# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
###### Abstract  
An online social media platform such as Instagram has become a popular communication channel that millions of people are using today. However, this media also becomes an avenue where fake accounts are used to inflate the number of followers on a targeted account. Fake accounts tend to alter the concepts of popularity and influence on the Instagram media platform and significantly impact the economy, politics, and society, which is considered cybercrime. This paper proposes a framework to classify fake and real accounts on Instagram based on a deep learning approach called the Long Short-Term Memory (LSTM) network. Experiments and comparisons with existing machine and deep learning frameworks demonstrate considerable improvement in the proposed framework. It achieved a detection accuracy of 97.42% and 94.21% on two publicly available Instagram datasets, with F-measure scores of 92.17% and 89.55%, respectively. Further experiments on the Twitter dataset reveal the effectiveness of the proposed framework by achieving an impressive accuracy rate of 99.42%. social media crime; Instagram fake accounts; deep learning; online social media; detection framework +
Footnote †: journal: _Future Internet_  
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Footnote †: journal: _Future Internet_  
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Footnote 0: _Future Internet_
video to friends. The app has more than 2 billion active users [3], and people have shared more than 40 billion photos since its launch in October 2010 [4]. Due to its versatility and ease of use, Instagram has become an ideal application for the proliferation of fake user accounts that behave in a very peculiar manner [5]. A fake account is mainly used to boost the Instagram metrics of accounts using fake followers. Fake accounts tend to improve the metrics of other Instagram users and thus create an unhealthy environment [6]. Instagram attacks are becoming a coordinated activity mainly associated with fake accounts [7]. Incorporating fake accounts in Instagram has been used to post fake bulk reviews to enhance product revenue or service [8]. Most of these users are involved in misleading and misinforming other genuine users. They are also involved in spreading malicious abuse, spamming, conning, and swaying the general public for their self-gain. Identifying these fake accounts becomes a significant task to protect honest users from these malicious and fake ones. Instagram fake account detection is quite difficult for a variety of reasons. Current methods, such as machine learning algorithms, have limitations when it comes to accurately detecting and obtaining features from fake accounts. When working with complex, high-dimensional numerical data, these methods' significant reliance on manual feature engineering and selection makes detection challenging. By automatically identifying pertinent features from raw data, methods based on deep learning, on the other hand, have the potential to overcome the drawbacks of conventional machine learning techniques. While deep learning models have demonstrated high accuracy in various applications, their effectiveness depends on the complexity and size of the dataset. For smaller or more structured datasets, traditional machine learning methods may still perform better, making it essential to evaluate both approaches in the context of fake account detection. While earlier research investigated the detection of fake accounts on platforms like Facebook and X, it must take into account Instagram's particular features, user habits, and data structures. Generalizing results from other platforms broadly to Instagram requires caution. Therefore, there is an intriguing research opportunity in this subject as there is a pressing need for more investigation to assess the usability and effectiveness of LSTM, especially for detecting fake accounts on Instagram. The objectives of this research are as follows:  
1. To build a strong and efficient fake account detection framework by leveraging the advantage of the power of LSTM networks.