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Deep learning methods for anomalies detection in social networks using multidimensional networks and multimodal data: a survey

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Abstract

Anomaly in Online Social Network can be designated as an unusual or illegal activity of an individual. It can also be considered as an outlier or a surprising truth. Due to the emergence of social networking sites such as Facebook, Instagram, etc., the number of negative impacts of aggressive and bullying phenomena has increased exponentially. Anomaly detection is a problem of crucial importance which has attracted researchers since the 2000s. This problem is often carried out, thanks to deep learning, artificial intelligence and statistics. Several methods have been devoted to solving the problem of detecting abnormal behavior on social media, which are kept under three different types: structural methods which are based on the analysis of graphs of social networks, behavioral methods which are based on the extraction and analysis of user activities and hybrid methods which combine the two types of methods mentioned above. This survey reviews various methods of data mining for the detection of anomalies to provide a better assessment that can facilitate the understanding of this area.

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Notes

  1. Weibo Community Management Center for false rumor category: ehttp://service.account.Weibo.com/?type=5.

  2. Abnormal Event Detection at 1000 FPS in MATLAB: http://www.cse.cuhk.edu.hk/ leojia/projects/detectabnormal/index.html.

  3. Privacy Preserving Crowd Monitoring: Counting People without People Models or Tracking: http://www.svcl.ucsd.edu/projects/peoplecnt/.

  4. Enron Email Dataset http://www.cs.cmu.edu/ enron/.

  5. John Jay & ARTIS Transnational Terrorism Database: http://doitapps.jjay.cuny.edu/jjatt/index.php.

  6. The Fake Project: http://wafi.iit.cnr.it/theFakeProject/.

  7. Friendly note: https://www.fastfollowerz.com/closed/.

  8. Twitterboost.com: http://twitterboost.com/.

  9. Insider Threat Test Dataset: https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=508099.

  10. NATOPS Aircraft Handling Signals Database: http://groups.csail.mit.edu/mug/natops/.

  11. Discover scientific knowledge and stay connected to the world of science: https://www.researchgate.net/.

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Authors and Affiliations

  1. SAMOVAR, Telecom SudParis, Institut Polytechnique de Paris, 19 place Marguerite Perey, 91120, Palaiseau, France

    Nour El Houda Ben Chaabene & Amel Bouzeghoub

  2. RIADI, National School of Computer Science, Campus Universitaire de la Manouba,2010, Manouba, Tunisia

    Nour El Houda Ben Chaabene & Henda Hajjami Ben Ghezala

  3. LIMTIC, Higher Institute of Computer Science, 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia

    Ramzi Guetari

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  1. Nour El Houda Ben Chaabene
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Ben Chaabene, N., Bouzeghoub, A., Guetari, R. et al. Deep learning methods for anomalies detection in social networks using multidimensional networks and multimodal data: a survey. Multimedia Systems 28, 2133–2143 (2022). https://doi.org/10.1007/s00530-020-00731-z

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