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Twitter games: how successful spammers pick targets

Published: 03 December 2012 Publication History

Abstract

Online social networks, such as Twitter, have soared in popularity and in turn have become attractive targets of spam. In fact, spammers have evolved their strategies to stay ahead of Twitter's anti-spam measures in this short period of time. In this paper, we investigate the strategies Twitter spammers employ to reach relevant target audiences. Due to their targeted approaches to send spam, we see evidence of a large number of the spam accounts forming relationships with other Twitter users, thereby becoming deeply embedded in the social network.
We analyze nearly 20 million tweets from about 7 million Twitter accounts over a period of five days. We identify a set of 14,230 spam accounts that manage to live longer than the other 73% of other spam accounts in our data set. We characterize their behavior, types of tweets they use, and how they target their audience. We find that though spam campaigns changed little from a recent work by Thomas et al., spammer strategies evolved much in the same short time span, causing us to sometimes find contradictory spammer behavior from what was noted in Thomas et al.'s work. Specifically, we identify four major strategies used by 2/3rd of the spammers in our data. The most popular of these was one where spammers targeted their own followers. The availability of various kinds of services that help garner followers only increases the popularity of this strategy. The evolution in spammer strategies we observed in our work suggests that studies like ours should be undertaken frequently to keep up with spammer evolution.

References

[1]
Benevenuto, F., Magno, G., Rodrigues, T., and Almeida, V. Detecting spammers on Twitter. In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS) (2010).
[2]
Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., and Chao Zhang, K. R. Identifying video spammers in online social networks. In Workshop on Adversarial Information Retrieval on the Web (AirWeb), held in conjunction with the International World Wide Web (WWW) conference (2008).
[3]
Chu, Z., Gianvecchio, S., and Wang, H. Who is tweeting on Twitter: Human, bot, or cyborg? In Annual Computer Security Applications Conference (ACSAC) (2010).
[4]
Gao, H., Chen, Y., Lee, K., Palsetia, D., and Choudhary, A. Towards online spam filtering in social networks. In ISOC Network and Distributed System Security Symposium (NDSS) (2012).
[5]
Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., and Zhao, B. Y. Detecting and characterizing social spam campaigns. In ACM/USENIX Internet Measurement Conference (IMC) (2010).
[6]
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., and Gummadi, K. P. Understanding and combating link farming in the Twitter social network. In International Conference on World Wide Web (WWW) (2012).
[7]
Grier, C., Thomas, K., Paxson, V., and Zhang, M. @spam: the underground on 140 characters or less. In ACM Conference on Computer and Communications Security (CCS) (2010).
[8]
Jones, K. S. A statistical interpretation of term specificity and its application in retrieval. In Journal of Documentation, Vol. 28 Issue: 1, pp. 11--21 (1972).
[9]
Lee, K., Caverlee, J., Kamath, K. Y., and Cheng, Z. Detecting collective attention spam. In Workshop on WebQuality, held in conjunction with International World Wide Web (WWW) conference (2012).
[10]
Lee, K., Caverlee, J., and Webb, S. Uncovering social spammers: Social honeypots + machine learning. In ACM Special Interest Group on Information Retrieval (SIGIR) Conference (2010).
[11]
Lee, S., and Kim, J. Warningbird: Detecting suspicious URLs in twitter stream. In ISOC Network and Distributed System Security Symposium (NDSS) (2012).
[12]
Song, J., Lee, S., and Kim, J. Spam filtering in twitter using sender-receiver relationship. In International Symposium on Recent Advances in Intrusion Detection (RAID) (2011).
[13]
Stringhini, G., Egele, M., Kruegel, C., and Vigna, G. Poultry markets: On the underground economy of Twitter followers. In ACM Workshop on Online Social Networks (WOSN) (2012).
[14]
Stringhini, G., Kruegel, C., and Vigna, G. Detecting spammers on social networks. In Annual Computer Security Applications (ACSAC) conference (2010).
[15]
Thomas, K., Grier, C., Ma, J., Paxson, V., and Song, D. Design and evaluation of a real-time URL spam filtering service. In IEEE Symposium on Security and Privacy (2011).
[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).
[17]
TweetAdder, 2012. http://www.tweetadder.com.
[18]
TweetAttacks manual, 2012. http://www.scribd.com/doc/59395233/Manual-Tweet-Attacks.
[19]
Twitter rules, 2012. https://support.twitter.com/entries/18311-the-twitter-rules.
[20]
Twitter size, 2012. http://blog.twitter.com/2012/03/twitter-turns-six.html.
[21]
Wang, A. H. Don't follow me: Spam detection in Twitter. In International Conference on Security and Cryptography (SECRYPT) (2010).
[22]
Yang, C., Harkreader, R., and Gu, G. Die free or live hard? Empirical evaluation and new design for fighting evolving Twitter spammers. In International Symposium on Recent Advances in Intrusion Detection (RAID) (2011).
[23]
Yang, C., Harkreader, R., Zhang, J., Shin, S., and Gu, G. Analyzing spammers' social networks for fun and profit: A case study of cyber criminal ecosystem on Twitter. In International Conference on World Wide Web (WWW) (2012).

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    cover image ACM Other conferences
    ACSAC '12: Proceedings of the 28th Annual Computer Security Applications Conference
    December 2012
    464 pages
    ISBN:9781450313124
    DOI:10.1145/2420950
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 03 December 2012

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    Author Tags

    1. Twitter
    2. online social networks (OSNs)
    3. spam

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    ACSAC '12
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    • ACSA
    ACSAC '12: Annual Computer Security Applications Conference
    December 3 - 7, 2012
    Florida, Orlando, USA

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    ACSAC '12 Paper Acceptance Rate 44 of 231 submissions, 19%;
    Overall Acceptance Rate 104 of 497 submissions, 21%

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    Cited By

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    • (2024)CGNN: A Compatibility-Aware Graph Neural Network for Social Media Bot DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339641311:5(6528-6543)Online publication date: Oct-2024
    • (2023)Enhancing Sybil Detection via Social-Activity Networks: A Random Walk ApproachIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315170120:2(1213-1227)Online publication date: 1-Mar-2023
    • (2021)Studying the Community of Trump Supporters on Twitter during the 2020 US Presidential Election via Hashtags #maga and #trump2020Journalism and Media10.3390/journalmedia20400422:4(709-731)Online publication date: 18-Nov-2021
    • (2020)An Evaluation of Low-Quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?Computational Science and Its Applications – ICCSA 202010.1007/978-3-030-58799-4_42(572-585)Online publication date: 1-Oct-2020
    • (2019)Social Network's Security Related to HealthcareIntelligent Pervasive Computing Systems for Smarter Healthcare10.1002/9781119439004.ch4(91-113)Online publication date: 5-Jul-2019
    • (2018)Sybil Detection in Social-Activity Networks: Modeling, Algorithms and Evaluations2018 IEEE 26th International Conference on Network Protocols (ICNP)10.1109/ICNP.2018.00015(44-54)Online publication date: Sep-2018
    • (2018)What the fake? Assessing the extent of networked political spamming and bots in the propagation of #fakenews on TwitterOnline Information Review10.1108/OIR-02-2018-0065Online publication date: 11-Oct-2018
    • (2017)A security approach based on honeypots: Protecting Online Social network from malicious profilesAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0203262:3(198-204)Online publication date: Apr-2017
    • (2017)A study on real-time low-quality content detection on Twitter from the users’ perspectivePLOS ONE10.1371/journal.pone.018248712:8(e0182487)Online publication date: 9-Aug-2017
    • (2017)Thank You For Being A Friend: An Attacker View on Online-Social-Network-Based Sybil Defenses2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW)10.1109/ICDCSW.2017.67(157-162)Online publication date: Jun-2017
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