Multi-level framework for anomaly detection in social networking

Abstract

Purpose

The purpose of this paper is to propose a structured multilevel system that will distinguish the anomalies present in different online social networks (OSN).

Design/methodology/approach

Author first reviewed the related work, and then, the research model designed was explained. Furthermore, the details regarding Levels 1 and 2 were narrated.

Findings

By using the proposed technique, F_Score obtained for Twitter and Facebook data set was 96.22 and 94.63, respectively.

Research limitations/implications

Four data sets were used for the experiment and the acquired outcomes demonstrate enhancement over the current existing frameworks.

Originality/value

This paper designed a multilevel framework that can be used to detect the anomalies present in the OSN.

Citation

Khamparia, A., Pande, S., Gupta, D., Khanna, A. and Sangaiah, A.K. (2020), "Multi-level framework for anomaly detection in social networking", Library Hi Tech, Vol. 38 No. 2, pp. 350-366. https://doi.org/10.1108/LHT-01-2019-0023