Deep Neural Network for Detecting Fake Profiles in Social Networks

1  Introduction

In recent decades, social media has significantly influenced interpersonal relationships, transforming the internet into a virtual platform for online development, trade, and exchanging knowledge by individuals and their organizations [1]. The various social communication systems have value chains aimed at certain user groups. For example, users may reunite with old acquaintances by browsing their Facebook profiles, and social media such as Twitter provides relevant updates and news of the following profiles. On the other hand, there are social network sites with different purposes, like LinkedIn, which is intended to serve as a support system for professional groups. Therefore, users are encouraged to fill their profiles with a significant number of personal data and to explore other users who share the same interests. According to usage rates, Facebook is the most popular social media platform, with 800 million monthly visits [2].

Estimates provided by Cloudmark suggest that between 20 and 40 percent of accounts on both Facebook and Twitter might be fake profiles [3]. It is becoming more challenging to tell a real user from a fake due to the high levels of user engagement that occur every day and the millions of transactions that take place each day. The anticipated outcomes from the efforts to elicit user participation in identifying fake accounts have not been attained [3]. In addition, when it comes to networks that have strict user privacy policies, there is a tiny amount of available public data. Thus, differentiating between fake and valid profile pages has become quite tricky systematically before trusting a possible association. In this piece of work, a way for distinguishing authentic accounts from false accounts in a logical manner is proposed. The approach is based on the limited publicly accessible account data on websites with strict privacy regulations, such as LinkedIn.

Social media’s growth can potentially raise people’s social evaluation and popularity. In particular, social network users may gain popularity by amassing many likes, follows, and remarks. On the other hand, establishing fake profiles is much too simple, and such accounts can be purchased online at little cost. For instance, purchasing followers and comments on social media platforms such as Facebook and Twitter may be done more easily on the internet [4]. Analysis of activity changes is one of the most common techniques open social networking methods use to spot strange accounts. The activities that people engage in throughout time tend to shift and evolve. Therefore, the server can identify a potential scam account by monitoring for sudden changes in access patterns to the content and activity it requires. In case of unsuccessful identification, the deviant might fill the systems with fake information [5]. Fig. 1 demonstrates a common schema of fake account detection on social networks.

Figure 1: Dataset and feature collection procedure

Another type of fake account is a Cyborg, which a human uses to communicate with real users. It lowers the legitimacy of the user and employs the hijacked account to disseminate false information, create disinformation, and polarize public opinion [6]. On the other hand, several communities suggest doing a variety of dataset analyses in conjunction with machine learning techniques to solve the issue. For example, one learning approach allows the model to calculate user categorization by “training” on the attributes data throughout some time. Several other articles examine the identification of false nodes using statistical approaches, distributed spatial using a density-based strategy, support vector machines (SVM), and hybrid models to identify social network fake profiles [7–9].

Social network accounts include various personally identifiable information, such as the username, the user’s complete name, phone number, etc. The critical material may be compromised or manipulated by a skilled attacker if personal protection is used, which is a disadvantage of the method. Cyber fraudsters can use social and industrial design tactics and create dummy accounts to steal information and modify data. An assault using fake profiles might jeopardize a company’s or institution’s reputation and confuse by providing odd and pointless updates [10]. According to the reviewed publications, the issues surrounding the protection and credibility of social networks have become more significant. Moreover, it is necessary to have a trustworthy security model, particularly in light of threats’ rising complexity and diversity.

While there are several methods for detecting fake profiles on social networks, they are not foolproof and come with their disadvantages. Here are some of the disadvantages of these methods:

Inaccuracy: One of the main challenges of detecting fake profiles is the methods’ accuracy. Algorithms and models may not always accurately identify fake profiles and may also flag legitimate profiles as fake. This can lead to false positives and false negatives, which can be frustrating for users.

Limited data access: Detecting fake profiles often requires access to private data, such as Internet Protocol (IP) addresses, device fingerprints, and browsing histories. This raises privacy concerns, as users may not be comfortable sharing this information with social networks or third-party apps.

The difficulty of distinguishing between real and fake content: It can be challenging to distinguish between real and fake content on social networks, especially regarding user-generated content. Fake profiles may generate content that appears legitimately, making it difficult to detect them.

Limited resources: Detecting fake profiles can be resource-intensive, requiring advanced algorithms and machine-learning models to process vast amounts of data. Small social networks or those with limited resources may be unable to afford or implement these tools, leaving them vulnerable to fake profiles.

Ethical considerations: Detecting fake profiles raises ethical considerations, particularly regarding user privacy and social networks’ responsibility to protect users. Social networks may need to balance the need for fraud prevention with the rights and privacy of their users.

Overall, while there are various methods for detecting fake profiles on social networks, they have their disadvantages. It is a continual challenge to stay ahead of the evolving tactics of fake profile creators.

To the best of our knowledge and based on our poll results, this is the only way to deal with fake social media profiles that involve training certain features using a deep learning approach. A neural network is a type of machine-learning model that is inspired by the structure and function of the human brain. It consists of layers of interconnected nodes, or neurons, that process information and makes predictions. A deep neural network (DNN) is a neural network with multiple hidden layers. The term “deep” refers to the number of layers in the network. In contrast, a general neural network may have just one or two hidden layers.

Adding multiple hidden layers in a deep neural network allows it to learn more complex features and relationships in the data, leading to better performance in tasks such as image and speech recognition, natural language processing, and many others. However, deep neural networks can be more challenging to train and suffer from overfitting or vanishing gradients.

To overcome these challenges, researchers have developed various techniques such as regularization, normalization, dropout, and gradient clipping to improve the training of deep neural networks. Overall, deep neural networks have revolutionized the field of machine learning and are widely used in various applications, from computer vision to natural language processing to autonomous vehicles.

The following is a summary of the most important contributions that our study has made, notably in addressing the fake profile classification task:

•   A deep learning approach provided a unique way of identifying fraudulent accounts inside social networks.

•   Sixteen profile-based features to train models for fake account detection problems were determined.

•   We put the proposed deep model through its paces by conducting an exhaustive examination to acquire cutting-edge findings, particularly regarding detecting fraudulent profiles.

The remainder of the proposed paper is as follows: Next section reviews the literature by exploring the related works. Section 3 demonstrates the materials and methods applied in this research by demonstrating the proposed framework, method, and dataset. Section 4 illustrates the obtained results and compares them with the state-of-the-art results. Section 5 discusses the obtained results by referencing the advantages, limitations of current work and future perspectives. In the end, the paper was concluded by demonstrating the obtained results.

2  Literature Review

Nowadays, social networking is expanding at an astonishingly rapid rate, which is significant for marketing initiatives and for celebrities attempting to advertise themselves by expanding their network of followers and admirers. Nevertheless, dummy accounts that seem to have been established on behalf of companies or individuals have the potential to tarnish their reputations and lead to a decline in the number of likes and follows they get. Fake updates and misunderstandings also plague them with other individuals. Fake accounts of any sort can lead to negative consequences that cancel out the benefits of social networks for companies in terms of promotion and marketing. They also prepare the ground for cyberbullying to occur. Users have varying worries about protecting their personal information in an online setting. Freelon et al. [11] outlined the dangers that lurk in social networking sites when members are often oblivious to them.

Loss of privacy and identity, as well as viruses, fake accounts, and harassment, are some of the issues that might arise due to cyber fraudsters’ activity on social media. In terms of popularity, social networks now have billions of people signed up for their services. Facebook has established itself as the most well-known social network, with over a billion active users. There are fundamentally four different types of threats on social media: traditional threats, contemporary threats, combination threats, and threats explicitly aimed at children. Several potential responses to these dangers may be grouped into one of three categories: operator responses, business responses, or scholarly responses. The processes included within each of these classes have the potential to assist in overcoming the challenges posed by social networks [12]. Social engineering is the most common source of social network privacy and security risks. The primary methods of social engineering include socio-technical, technical, physical, and social. These methods are often carried out with the assistance of either software or actual users. Email, instant messaging, the telephone, messengers, Voice over Internet Protocol (VoIP), open social networks, the cloud, websites, and even physical routes may all be used as vectors for social engineering. In addition, there are modern forms of assault, such as social phishing, context-aware spam, false profiles, spear phishing, and phony identities stored in the cloud. Investigating the factors contributing to social network vulnerabilities revealed that fake accounts are the most significant factor [13]. Detecting fake profiles is essential before the profiles in question are enrolled as social networking site members. Methods of detection similar to these will be covered later in this article. To acquire meaningful insights about the vast amount of accessible data, many companies have begun to explore the unstructured data available on social media [14].

Previous methods operate on the assumption that machine learning methods are complicated to implement because scammers produce patterns that computers cannot learn. However, recent research has shown that adversarial learning may be effectively implemented using many traditional machine learning methods, including ensembles of classifiers, Random Forests, SVM, adaptive boosting (AdaBoost), and Naive Bayes [15]. Furthermore, the grouping and categorization of profiles according to their qualities are accomplished via several machine learning methods. This review on effective machine learning introduces several machine learning techniques. Furthermore, it examines the capacity of such algorithms to handle large amounts of data concerning the accuracy of their predictions. The computational needs of a model, the amount of memory required the least, and the ratio of the cost of computing to the accuracy of predictions are used to determine a model’s performance. In addition, clustering methods were applied to analyze social network graphs to identify fake profiles [16].

It is common practice to utilize fake social media accounts to gain users’ confidence and then distribute malware or a link. In addition, these scammers are involved in various other forms of criminal operations. So far, a considerable amount of research has been centered on identifying false accounts on social media platforms to find solutions to these issues. This taxonomy has generally been used since it was described in [17]. The methods for detecting fake accounts on social networking sites can be broken down into two categories: those that focus on analyzing individual accounts and those that concentrate on capturing coordinated activities that span many profiles. For instance, one research report identified and classified ghost accounts in popular social networking games [18]. The research investigates Facebook games, known as the online game “Fighters club,” which offers rewards and a competitive edge to users who request their friends to participate. The researchers claim that the game stimulates its layers to develop phony profiles by initiating such an offer. As a result, users would improve their incentive value by putting such fake profiles into the game. The authors begin by gathering thirteen characteristics for each user and then go on to the classification process using support vector machines. According to the findings of this research, the approaches above do not provide any clear discriminators that might differentiate genuine users from phony ones. This most recent study employed graph-based characteristics, such as local clustering coefficient, betweenness centrality, and bi-directional connections ratio, as well as neighbor-based and timing-based features, to design several classifiers [19]. The obtained accuracy resulted in 86% true and 5% false positives. However, subsequent efforts have made use of a number of the more common machine learning techniques. Several different machine learning techniques are used to classify profiles according to their attributes of those profiles. The review of research on effective machine learning presents some algorithms and examines their processing abilities concerning prediction accuracy. Several machine learning techniques are applied to detect false accounts that may exist inside social media platforms. Numerous approaches to machine learning have been used in various studies.

Using supervised learning methods, the technique that Singh and Sharma suggested to identify spammer patterns was discussed [20]. Honey profiles were dispersed around Twitter and Facebook with the intention of coaxing spammers into exposing their identities by forming links with the honey profiles. All these accounts that were found to have established connections with the honey profiles were carefully evaluated and classified as either spammers or genuine users. On Facebook, a classifier was built using 1,000 tagged profiles; out of them, 173 of 1000 profiles were recognized as belonging to spam bots and were removed from the classifier. The authors used an unlabeled dataset, including 790,951 profiles, to assess the built classifier. Only 130 of these profiles were identified as belonging to spammers.

Cresci employed machine learning and honeypots to identify spammers on MySpace and Twitter social networks [21]. They conducted their research in the same year. Rampersad et al. identified real-time disasters like earthquakes and typhoons by using data analysis of tweets sent by Twitter users [22]. Behavior and content analysis were used by Umer et al. to identify spammers on Twitter [23]. They amassed a dataset including 54 million individual profiles. To develop their predictor, they employed a training set that had 8,207 manually labeled individuals, of whom 355 turned out to be spammers and 7,852 were not considered as spammers. Due to a disproportionately large number of spammers compared to genuine users, 710 legitimate users were chosen randomly and included in the training set along with the spammers. In addition, a regular SVM classifier was applied for the phase that dealt with classification.

Consequently, the research demonstrated a true-positive percentage of 70.1% when detecting spammers, while their false-positive rate was 3.6%. Phantom profiles, also known as profiles constructed to get a strategic edge in social games, were the primary focus of Mourão et al. [24]. They successfully built a phantom profiles identification classifier by employing data from profiles and gaming activity on Facebook. The obtained results have shown 86.4% true-positive and 13.4% false-positive rates.

While research in Deep Neural Networks for detecting fake profiles in social networks has shown promising results [25–29], some limitations and challenges still need to be addressed. Some of these limitations include the following:

Limited Training Data: One of the main challenges in training a Deep Neural Network for detecting fake profiles is limited labeled training data availability. The lack of diverse and large-scale datasets can result in overfitting and poor model generalization to unseen data.

Adversarial Attacks: Adversarial attacks can trick the Deep Neural Network into misclassifying fake profiles as genuine ones. Attackers can use various techniques, such as data poisoning or model inversion, to manipulate the model’s outputs and bypass the detection mechanism.

Ethical Concerns: There are ethical concerns surrounding using Deep Neural Networks to detect fake profiles. For example, the algorithm’s accuracy may vary based on cultural differences and demographics, leading to prediction bias. Furthermore, using such algorithms can violate user privacy and raise concerns about using personal data.

Limited Interpretability: Deep Neural Networks are often considered black boxes, making it difficult to interpret their decision-making processes. This lack of interpretability can make understanding how the model identifies fake profiles challenging and limit its effectiveness.

Computational Complexity: Deep Neural Networks can be computationally expensive, requiring significant computational resources to train and operate. This complexity can limit the model’s scalability and practicality for real-time applications.

While Deep Neural Networks have shown promising results in detecting fake profiles in social networks, researchers must address these limitations to develop more accurate, ethical, and scalable detection mechanisms.

3  Materials and Methods

As is the case with most social networking sites, the public Facebook social network developer application programming interface (API) only presents users’ public information. Therefore, it is impossible to acquire access to the different activities of specific customers, and this occurs most of the time when a client has already changed the mode of their account to private. This annoyance is considered an obstruction to the system of records series since it causes a lot of trouble. Therefore, to address the issues and crawl the customers’ data, a specialized crawler for data extraction and a function series device, both described in the following stages, were developed. Fig. 2 demonstrates the dataset creation and feature collection process.

Figure 2: Dataset and feature collection process

3.1 Data Collection

The 6868 regular customers, including celebrities, corporations, and daily legitimate consumers, and the 3132 anomaly customers who were personally checked and chosen have been collected inside the dataset. Additionally, the dataset contains 3132 customers that were deemed to be anomalous. We have developed more sophisticated types of record crawlers, one for reaching typical clients and another for finding unusual ones. The daily user crawler used the find feature on Facebook to locate ordinary users to be included in the list of everyday users in the dataset. The Explore section of Facebook displays recently published photos and videos that capture the attention of other users, indicating that the content shared on Facebook is, for the most part, genuine and authentic. In addition, to find and harvest fake customers on Facebook, an advanced crawler was initially utilized to acquire fake customer identifications (IDs) through the follower listings of customers who considered a wide variety of fake customers of their follower listings. Secondly, another system that allows the manual test of all false archived customers included in the dataset was developed. Thus, it will allow us to be sure about the customers’ identities and improve the dataset’s quality.

The Facebook application programming interface (API) crawled a few public records for each user. An overview of the dataset and a description of the crawled capabilities can be found indexed in Tables 1 and 2, respectively. The compiled capabilities are cataloged in Table 2.

3.2 Feature Preparation

After a significant amount of manual tagging, 1002 real accounts, and 201 fake accounts were acquired for the dataset. These accounts included debts from a variety of countries and professions throughout the world. The criteria that are taken into consideration during this data collection process include follower and following counts, media counts, media posting dates or frequencies, comments on posts made by social network users, the number of accompanying and following accounts, the lifespan of the profile picture, and the username of the profile.

A specific example: noticing different fake accounts from the dataset is possible. As can be seen, it has a large number of followers, 3949, and a small number of coffee followers, 15. Additionally, it does not have a profile picture or any published material.

The selected essential functions may be indexed in the dataset in the following manner:

•   The whole range of activities by the account.

•   Remember that the account is vital to the followers.

•   After taking into consideration the history.

•   The number of digits that may be found in the account username.

Regardless of whether or not the account is private, none of the functions are connected to the user’s media; hence, the set of restrictions does not violate the user’s account privacy. In this day and age of fake debts, some debts are manufactured by adding various numbers to the same name, which is why account usernames must have a diverse range of digits. It’s possible to make out the several ways the digits are distributed.

As can be seen, more than half of the fake debts have more than one number, while most real accounts have zero or one digit, accounting for around 89% of the total.

3.3 The Proposed Method

In this part of the research, a deep convolutional neural network (CNN) model was proposed for recognizing fake accounts on social networks. Fig. 3 demonstrates the proposed deep CNN model. The primary function of the suggested CNN consists of a backward pass and a forward pass that will operate concurrently throughout each iteration. The forward pass is computed by the proposed CNN using the following equation.

yjn=∑ikijn⋅xin(1)

Figure 3: Architecture of the proposed deep CNN

In formula (1) shown above, the first network is responsible for computing the feature maps, which are then provided as the input layer to the proposed CNN.

Depending on the signification of xin is the i-th input feature map of the sample n and kijn is the ij input filter of the sample n. Then, j−th becomes the output feature map of sample n networks. Using the formulae, the proposed convolutional neural network does the calculation for a backward pass as in the following equation:

∂l∂xin=∑j(∂l∂yjn)⋅(kijn)(2)

∂l∂kijn=(∂l∂kjn)⋅xin(3)

The CNN goes over each neuron to calculate the loss function, working its way up through the layers. The forward pass of the proposed neural network refers to the forward propagation of errors. In contrast, the forward pass of the proposed network refers to the forward propagation of errors using gradient descent to estimate the gradient of the loss function concerning the network parameters. The backward pass of CNN describes how mistakes are propagated in the other direction (weight and bias). To update the learning parameters, gradient descent is necessary to attain the minor loss function possible [30]. Besides, the Cross-Entropy (CE) loss function was applied during the study, a primary loss function for classification issues [31]. The calculation of the following accomplishes this:

H(y)=−∑iyi′log⁡(yi)(4)

Regarding the preceding operation, yi′ is the target label, and yi is the output of the classification model. Finally, the CE function is applied to get an output with a probability distribution, which is the most popular loss function for use with SoftMax.

3.4 Classification

A deep learning model in binary classification has been applied to distinguish between dummy and valid profiles. Converting the array into binary tensors is a crucial step before supplying it with input, the matrix, into the network to facilitate adaptation. The suggested CNN begins by collecting input data in the first layer and crossing it onto hidden layers as its first operational phase. It employs social network characteristics as its input matrix at the beginning of the process.

The network performs a convolutional operation and a pooling operation, as well as a calculation using fully connected layers to create the output. The purpose of the convolutional layer is to extract numeric characteristics from the input data by iteratively sliding a smaller matrix filter over it. On the other hand, the convolutional method produces vast arrays. To simplify the array, we have optimized the pooling procedure using an innovative pooling function. Pooling allows us to restrict the number of generated feature maps while retaining the component that delivers the most information [32]. This is accomplished by the downsampling approach, which helps us prevent overfitting in a CNN [33].

We trained the model in the hidden layer by supplying multiple layers to test how well the proposed deep convolutional neural network performs with the activation layer. In this study, to minimize the number of parameters and boost the network’s capacity to generalize its results, the automated weight-sharing characteristics of the network were modified. By reducing the number of degrees of freedom that make up the complexity of the network, overfitting may be avoided by sharing the weight of neurons [34].

To downsample the input feature map, reducing its size while retaining the essential information, MaxPooling was applied. This operation effectively reduces the spatial resolution of the feature map but preserves the essential features by selecting the maximum value. Max pooling has several benefits, including reducing the computational complexity of the network, making it more robust to small translations in the input, and helping to prevent overfitting by enforcing a form of regularization.

4  Results

The experiment allowed us to achieve a balance between the amount of time it takes and how accurately it performs. We put gradient descent through its paces and experiment with various optimization algorithms’ hyperparameters. One of the most vital factors for improving the network’s training quality is choosing a hyperparameter value. As a result, we achieved the best possible performance of the network by optimizing the hyper-parameters. During training and testing, the epoch to equal 15 and the batch size to equal 50 were tuned. To train a model effectively and have enough data to evaluate its performance of the proposed model, the dataset was divided between training and testing set as 80% to 20%. The performance of the DeepProfile CNN concerning computing validation accuracy and loss is detailed in Table 3.

These findings were gleaned by applying the machine learning models to the selected dataset. Graphs, comparison charts, and ROC curves are the presentation formats for the findings produced during the study. In addition, the accuracy or loss patterns under each model were considered. Further discussion is on the accuracy or loss computed during the training and validation. X Since Google Colab allows for the use of free GPUs, the platform for the training of our models was chosen. The NVIDIA Tesla K80 GPU from Google Colab has 12 GB of memory and can operate nonstop for up to 12 h. As a main language to write down all of the models, Python3 was used [35].

The following findings were obtained after all the models were trained and validated. The model accuracy, model loss vs. the epoch graphs, and the receiver operating characteristic (ROC) curve for the random forest, eXtreme Gradient Boosting (XG boost), and other approaches are presented for the proposed neural network. Model accuracy comparisons are also performed.

Figs. 4 and 5, which represent the trained neural network, provide the accuracy and loss graphs for the model, respectively. The accuracy and loss graphs above let the algorithm run for 15 epochs. Beginning at a value of 0.97 and continuing down the route, the accuracy gradually improves until it achieves its highest possible value, which is 0.98. Similarly, the loss graph for testing data starts at 1, whereas the loss graph for validation data starts at 4. Both graphs ultimately converge on a minimum point that is smaller than 0.5. The binary cross-entropy function is used to compute the loss amount. At first, each feature receives a completely random weight, and in the end, the machine gives a weight that is unique to each feature.

Figure 4: Model accuracy

Figure 5: Model loss

Fig. 5 demonstrates the training and validation loss of the proposed deep convolutional neural networks for 12 training and testing epochs. As the figure illustrates, train loss sharply decreased from the first training epoch, and validation loss remains with less error rate. The results show that the proposed model has achieved high accuracy with less error rate.

Comparison to Other Methods: In the chart that compares the various models’ accuracy, we can see the performance of different machine learning algorithms. The histogram for comparing the levels of accuracy, as well as the Area under the ROC Curve (AUC-ROC) curves, have been provided in Fig. 6. As it can be seen from the figure, the proposed deep neural network demonstrates higher performance in terms of all the evaluation parameters including accuracy, precision, recall, F-score, and AUC-ROC.

Figure 6: Comparison of the proposed network and machine learning methods

Fig. 7 demonstrates the AUC-ROC curve of the proposed deep convolutional neural network. The horizontal axis represents false positive rates, while the vertical axis represents true positive rates. As the figure illustrates, the proposed network shows a high rate of AUC-ROC that is very useful in practical use.

Figure 7: AUC-ROC curve in detecting fake profiles

Table 3 compares the proposed deep neural network with the other machine learning and deep learning models on Kaggle’s Social Network Fake Account Dataset. The results show that the proposed deep neural networks cope better than traditional machine learning and deep learning models.

To summarize the suggested learning method in detecting fake profiles, the provided approach may be a future choice for analyzing accounts in a huge dataset. The results show that the proposed method can serve as a protection strategy in social networks. Furthermore, the expenditures of research and development for harmful account analysis may be reduced under this approach, which is another advantage of using this method. We have concluded that the proposed model can achieve an efficient result and one that the neural network can do with more precision. In a real-world scenario, the convolutional neural network is a potentially viable approach for addressing difficulties related to harmful behavior on social networks. Table 4 compares state-of-the-art research results with the proposed convolutional neural network regarding different evaluation parameters.

5  Conclusion

To sum it up, our research proposes an approach for categorizing profiles on social networking sites as either real or actual accounts. The convolutional neural network-based deep model served as the foundation for the algorithm designed to extract patterns of descriptive writing from the contents of posts. Experiments have been carried out using classifiers using our proposed dataset collected from the internet. It was shown that the suggested technique generates relatively high detection performance, coming in at 99.8% overall. When the findings are considered, the proposed text-based model shows promising accuracy in categorizing the user type based on the writing style they use. In our opinion, the proposed methodology has the potential to be of great assistance in the fight against fraud on social networking sites. Further investigation into diverse deep-learning techniques may provide additional intriguing findings.

Funding Statement: The authors received no specific funding for this study.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

References

1. M. Mansoor, Z. Rehman, M. Shaheen, A. Khan and M. Habib, “Deep learning based semantic similarity detection using text data,” Information Technology and Control, vol. 49, no. 4, pp. 495–510, 2020. [Google Scholar]

2. M. Shaheen and M. Shahbaz, “An algorithm of association rule mining for microbial energy prospection,” Scientific Reports, vol. 7, no. 1, pp. 1–12, 2017. [Google Scholar]

3. R. Kaliyar, A. Goswami and P. Narang, “FakeBERT: Fake news detection in social media with a BERT-based deep learning approach,” Multimedia Tools and Applications, vol. 80, no. 8, pp. 11765–11788, 2021. [Google Scholar] [PubMed]

4. F. Pierri and S. Ceri, “False news on social media: A data-driven survey,” ACM Sigmod Record, vol. 48, no. 2, pp. 18–27, 2019. [Google Scholar]

5. D. Sultan, A. Toktarova, A. Zhumadillayeva, S. Aldeshov, S. Mussiraliyeva et al., “Cyberbullying-related hate speech detection using shallow-to-deep learning,” Computers, Materials & Continua, vol. 74, no. 1, pp. 2115–2131, 2023. [Google Scholar]

6. B. Ghanem, P. Rosso and F. Rangel, “An emotional analysis of false information in social media and news articles,” ACM Transactions on Internet Technology, vol. 20, no. 2, pp. 1–18, 2020. [Google Scholar]

7. M. Ahvanooey, M. Zhu, W. Mazurczyk, K. Choo, M. Conti et al., “Misinformation detection on social media: Challenges and the road ahead,” IT Professional, vol. 24, no. 1, pp. 34–40, 2022. [Google Scholar]

8. G. Pennycook and D. Rand, “The psychology of fake news,” Trends in Cognitive Sciences, vol. 25, no. 5, pp. 388–402, 2021. [Google Scholar] [PubMed]

9. D. Sultan, B. Omarov, Z. Kozhamkulova, G. Kazbekova, L. Alimzhanova et al., “A review of machine learning techniques in cyberbullying detection,” Computers, Materials & Continua, vol. 74, no. 3, pp. 5625–5640, 2023. [Google Scholar]

10. D. Barud and A. Salau, “Detection of fake news and hate speech for Ethiopian languages: A systematic review of the approaches,” Journal of Big Data, vol. 9, no. 1, pp. 1–17, 2022. [Google Scholar]

11. D. Freelon, M. Bossetta, C. Wells, J. Lukito, Y. Xia et al., “Black trolls matter: Racial and ideological asymmetries in social media disinformation,” Social Science Computer Review, vol. 40, no. 3, pp. 560–578, 2022. [Google Scholar]

12. K. Hartley and M. Vu, “Fighting fake news in the COVID-19 era: Policy insights from an equilibrium model,” Policy Sciences, vol. 53, no. 4, pp. 735–758, 2020. [Google Scholar] [PubMed]

13. B. Omarov, N. Saparkhojayev, S. Shekerbekova, O. Akhmetova, M. Sakypbekova et al., “Artificial intelligence in medicine: Real time electronic stethoscope for heart diseases detection,” Computers, Materials & Continua, vol. 70, no. 2, pp. 2815–2833, 2022. [Google Scholar]

14. N. Kanagavalli and S. Baghavathi Priya, “Social networks fake account and fake news identification with reliable deep learning,” Intelligent Automation & Soft Computing, vol. 33, no. 1, pp. 191–205, 2022. [Google Scholar]

15. D. Koggalahewa, Y. Xu and E. Foo, “An unsupervised method for social network spammer detection based on user information interests,” Journal of Big Data, vol. 9, no. 1, pp. 1–35, 2022. [Google Scholar]

16. B. Omarov, S. Narynov, Z. Zhumanov, A. Gumar and M. Khassanova, “A Skeleton-based approach for campus violence detection,” Computers, Materials & Continua, vol. 72, no. 1, pp. 315–331, 2022. [Google Scholar]

17. A. Bruns, “After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research,” Information, Communication & Society, vol. 22, no. 11, pp. 1544–1566, 2019. [Google Scholar]

18. S. Tsao, H. Chen, T. Tisseverasinghe, Y. Yang, L. Li et al., “What social media told us in the time of COVID-19: A scoping review,” The Lancet Digital Health, vol. 3, no. 3, pp. e175–e194, 2021. [Google Scholar] [PubMed]

19. N. Brashier and D. Schacter, “Aging in an era of fake news,” Current Directions in Psychological Science, vol. 29, no. 3, pp. 316–323, 2020. [Google Scholar] [PubMed]

20. B. Singh and D. Sharma, “Predicting image credibility in fake news over social media using multi-modal approach,” Neural Computing and Applications, vol. 34, no. 24, pp. 21503–21517, 2022. [Google Scholar] [PubMed]

21. S. Cresci, “A decade of social bot detection,” Communications of the ACM, vol. 63, no. 10, pp. 72–83, 2020. [Google Scholar]

22. G. Rampersad and T. Althiyabi, “Fake news: Acceptance by demographics and culture on social media,” Journal of Information Technology & Politics, vol. 17, no. 1, pp. 1–11, 2020. [Google Scholar]

23. M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G. Choi et al., “Fake news stance detection using deep learning architecture (CNN-LSTM),” IEEE Access, vol. 8, no. 1, pp. 156695–156706, 2020. [Google Scholar]

24. R. Mourão and C. Robertson, “Fake news as discursive integration: An analysis of sites that publish false, misleading, hyperpartisan and sensational information,” Journalism Studies, vol. 20, no. 14, pp. 2077–2095, 2019. [Google Scholar]

25. M. Awan, M. Khan, Z. Ansari, A. Yasin and H. Shehzad, “Fake profile recognition using big data analytics in social media platforms,” International Journal of Computer Applications in Technology, vol. 68, no. 3, pp. 215–222, 2022. [Google Scholar]

26. U. Ahmed, J. Lin and G. Srivastava, “Social media multiaspect detection by using unsupervised deep active attention,” IEEE Transactions on Computational Social Systems, vol. 6, no. 1, pp. 1–9, 2022. [Google Scholar]

27. R. Dougnon, P. Fournier-Viger, J. Lin and R. Nkambou, “Inferring social network user profiles using a partial social graph,” Journal of Intelligent Information Systems, vol. 47, no. 1, pp. 313–344, 2016. [Google Scholar]

28. Z. Guo, K. Yu, Y. Li, G. Srivastava and J. Lin, “Deep learning-embedded social internet of things for ambiguity-aware social recommendations,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1067–1081, 2021. [Google Scholar]

29. P. Wanda, “RunMax: Fake profile classification using novel nonlinear activation in CNN,” Social Network Analysis and Mining, vol. 12, no. 1, pp. 1–11, 2022. [Google Scholar]

30. M. Irfan and M. Munsif, “Deepdive: A learning-based approach for virtual camera in immersive contents,” Virtual Reality & Intelligent Hardware, vol. 4, no. 3, pp. 247–262, 2022. [Google Scholar]

31. L. Allein, M. Moens and D. Perrotta, “Preventing profiling for ethical fake news detection,” Information Processing & Management, vol. 60, no. 2, pp. 1–22, 2023. [Google Scholar]

32. M. Munsif, H. Afridi, M. Ullah, S. Khan, F. Cheikh et al., “A lightweight convolution neural network for automatic disasters recognition,” in 2022 10th European Workshop on Visual Information Processing, IEEE, Lisbon, Portugal, pp. 1–6, 2022. [Google Scholar]

33. D. Choi, S. Chun, H. Oh, J. Han and T. Kwon, “Rumor propagation is amplified by echo chambers in social media,” Scientific Reports, vol. 10, no. 1, pp. 1–10, 2020. [Google Scholar]

34. M. Vicario, W. Quattrociocchi, A. Scala and F. Zollo, “Polarization and fake news: Early warning of potential misinformation targets,” ACM Transactions on the Web, vol. 13, no. 2, pp. 1–22, 2019. [Google Scholar]

35. A. Shrestha and F. Spezzano, “Characterizing and predicting fake news spreaders in social networks,” International Journal of Data Science and Analytics, vol. 13, no. 4, pp. 385–398, 2022. [Google Scholar]

36. A. Al-Zoubi, J. Alqatawna, H. Faris and M. Hassonah, “Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context,” Journal of Information Science, vol. 47, no. 1, pp. 58–81, 2021. [Google Scholar]

37. A. Ala’M, H. Faris, J. Alqatawna and M. Hassonah, “Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts,” Knowledge-Based Systems, vol. 153, no. 1, pp. 91–104, 2018. [Google Scholar]

38. A. Ali and A. Abdullah, “Fake accounts detection on social media using stack ensemble system,” International Journal of Electrical & Computer Engineering, vol. 12, no. 3, pp. 3013–3022, 2022. [Google Scholar]

39. R. Aswani, A. Kar and P. Ilavarasan, “Detection of spammers in twitter marketing: A hybrid approach using social media analytics and bio inspired computing,” Information Systems Frontiers, vol. 20, no. 3, pp. 515–530, 2018. [Google Scholar]

40. D. Michail, N. Kanakaris and I. Varlamis, “Detection of fake news campaigns using graph convolutional networks,” International Journal of Information Management Data Insights, vol. 2, no. 2, pp. 1–10, 2022. [Google Scholar]

41. S. Mussiraliyeva, B. Omarov, P. Yoo and M. Bolatbek, “Applying machine learning techniques for religious extremism detection on online user contents,” Computers, Materials & Continua, vol. 70, no. 1, pp. 915–934, 2022. [Google Scholar]

42. K. Purba, D. Asirvatham and R. Murugesan, “Influence maximization diffusion models based on engagement and activeness on instagram,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp. 2831–2839, 2020. [Google Scholar]

43. S. Kudugunta and E. Ferrara, “Deep neural networks for bot detection,” Information Sciences, vol. 467, no. 1, pp. 312–322, 2018. [Google Scholar]