Detection of Fake Profile in Online Social Networks Using Machine Learning

Singh, Naman Deep; Sharma, Tushar; Thakral, Abha; Choudhury, Tanupriya

doi:10.1109/icacce.2018.8441713

Cited by 41 publications

(13 citation statements)

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“…The method is evaluated with the twitter data set [1,2]. A machine learning model is presented towards securing the social media accounts which calculate followers and friends of any account to measure the trust of any user [3].…”

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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“…Detecting Spam profiles, which is one of the fake profiles types [10], has also considered in the literature. Authors of [18] proposed a statistical analyzing model with 14 generic features from Facebook and Twitter data set regarding 4 basic kinds of social interactions including (profile interaction features, posts/ tweets, URLs and tags & mentions).…”

Section: Related Workmentioning

confidence: 99%

“…For example, Spam on social media repeatedly posts the same thing over and over or causes a sudden spike in messaging activity [9]. Fake profiles on the other hand, allow scammers to infiltrate networks of friends to wreak all sorts of havoc such as: stealing valuable information, financial fraud, or entering other user's social graph [10].…”

Section: Introductionmentioning

confidence: 99%

“…These attributes are provided in their personal profiles and used to identify the reality of user's identity such as: person's legal name, location, workplace, age, education, and others. The required data set for the training and testing purposes in our work has been collected from the authors' personal profiles considered as a source of real profiles, and from a created honeypot page, which is fake Facebook's pages used for the purposes of data harvesting [10] to attract and collect these profiles. As a collecting tool we wrote our own script to develop a special CRAWLER for gathering the required data set.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Study for Detecting Fake Facebook Profiles Using Supervised Mining Techniques

Albayati

Altamimi

2019

IJCAI

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Our social life and the way of people communicate are greatly affected by the social media technologies. The variety of stand-alone and built-in social media services such as Facebook, Twitter, LinkedIn, and alike facilitate users to create highly interactive platforms. However, these overwhelming technologies made us sank in an enormous amount of information. Recently, Facebook exposed data on 50 million Facebook unaware users for analytical purposes. Fake profiles are also used by Scammers to infiltrate networks of friends to wreak all sorts of havoc as stealing valuable information, financial fraud, or entering other user's social graph. In this paper, we turn our focus to Facebook fake profiles, and proposed a smart system (FBChecker) that enables users to check if any Facebook profile is fake. To achieve that, FBChecker utilizes the data mining approach to analyze and classify a set of behavioral and informational attributes provided in the personal profiles. Specifically, we empirically examine these attributes using four supervised data mining algorithms (e.g., k-NN, decision tree, SVM, and naïve Bayes) to determine how successfully we can recognize the fake profiles. To demonstrate the validity of our conceptual work, the selected classifiers have been implemented using RapidMiner data science platform with a dataset of 200 profiles collected from the authors' profile and a honeypot page. Two experiments are developed; in the first one, the k-NN schema is applied as an estimator model for imputation the missing data with substituted values, whereas in the second experiment a filtering operator is applied to exclude the profiles with missing values. Results showed high accuracy rate with the all classifiers, however, the SVM outperforms other classifiers with an accuracy rate of 98.0% followed by Naïve Bayes.Povzetek: Opisana je metoda iskanja lažnih profilov na Facebooku s pomočjo strojnega učenja.

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“…While fictitious, the above scenario represents a specific case of manipulation caused by online deception (Tsikerdekis & Zeadally, 2014). In a nutshell, while online deception deals with the general case of lying and misleading other users online (Ebner et al, 2018;Singh, Sharma, Thakral, & Choudhury, 2018;Van der Walt, Eloff, & Grobler, 2018) , manipulation, this paper deals with the specific case where a manipulator uses deception methods to manipulate a victim and potentially gets access to his/her personal data.…”

Section: Introductionmentioning

confidence: 99%

Privacy, Trust, and Manipulation in Online Relationships

Aïmeur

Sahnoune

2019

Journal of Technology in Human Services

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This work studies factors that influence personal information disclosure coming from vulnerable users who may adopt risky online behaviour on social media. These users trust their online relationships and reveal confidential or sensitive information, causing damage to themselves and to their relatives and friends. We carried a survey-based study on 1532 participants to see if they would reveal sensitive information when they have close online relationships whom they trust. The results led to several discoveries, especially concerning the factors affecting the disclosure in online relationships, and those affecting the attitude towards online manipulation. Moreover, this study attested a strong positive relation between disclosure issues and the user's attitudes towards manipulation. The latter which is affected by their online behavior and social interaction ties. In a nutshell, users attitudes towards manipulation may lead to self-disclosure on the one hand, and these attitudes could be identified through their social behavior on the other hand.

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Detection of Fake Profile in Online Social Networks Using Machine Learning

Cited by 41 publications

References 1 publication

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

An Empirical Study for Detecting Fake Facebook Profiles Using Supervised Mining Techniques

Privacy, Trust, and Manipulation in Online Relationships

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