Analyzing Real and Fake users in Facebook Network based on Emotions

Wani, Mudasir Ahmad; Agarwal, Nancy; Jabin, Suraiya; Hussain, S. M. Suhail

doi:10.1109/comsnets.2019.8711124

Cited by 23 publications

(12 citation statements)

References 22 publications

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“…A significant proportion of these fictitious accounts are established with the express intention of acquiring a higher number of followers. Everyone has the same objective, which is to seem as fashionable as possible on social media: Many individuals' resort to utilizing fake followers in order to give the impression that they have a large following that can be detected by techniques like Deep Fake Book (DFB) [16,17,18,19]. According to the findings of the study, the most damaging kind of cybercrime is the creation of bogus accounts.…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

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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“…K-means, Gaussian Mixture Model, and Spectral Clustering algorithms were used, and engagement of Impersonators was successfully studied. Wani et al [28], analyzed real and fake users on Facebook based on emotions. They trained their model based on 12 emotions using different machine learning approaches containing "SVM, Naive Bayes, JRip, and Random Forest".…”

Section: Role Of Machine Learning In Fake Profile Detectionmentioning

confidence: 99%

Exploring machine learning techniques for fake profile detection in online social networks

Bharti

Gill

Gulia

2023

IJECE

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<span lang="EN-US">The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results.</span>

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“…To detect fake profiles, there are various types of methods. Some techniques use for instance machine learning method [63], some use another forged profile or account to detect a fake profile [64], detection of emotions [65] and there could be also some collective efforts among the legitimate or real users for such detection.…”

Section: Fake Profile Detectionmentioning

confidence: 99%

Cyber Security Concerns in Social Networking Service

Hamid¹,

Alam²,

Sheherin³

et al. 2022

Int. j. commun. netw. inf. secur.

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Today’s world is unimaginable without online social networks. Nowadays, millions of people connect with their friends and families by sharing their personal information with the help of different forms of social media. Sometimes, individuals face different types of issues while maintaining the multimedia contents like, audios, videos, photos because it is difficult to maintain the security and privacy of these multimedia contents uploaded on a daily basis. In fact, sometimes personal or sensitive information could get viral if that leaks out even unintentionally. Any leaked out content can be shared and made a topic of popular talk all over the world within few seconds with the help of the social networking sites. In the setting of Internet of Things (IoT) that would connect millions of devices, such contents could be shared from anywhere anytime. Considering such a setting, in this work, we investigate the key security and privacy concerns faced by individuals who use different social networking sites differently for different reasons. We also discuss the current state-of-the-art defense mechanisms that can bring somewhat long-term solutions to tackling these threats.

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Analyzing Real and Fake users in Facebook Network based on Emotions

Cited by 23 publications

References 22 publications

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

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

Exploring machine learning techniques for fake profile detection in online social networks

Cyber Security Concerns in Social Networking Service

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