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A Pilot Study and Survey on Methods for Anomaly Detection in Online Social Networks

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Human-Centric Smart Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 316))

Abstract

An anomaly in online social networks is an unusual occurrence that deviates greatly from the standard. Financial fraud, social spam, and network intrusion can all be prevented with anomaly detection, which is designed to find this infrequent observational data. Anomaly detection on social media is crucial for preventing hostile actions like bullying, the spread of fraud information, etc. New forms of aberrant behavior have come to the forefront due to the increasing popularity of social media, creating alarm among a variety of groups. While classic anomaly detection problems have received a lot of attention, we have noticed a rise in research attention in the emerging area of social media anomaly detection. We classify anomalies as static versus dynamic, attributed versus unattributed, and investigate methods for identifying various forms of anomalies. We also analyze the benefits and disadvantages of various methodologies in each category, as well as the problems that this study area presents. Finally, we present challenges for detecting graph anomalies.

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Acknowledgements

This work is acknowledged under Integral University manuscript No. IU/R&D/2022-MCN0001490

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Authors and Affiliations

  1. Integral University, Lucknow, India

    Wasim Khan & Mohammad Haroon

Authors
  1. Wasim Khan
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  2. Mohammad Haroon
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Correspondence to Wasim Khan .

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Editors and Affiliations

  1. Rajnagar Mahavidyalaya, Rajnagar, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering (AI and ML), Bengal Institute of Technology, Kolkata, India

    Jyoti Sekhar Banerjee

  3. Department of Computer Science and Electronics, Kyushu Institute of Technology, Fukuoka, Japan

    Mario Köppen

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Khan, W., Haroon, M. (2023). A Pilot Study and Survey on Methods for Anomaly Detection in Online Social Networks. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M. (eds) Human-Centric Smart Computing. Smart Innovation, Systems and Technologies, vol 316. Springer, Singapore. https://doi.org/10.1007/978-981-19-5403-0_10

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