Abstract

Involvement of social media like personal, business and political propaganda activities, attracts anti-social activities and has also increased. Anti-social elements get a wider platform to spread negativity after hiding their identity behind fake and false profiles. In this paper, an analytical and methodological user identification framework is developed to significantly binds implicit and explicit link relationship over the end-users graphical perspective. Identify malicious user, its communal information and sockpuppet node. Apart from that, this work provides the concept of the deep neural network approach over the graphical and linguistic perspective of end-user to classify as malicious, fake and genuine. This concept also helps identify the trade-off between the similarity of nodes attributes and the density of connections to classifying identical profile as sockpuppet over social media.

Highlights

  • Social media has entered our lives in many areas, among 7.5 billion people globally; 3.1 billion are active on social media

  • Ungenuine user-profiles opened by users for mischievous purposes in social networks such as Facebook, Twitter and LinkedIn are called fake accounts

  • Domenico et al [2] state that false profiles on social networks are those that do not comply with the terms and conditions established by the platform, they do not belong to real people, they do not belong to the person they indicate, and they pretend to be real profiles existing

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Summary

INTRODUCTION

Social media has entered our lives in many areas, among 7.5 billion people globally; 3.1 billion are active on social media. Domenico et al [2] state that false profiles on social networks are those that do not comply with the terms and conditions established by the platform, they do not belong to real people, they do not belong to the person they indicate, and they pretend to be real profiles existing They indicate fake, manual or artisan profiles (created by people) and those generated and manipulated manually and automatically (bots or robots). Apart from that, this work provides the concept of the deep neural network approach over the graphical and linguistic perspective of end-user to classify as malicious, fake and genuine This concept helps identify the tradeoff between the similarity of nodes attributes and the density of connections for Influence maximization. While the evaluation results are mentioned in the fourth section, results and suggestions are given in the last www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 12, No 3, 2021 section

RELATED WORK
PROPOSED WORK
Data Extraction
User Feature Vector
Relationship Identification
ENVIRONMENTAL SETUP AND RESULT ANALYSIS
Evaluation Parameter
CONCLUSION
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