As online spamming has posed serious security threat to cyberspaces, relevant detection technologies based on artificial intelligence is being widely studied. Existing related research literatures can be divided into two classes: methods based on behavior patterns and methods based on semantic patterns. In order to better solve this challenge, we clearly proposed a semantics and behaviors-collaboratively driven spammer detection Method (Co-Sdm) in social networks. In particular, long-term behavior and semantic pattern of multi-source information fusion and collaborative coding is introduced. Therefore, a more comprehensive feature space representation can be captured to further detect spammers and improve the ability to deal with spam. In the experiment, we carried out a series of experiments under different scenarios and basic parameters based on two real datasets. Compare the high efficiency of Co-Sdm clearly proposed with the three baselines of multiple evaluation index values. The test results show that, compared with the baseline, the average characteristic of Co-Sdm has improved by about 5%.


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    Titel :

    A Semantics and Behaviors-Collaboratively Driven Spammer Detection Method


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Guo, Zhiwei (Autor:in) / Yang, Jinhui (Autor:in) / Yu, Keping (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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