Fake Profile Detection on Social Networking Websites: A Comprehensive Review

Roy, Pradeep Kumar; Chahar, Shivam

doi:10.1109/tai.2021.3064901

Cited by 46 publications

(20 citation statements)

References 111 publications

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“…In 2009, Tony La Russa, then manager of the St. Louis Cardinals, filed a suit against Twitter because of their negligence in removing an imitation account (Marrone, 2009). Indeed, detecting fake accounts on social media is an area full of research (see Roy & Chahar, 2021 for a literature review) and verification may help with some aspects of this. Specifically, in some instances, verification grants the public reassurance that a profile is who it says it is; it does not solve the issue of unverified fake accounts, though.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

“…Indeed, detecting fake accounts on social media is an area full of research (see Roy & Chahar, 2021 for a literature review) and verification may help with some aspects of this. Specifically, in some instances, verification grants the public reassurance that a profile is who it says it is; it does not solve the issue of unverified fake accounts, though.…”

Section: Social Media Verificationmentioning

confidence: 99%

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When influencers are not very influential: The negative effects of social media verification

Dumas

Stough

2022

J of Consumer Behaviour

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As social media usage has grown over the recent past, so too has a new form of celebrity: social media influencers (SMIs). It can be difficult for consumers to know whether the influencers they are following are real. To combat this, social media sites introduced user verification. Verification, denoted by a blue checkmark badge visible in search and on one's profile, is ostensibly a way of confirming one's identity, yet only accounts with large followings are awarded verification status by the platform. In this paper, we investigate the perception of verification in the context of SMIs, a topic relatively absent from the literature despite the billions of dollars spent on partnerships. Specifically, we investigate if consumers perceive verification as more directly associated with credibility or celebrity. Further, we consider whether the fit of the influencer with the advertisement yields discrepancies in consumers' trust of the advertisement and endorser in paid partnerships on social media. Through two studies, we find that consumers associate verification most closely with celebrity, rather than authenticity and that when the influencer is advertising a product that does not fit with their brand, consumers are significantly less likely to trust verified accounts rather than unverified accounts. This study has theoretical implications for marketing researchers as well as practical implications for marketing managers. Verified influencers cost more; therefore, this research provides unique insights for brands to capitalize their ad return if they are made aware of the implications associated with verification.

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Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Section: Social Media Verificationmentioning

confidence: 99%

When influencers are not very influential: The negative effects of social media verification

Dumas

Stough

2022

J of Consumer Behaviour

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show abstract

“…The study's authors had a 95% accuracy rate, which was a stunning result. In a study [24] that used feature extraction using phony profiles, the SVM-NN classification system had the highest performance of 98.3% in predicting sybil profiles.…”

Section: Discussionmentioning

confidence: 99%

Fake Profile Detection Using Machine Learning Techniques

Chakraborty¹,

Shazan²,

Nahid³

et al. 2022

JCC

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Our lives are significantly impacted by social media platforms such as Facebook, Twitter, Instagram, LinkedIn, and others. People are actively participating in it the world over. However, it also has to deal with the issue of bogus profiles. False accounts are frequently created by humans, bots, or computers. They are used to disseminate rumors and engage in illicit activities like identity theft and phishing. So, in this project, the author'll talk about a detection model that uses a variety of machine learning techniques to distinguish between fake and real Twitter profiles based on attributes like follower and friend counts, status updates, and more. The author used the dataset of Twitter profiles, separating real accounts into TFP and E13 and false accounts into INT, TWT, and FSF. Here, the author discusses LSTM, XG Boost, Random Forest, and Neural Networks. The key characteristics are chosen to assess a social media profile's authenticity. Hyperparameters and the architecture are also covered. Finally, results are produced after training the models. The output is therefore 0 for genuine profiles and 1 for false profiles. When a phony profile is discovered, it can be disabled or destroyed so that cyber security problems can be prevented. Python and the necessary libraries, such as Sklearn, Numpy, and Pandas, are used for implementation. At the end of this study, the author will come to the conclusion that XG Boost is the best machine learning technique for finding fake profiles.

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“…For example, using user profile informations such as user email address, number of friends and followers or user's behavioral informations such as languages and topics that included in postings which written by each accounts. References [23] [24] [25] can be used to determine whether the account is fake or not.…”

Section: Discussionmentioning

confidence: 99%

Detection Method for Randomly Generated User IDs: Lift the Curse of Dimensionality

Kang

Seo

et al. 2022

IEEE Access

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Internet services are essential to our daily life in these days, and user accounts are usually required for downloading or browsing for multimedia contents from service providers such as Yahoo, Google, YouTube and so on. Attackers who perform malicious actions against these services use fake user accounts to hide their identity, or use them to continue malicious actions even after being caught by the service's detection system. Using a random string generation algorithm for user identification (ID) string is one of the common method to create and obtain a large number of fake user accounts. To detect IDs and to defend against such attacks, some researchers have proposed the models that detect randomly generated IDs. Among these detection models, the n-gram-based using term frequency-inverse document frequency model is regarded as a state-of-the-art model to detect randomly generated IDs, but n-gram-based approaches have the problem of the curse of dimensionality because the sparsity of feature vector increases exponentially with the increase of size n. As a result, the improvement of the detection accuracy is limited since size n cannot be increased. This paper proposes two methods to detect randomly generated IDs more accurately. The first is to avoid the curse of dimensionality with the compression of feature dimension size. The second is a technique to reduce false positives by using pattern matching and Bhattacharyya distance. We tested our method with about 3 million normal user IDs collected from the real portal service, 1 million IDs generated by a random string generation algorithm, and 8,541 IDs found after being used for malicious behavior in real portal services. The experimental results showed that the proposed method can improve detection accuracy as well as inference performance.INDEX TERMS Authentication, computer crime, identity management systems, web sites.

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Fake Profile Detection on Social Networking Websites: A Comprehensive Review

Cited by 46 publications

References 111 publications

When influencers are not very influential: The negative effects of social media verification

When influencers are not very influential: The negative effects of social media verification

Fake Profile Detection Using Machine Learning Techniques

Detection Method for Randomly Generated User IDs: Lift the Curse of Dimensionality

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