Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning

Ezarfelix, Juandreas; Jeffrey, Nathannael; Sari, Novita

doi:10.21512/emacsjournal.v4i1.8076

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Their work also presents the results of a thorough analysis that draws on existing literature to determine the most efficient approach. Through an exhaustive series of assessments and evaluations, their results consistently emphasize the effectiveness of neural networks as the superior method for detecting counterfeit accounts [6]. Kaubiyal, J. et al have utilized a feature-based strategy to enhance the detection of fraudulent profiles on social media platforms.…”

Section: Related Workmentioning

confidence: 98%

Detecting Fake Social Media Profiles Using the Majority Voting Approach

Patil,

Pattewar,

Punjabi

et al. 2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The rise of social media platforms has brought about a concerning surge in the creation of fraudulent user profiles, with intentions ranging from spreading false information and perpetrating fraud to engaging in cyberbullying. The detection of these deceptive profiles has emerged as a critical imperative to safeguard the trustworthiness and security of online communities.OBJECTIVES: This paper focused on the detection and identification of fake social media profiles.METHODS: This paper introduces an innovative approach for discerning and categorizing counterfeit social media profiles by leveraging the majority voting approach. The proposed methodology integrates a range of machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost and K-Nearest Neighbors each tailored to capture distinct facets of user behavior and profile attributes. This amalgamation of diverse methods results in an ensemble of classifiers, which are subsequently subjected to a majority voting mechanism to render a conclusive judgment regarding the legitimacy of a given social media profile.RESULTS: We conducted thorough experiments using a dataset containing both legitimate and fake social media profiles to determine the efficiency of our methodology. Our findings substantiate that the Majority Voting Technique surpasses individual classifiers, attaining an accuracy rate of 99.12%, a precision rate of 99.12%, a recall rate of 99.12%, and an F1-score of 99.12%.CONCLUSION: The results show that the majority vote method is reliable for detecting and recognising fake social media profiles.

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Section: Related Workmentioning

confidence: 98%

Detecting Fake Social Media Profiles Using the Majority Voting Approach

Patil,

Pattewar,

Punjabi

et al. 2024

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

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Machine learning-based social media bot detection: a comprehensive literature review

Aljabri

Zagrouba

Shaahid

et al. 2023

Soc. Netw. Anal. Min.

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In today’s digitalized era, Online Social Networking platforms are growing to be a vital aspect of each individual’s daily life. The availability of the vast amount of information and their open nature attracts the interest of cybercriminals to create malicious bots. Malicious bots in these platforms are automated or semi-automated entities used in nefarious ways while simulating human behavior. Moreover, such bots pose serious cyber threats and security concerns to society and public opinion. They are used to exploit vulnerabilities for illicit benefits such as spamming, fake profiles, spreading inappropriate/false content, click farming, hashtag hijacking, and much more. Cybercriminals and researchers are always engaged in an arms race as new and updated bots are created to thwart ever-evolving detection technologies. This literature review attempts to compile and compare the most recent advancements in Machine Learning-based techniques for the detection and classification of bots on five primary social media platforms namely Facebook, Instagram, LinkedIn, Twitter, and Weibo. We bring forth a concise overview of all the supervised, semi-supervised, and unsupervised methods, along with the details of the datasets provided by the researchers. Additionally, we provide a thorough breakdown of the extracted feature categories. Furthermore, this study also showcases a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to explore.

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Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning

Cited by 4 publications

References 4 publications

Detecting Fake Social Media Profiles Using the Majority Voting Approach

Detecting Fake Social Media Profiles Using the Majority Voting Approach

Machine learning-based social media bot detection: a comprehensive literature review

Examining the correlation between metrics in the Instagram social network to identify fake pages and improve marketing

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