased Twitter FollowersPreprint
Aggarwal, A., & Kumaraguru, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.1534 · Cited by 17 · see citing works
“Previous studies have shown that such compromised accounts are used to spread malicious URLs and tweets promoting the merchants [19].”
BackgroundInfluentialFraud De-Anonymization for Fun and Profit
Hernandez, N., Rahman, M., Recabarren, R., & Carbunar, B. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243770 · Cited by 16 · see citing works
“Decreasing account reuse decreases profits, as reputable accounts are often preferred in search rank fraud jobs [22, 68, 87].”
BackgroundFollowers or Fradulents? An Analysis and Classification of Twitter Followers Market Merchants
Singh, M., Bansal, D., & Sofat, S. (2016) · Cybernetics & Systems · doi:10.1080/01969722.2016.1237227 · Cited by 15 · see citing works
“They further extended their work (Stringhini et al. 2013) by detecting operations of Twitter followers market by identifying and sus-pending the accounts of the customers of these malicious enterprises.”
BackgroundAggarwal, A., Kumar, S., Bhargava, K., & Kumaraguru, P. (2018) · ACM Symposium on Applied Computing · doi:10.1145/3167132.3167318 · Cited by 15 · see citing works
“3% customers of freemium marketplaces in our dataset [24], (3) Although previous studies have labeled freemium customers as victims and identified them by looking for promotional content in their tweets [20]; we find less than 30% of freemium market customers posting advertising content.”
BackgroundCanaryTrap: Detecting Data Misuse by Third-Party Apps on Online Social Networks
Farooqi, S., Musa, M. B., Shafiq, Z., et al. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0076 · Cited by 14 · see citing works
“Second, prior work has used honeypots to monitor attacker interactions with honeypots without explicitly handing over their access, such as by purchasing fake followers on Facebook, Instagram, or Twitter [36, 45, 74].”
BackgroundDistance-based customer detection in fake follower markets
Jang, B., Jeong, S., & Kim, C.-K. (2018) · Information Systems · doi:10.1016/j.is.2018.12.001 · Cited by 14 · see citing works
Blackmarket-Driven Collusion on Online Media: A Survey
Dutta, H. S., & Chakraborty, T. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3517931 · Cited by 13 · see citing works
“[124, 125, 127] for a detailed study of the Twitter follower markets.”
BackgroundAn Empirical Investigation of Ecommerce-Reputation-Escalation-as-a-Service
Xu, H., Liu, D., Wang, H., & Stavrou, A. (2017) · ACM Transactions on the Web · doi:10.1145/2983646 · Cited by 12 · see citing works
“Several works studied the underground economy related to Twitter, including the markets for selling fraudulent accounts [Thomas et al. 2011, 2013] and Twitter followers [Stringhini et al. 2013].”
BackgroundSupervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach
El-Mawass, N., Honeiné, P., & Vercouter, L. (2018) · Multidisciplinary International Social Networks Conference (MISNC) · doi:10.1145/3227696.3227712 · Cited by 12 · see citing works
Fake Profile Identification Using Machine Learning
Sudhakar, T., Gogineni, B. C., & Vijaya, J. (2022) · IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) · doi:10.1109/wiecon-ece57977.2022.10150753 · Cited by 12 · see citing works
“According to the authors, two main categories of bills chase the ”client”: hacked accounts and fake accounts (often known as ”sybils”), whose suppliers do not assume that the number of their followers is growing [10].”
BackgroundTweet! – And I Can Tell How Many Followers You Have
Klotz, C., Ross, A., Clark, E., et al. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-06538-0_24 · Cited by 12 · see citing works
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware DetectionPreprint
Sun, R., Yuan, X., He, P. L., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01145 · Cited by 12 · see citing works
Lee, K., Webb, S., & Ge, H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.0574 · Cited by 12 · see citing works
“(Stringhini et al. 2013) and Thomas et al.”
BackgroundCredibility assessment of financial stock tweets
Evans, L., Owda, M., Crockett, K. A., et al. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114351 · Cited by 11 · see citing works
“…a user ’ s follower count) is that they can be artificially inflated, as users can obtain thousands of followers from Twitter follower markets within minutes (Stringhini et al., 2013), giving a false indication that the user has a large follower base and is credible (De Micheli & Stroppa, 2013).”
BackgroundSoboleva, A., Burton, S., & Khan, A. (2015) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-4666-8408-9.ch001 · Cited by 11 · see citing works
Stress Testing the Booters: Understanding and Undermining the Business of DDoS ServicesPreprint
Karami, M., Park, Y., & McCoy, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.03410 · Cited by 10 · see citing works
“In this respect our work is more along the lines of studies focused on criminal support services, such as email spam delivery [13, 28], fake social links [29] and fake account creation [32].”
BackgroundDetecting Spam on Twitter via Message-Passing Based on Retweet-Relation
Chen, P.-C., Lee, H.-M., Tyan, H.-R., Wu, J.-S., & Wei, T.-E. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13987-6_6 · Cited by 8 · see citing works
Protecting Online Social Networks Profiles by Hiding Sensitive Data Attributes
Abdul-Kader, H. M., Elabd, E., & Ead, W. M. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.04.004 · Cited by 9 · see citing works
Twitter Influencers or Cheated Buyers?
Zenonos, S., Tsirtsis, A., & Tsapatsoulis, N. (2018) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00049 · Cited by 7 · see citing works
“[12] have focused on the detection of fake followers on Twitter.”
BackgroundWhy allowing profile name reuse is a bad idea
Mariconti, E., Onaolapo, J. O., Ahmad, S. S., Nikiforou, N., Egele, M., Nikiforakis, N., & Stringhini, G. (2016) · European Workshop on System Security · doi:10.1145/2905760.2905762 · Cited by 7 · see citing works
“Enrico Mariconti was funded by the EPSRC under grant 1490017, while Jeremiah Onaolapo was supported by the Petroleum Technology Development Fund (PTDF), Nigeria.”
BackgroundMethodSelf-citationDetecting and Mitigating the Effect of Manipulated Reputation on Online Social Networks
Aggarwal, A. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2888601 · Cited by 6 · see citing works
“There has been some work done to identify Online Blackmarkets by finding websites on search engines using keywords like buy cheap followers and purchase Facebook likes [16].”
BackgroundMe, Myself and My Killfie: Characterizing and Preventing Selfie DeathsPreprint
Lamba, H., Bharadhwaj, V., Vachher, M., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01911 · Cited by 7 · see citing works
“The authors specifically analyzed death of a young teenager in Lebanon who died moments after taking a selfie near a golden SUV, that blew up.”
BackgroundAdverSPAM: Adversarial SPam Account Manipulation in Online Social Networks
Concone, F., Gaglio, S., Giammanco, A., Re, G. L., & Morana, M. (2024) · ACM Transactions on Privacy and Security · doi:10.1145/3643563 · Cited by 7 · see citing works
Comparative Analysis of Fake Account Detection Using Machine Learning Algorithms
Padmavathi, A., & Vaisshnavi, K. B. (2024) · International Conference on Artificial Intelligence and Signal Processing (AISP) · doi:10.1109/aisp61711.2024.10870733 · Cited by 6 · see citing works
Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning
Shan, M. F., Verma, V., Dwivedi, A., Perwej, Y., & Srivastava, A. K. (2023) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit23903126 · Cited by 8 · see citing works
- Authors
- Stringhini, G., Wang, G., Egele, M., Kruegel, C., Vigna, G., Zheng, H., & Zhao, B. Y.
- Affiliations
- University of California, Santa Barbara (US)
- Carnegie Mellon University (US)
- University of California, Santa Barbara, Santa Barbara, CA, USA
- Venue
- Proceedings of the 2013 conference on Internet measurement conference
- Publisher
- ACM
- Published
- 2013-10-23
- Pages
- 163-176
- OpenAlex
- W2011366667
- Cited by
- 169
- References
- 30
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 11 · 2023: 3 · 2022: 5 · 2021: 10 · 2020: 14 · 2019: 12 · 2018: 29 · 2017: 22 · 2016: 20 · 2015: 25 · 2014: 15 · 2013: 1
- FWCI
- 16.40
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- fake accounts, pyramid schemes, social media influence, fraud detection, microblogging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-12.
Statistics and arithmetic are recomputed, not judged.
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