Instagram Fake and Automated Account Detection

Akyon, Fatih Çağatay; Kalfaoglu, M. Esat

doi:10.1109/asyu48272.2019.8946437

Cited by 58 publications

(49 citation statements)

References 9 publications

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“…Fake accounts are characterized by the absence of a profile picture and an unusual username. [11] Fake accounts are risky for social media platforms because they can change notions like popularity and influence on Instagram, as well as have an impact on the economics, politics, and society. For the Instagram platform, this research proposed a machine learning-based fake account detection method.…”

Section: Social Mediamentioning

confidence: 99%

“…Several previous studies used datasets that are made up of publicly available metadata. Where the dataset is derived from the results of personal data scraping [11] [14], as well as datasets created by others. [5] [12] [13].…”

Section: Datasetsmentioning

confidence: 99%

“…For example, here's a comparison of real accounts and fake accounts based on the number of followers and the number of followers [11]…”

Section: Datasetsmentioning

confidence: 99%

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Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning

2022

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The popularity of social media continues to grow, and its dominance of the entire world has become one of the aspects of modern life that cannot be ignored. The rapid growth of social media has resulted in the emergence of ecosystem problems. Hate speech, fraud, fake news, and a slew of other issues are becoming un-stoppable. With over 1.7 billion fake accounts on social media, the losses have al-ready been significant, and removing these accounts will take a long time. Due to the growing number of Instagram users, the need for identifying fake accounts on social media, specifically in Instagram, is increasing. Because this process takes a long time if done manually by humans, we can now use machine learning to identify fake accounts thanks to the rapid development of machine learning. We can detect fake accounts on Instagram using machine learning by implementing the combination of image detection and natural language processing.

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Section: Social Mediamentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

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Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning

2022

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“…During the last five years, a variety of approaches have been developed to manage user profiling problem not only with regards to data discovery but also for unhealthy activities detection for spam/ non-spam accounts [3], fake or bot accounts [4], fake followers [5], fake news [6], and fake engagement [7] using different Machine Learning Algorithms (MLAs) for classification purposes. Generally speaking, the classification task involves five main steps: data collection, feature extraction, feature selection, classification and prediction.…”

Section: Introductionmentioning

confidence: 99%

A Quantum Beta Distributed Multi-Objective Particle Swarm Optimization Algorithm for Twitter Fake Accounts Detection

Aboud¹,

Rokbani²,

Mirjalili³

et al. 2023

Preprint

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<p>Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter problems caused by a growing number of unethical online social activities. This study presents a new Quantum Beta-Distributed Multi-Objective Particle Swarm Optimization (QBD-MOPSO) system to detect fake accounts on Twitter. The proposed system aims to minimize two objective functions simultaneously: specifically features dimensionality and classification error rate. The QBD-MOPSO has two optimization profiles: the first uses a quantum behaved equation for improving the exploratory behaviour of PSO, while the second uses a beta function to enhance PSO’s exploitation. Six variants of the QBD-MOPSO approach are proposed to account for various data distribution types. The QBD-MOPSO system provides a feature selection technique based on the sigmoid function for position binary encoding. Each particle has a binary vector as a potential solution for feature subset selection, and a bit with the value of “1” indicating selection of a feature and “0” otherwise. Machine learning based classification models are trained and tested using a subset of selected features. An extensive experimental study is carried using two benchmark Twitter datasets with 1982 and 928 accounts. From 46 original features, QBD-MOPSO has selected 32 and 25 pertinent features and accurately classified 99.19% and 97.52% account on the datasets.</p>

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“…Another previous study [13] identified fake Instagram accounts as a problem of binary classification and proposed a cost-sensitive technique for reducing required features. The technique was based on a genetic algorithm to pick the best attributes for automatic classification of computation, correct the variance using the synthetic minority over-sampling technique-nominal continuous (SMOTE-NC) algorithm in a false computation dataset, and evaluate multiple methods of pattern recognition on pooled datasets.…”

Section: Introductionmentioning

confidence: 99%

Fake accounts detection on social media using stack ensemble system

Ali

Abdullah

2022

IJECE

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<p>In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and Naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.</p>

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Instagram Fake and Automated Account Detection

Cited by 58 publications

References 9 publications

Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning

Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning

A Quantum Beta Distributed Multi-Objective Particle Swarm Optimization Algorithm for Twitter Fake Accounts Detection

Fake accounts detection on social media using stack ensemble system

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