Identification of Fake accounts in social media using machine learning

Shreya, Kotra; Kothapelly, Amith; Deepika, V; Hariharan, Shanmugasundaram

doi:10.1109/icerect56837.2022.10060194

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…Few features are employed in the developed hybrid SVM model and the profiles are properly categorized by suggested model. Shreya et al [16], DNN and ML algorithms such as RF, ANN, and SVM techniques are employed in order to evaluate the probability that Facebook account data is precise or not. The dataset utilized in this work is captured from GitHub which is a Facebook profile dataset for recognizing false and honest profiles.…”

Section: Related Workmentioning

confidence: 99%

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Unni,

S.,

Kalapurackal

et al. 2024

IJEECS

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<p class="Abstract">Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision.</p>

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Section: Related Workmentioning

confidence: 99%

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Unni,

S.,

Kalapurackal

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…Shreya et al [39] User age, gender, account age, link in the description, status, friends count, location, location IP, status. 9…”

mentioning

confidence: 99%

Fake account detection in social media using machine learning methods: literature review

Kerrysa,

Utami

2023

Bulletin EEI

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With the rapid development of emerging technologies in the industrial revolution 4.0 or 5.0, social media has become one of the social environments to carry out social activities, both socializing and advertising. However, since it is an open platform by nature, cybercrime occurrence in social media is inevitable. Currently, more than a million fake accounts are existing on Instagram, Twitter, and Facebook, intending to increase followers, spread hoaxes, and spam. On one hand, it is difficult to manually eliminate these accounts on social media platforms. On the other hand, research on automatic fake account detection has been carried out for more than a decade. This study provides literature reviews aiming to deliver information about several methods and machine learning algorithms with the performances measured in identifying fake accounts on three well-known social media platforms: Twitter, Instagram, and Facebook.

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Unveiling the Underworld: Detecting Fake Profiles Through Network Analysis and Behavioral Modeling on Social Media

Aditya,

Mohanty

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Identification of Fake accounts in social media using machine learning

Cited by 9 publications

References 19 publications

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

Fake account detection in social media using machine learning methods: literature review

Unveiling the Underworld: Detecting Fake Profiles Through Network Analysis and Behavioral Modeling on Social Media

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