skip to main content
10.1145/3007818.3007838acmotherconferencesArticle/Chapter ViewAbstractPublication PagesedbConference Proceedingsconference-collections
research-article

Spammer detection for real-time big data graphs

Published: 17 October 2016 Publication History

Abstract

In recent years, prodigious explosion of social network services may trigger new business models. However, it has negative aspects such as personal information spill or spamming, as well. Amongst conventional spam detection approaches, the studies which are based on vertex degrees or Local Clustering Coefficient have been caused false positive results so that normal vertices can be specified as spammers. In this paper, we propose a novel approach by employing the circuit structure in the social networks, which demonstrates the advantages of our work through the experiment.

References

[1]
http://an.kaist.ac.kr/traces/WWW2010.html.
[2]
A. H. Wang, "Don't follow me: Spam detection in Twitter," SECRYPT, pp. 1--10, 2010.
[3]
A. Lahmadi, L. Delosieres, and O. Festor, "Hinky: Defending against Text-Based Message Spam on Smartphones," ICC, pp. 1--5, 2011.
[4]
C. Chen, F. Li, B. C. Ooi, and S. Wu, "TI: An Efficient Indexing Mechanism for Real-Time Search on Tweets," SIGMOD, pp. 649--660, 2011.
[5]
G. L. Ted, "Network Science: Theory and Applications," Wiley, pp. 190--214, 2009.
[6]
H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, a social network or a news media?," WWW, pp. 591--600, 2010.
[7]
J. D. Falk, and M. S. Kucherawy, "Battling Spam: The Evolution of Mail Feedback Loops," IEEE Internet Computing, vol. 14(6), pp. 68--71, 2010.
[8]
M. Z. Rafique, N. Alrayes, and M. K. Khan, "Application of evolutionary algorithms in detecting SMS spam at access layer," GECCO, pp. 1787--1794, 2011.
[9]
P. O. Boykin, and V. P. Roychowdhury, "Leveraging social networks to fight spam," IEEE Computer, vol. 38(4), pp. 61--68, 2005.
[10]
R. Williams, "Faster all-pairs shortest paths via circuit complexity," STOC, pp. 664--673, 2014.
[11]
S. Havlin, "Phone Infections," Science, vol. 324(5930), pp. 1023--1024, 2009.
[12]
S. Ghosh, B. Viswanath, F. Kooti, N. Sharma, G. Korlam, F. Benevenuto, N. Ganguly, and K. Gummadi, "Understanding and combating link farming in the twitter social network," WWW, pp. 61--70, 2012.
[13]
S. Milgram, "The Small World Problem," Psychology Today, Vol. 2, pp. 60--67, 1967.
[14]
S. Pettie, "A new approach to all-pairs shortest paths on real-weighted graphs, "Theoretical Computer Science," vol. 312(1), pp. 47--74, 2004.
[15]
T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, "Introduction to Algorithms," MIT Press and McGraw-Hill, pp. 636--640, 2001.
[16]
U. Kang, D. H. Chau, and C. Faloutsos, "Mining large graphs: Algorithms, inference, and discoveries," ICDE, pp. 243--254, 2011.
[17]
W. Lee, J. Lee, J. Song, and S. Eom, "Maximum Reliable Tree for Social Network Search," DASC, pp. 1243--1249, 2011.
[18]
X. Hu, J. Tang, H. Liu, "Leveraging knowledge across media for spammer detection in microblogging," SIGIR, pp. 547--556, 2014.
[19]
X. Hu, J. Tang, H. Liu, "Online Social Spammer Detection," AAAI, pp. 59--65, 2014.
[20]
Y. Shin, S. Myers, M. Gupta, and P. Radivojac, "A link graph-based approach to identify forum spam," Security Comm. Networks, Vol 8(2), pp. 176--178, 2015.
[21]
Z. Gyongyi, and H. Garcia-Molina, "Link Spam Alliances," VLDB, pp. 517--528, 2005.
[22]
Z. Gyongyi, P. Berkhin, H. Garcia-Molina, and J. Pedersen, "Link Spam Detection Based on Mass Estimation," VLDB, pp. 439--450, 2006.

Index Terms

  1. Spammer detection for real-time big data graphs

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    EDB '16: Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory
    October 2016
    183 pages
    ISBN:9781450347549
    DOI:10.1145/3007818
    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]

    Sponsors

    • KoDB: Korea Database Agency
    • Nara System: Nara System
    • 2e consulting: 2e consulting

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 October 2016

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. circuit
    2. graph
    3. local clustering coefficient
    4. shortest path
    5. spammer

    Qualifiers

    • Research-article

    Conference

    EDB
    Sponsor:
    • KoDB
    • Nara System
    • 2e consulting
    EDB: 2016 International Conference on Emerging Databases
    October 17 - 19, 2016
    Jeju, Republic of Korea

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 85
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 24 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media