Finite Automata for Fake Profile Identification in Online Social Networks

Krishnan, Padmaveni; Aravindhar, D. John; Reddy, Palagati Bhanu Prakash

doi:10.1109/iciccs48265.2020.9121086

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Similarly, set of methods are validated for their performance. The performances of Decision tree and naïve Bayes algorithms are validated for their performance in identifying fake profiles [11][12].…”

Section: Related Workmentioning

confidence: 99%

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

2022

IJIES

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The problem of social network security has been well studied. Numbers of approaches are identified towards securing the profile of social network users and identifying the fake profiles. Still the methods suffer to produce efficient results on detecting fake profiles. To improve the performance, an efficient profile analysis and fake detection model is presented in this article. The proposed Real Time Profile Analysis and Fake Detection Model (RTPAFDM) works on three phases: first the Post Level Trust Analysis (PLTA) is performed, which monitors the post updated on every day and share the posts internally with other users to claim the trustworthy of the post which computes the value of User Post Trust Weight (UPTW); second the method performs Profile Level Trust Analysis (PrLTA) which extracts the profile information like user name, personal details, contact details and friends list. According to the information extracted, the method searches on the entire social network to measure the Profile Match Similarity (PSM). Based on the PSM value, the method would identify the originality of the profile; finally, the method performs Profile Historic Trust Analysis (PHTA) which works on the earlier posts made and their truth values. It computes the value of Historical Trusted Post Weight (HTPW). Using the result of all these trust analysis values, the profile security and fake detection is performed. The method is evaluated with Twitter data set and achieved fake detection accuracy up to 97% and supports the improvement profile security efficiently.

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Section: Related Workmentioning

confidence: 99%

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

2022

IJIES

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“…These rumors add gasoline to the fire of disputes that already exist in society. Recently, it has come to light that there are issues with regard to privacy, exploitation, cyberbullying [1,2,3], and incorrect information.…”

Section: Introductionmentioning

confidence: 99%

TSMFPMSM: Design of a Temporal Sentiment analysis Model for identification of Fake Profiles from Multimodal Social Media interfaces

BHRUGUMALLA¹,

Mohanty²

2023

Preprint

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The growing number of fake profiles on social media has made it necessary to develop intelligent models for identifying and removing them. This study proposes a novel approach for identifying fake profiles using temporal sentiment analysis. The model collects large datasets from various social media platforms, which are then processed using an ensemble sentiment analysis engine. This produces temporal sentiment patterns, which are used to train a 1D Convolutional Neural Network (1D CNN) for the efficient identification of fake profiles. The model is based on the theory that the data communicated by fake users always converges, and was observed to improve fake-profile detection accuracy by 5.9% with higher precision and recall compared to standard methods. This proposed model can be applied to multimodal social media interfaces.

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F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets

Aditya

Gnanajeyaraman

Hole

et al. 2023

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

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Finite Automata for Fake Profile Identification in Online Social Networks

Cited by 7 publications

References 11 publications

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

TSMFPMSM: Design of a Temporal Sentiment analysis Model for identification of Fake Profiles from Multimodal Social Media interfaces

F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets

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