Content area
The high rate of social media development has triggered a high rate of fake accounts, which are a great risk to the privacy of users and the integrity of the platform. These malicious accounts are hard to detect because user activity data is highly imbalanced, dimensional, and sequential. The emergence of fake profiles on social media endangers the privacy and trust of social media users. It is difficult to detect such accounts because of high-dimensional, highly sequential, and imbalanced user behavior data. Current techniques tend to miss out on the complicated activity patterns or even overfit, which is why a strong, scalable, and precise model of social media fraud detection is required. This study suggests a new deep learning architecture that entails a Temporal Convolutional Network (TCN) with Generative Adversarial Network (GAN)-based data augmentation to generate minority classes, and Autoencoder-based feature extraction to reduce dimensionality. The Seagull Optimization Algorithm (SOA), which is a metaheuristic algorithm, is used to optimize hyperparameters by balancing efficiency and speed of convergence in global search. The framework is tested on benchmark datasets (Cresci-2017 and TwiBot-22) and compared to the state-of-the-art models. It has been shown in experiments that the suggested TCN-GAN-SOA framework performs better, with ROC-AUC scores of 0.96 on Cresci-2017 and 0.95 on TwiBot-22, and a higher precision-recall value and better F1-scores. In addition, computational efficiency can be verified by the runtime analysis; case studies prove the framework’s strength when handling various situations of fraudulent behaviors. The given solution offers a scalable, reliable, and accurate methodology of detecting social media fraud based on the combination of sophisticated sequence modeling, realistic data augmentation, and hyperparameter optimization.
Introduction
With billions of users and a vast amount of material produced every day, social media has become one of the most influential digital spaces. Facebook, Instagram, Twitter (X), and LinkedIn, among other platforms, are not only used in personal communication but also in activities, digital marketing, and political debates1. Nonetheless, such widespread access and openness also bring into the picture vicious players who use social media to commit fraud. The most frequent schemes of fraud are phishing attacks, financial fraud, identity theft, misinformation, bot-based campaigns, and deepfake-based manipulations2. In contrast to the conventional fraud detection mechanisms that process structured and transactional data, social media fraud detection has to process heterogeneous, high-volume, and dynamic content streams3.
Although there is improvement in the research of fraud detection, the current systems are not efficient in dealing with the special issues that social media sites present. The fraud trends are changing at a very fast pace and tend to take the shape of the genuine user traffic to go unnoticed4. Data (text, images, videos, and network links) are unstructured and multimodal, making them even harder to detect. Furthermore, the manual or rule-based monitoring cannot be used to manage the large-scale and ever-evolving environment of social media5. This, in turn, causes high false positives in fraud detection systems, the inability to adjust to new attack patterns, and the inability to scale over multiple platforms6. These loopholes indicate the necessity of high-quality, dynamic, and smart fraud detection systems with specific applications to social media ecosystems.
This research is motivated by the current sophistication of the fraudulent activities on social media due to the resulting financial losses, lack of user trust, and platform credibility7. As fraud with the help of Artificial Intelligence (AI) (deepfakes and bot networks) becomes commonplace, the threats to political, economic, and social stability grow8. This paper aims to create scalable and adaptive models that combine machine learning, deep learning, and optimization algorithms to support the detection of fraud in real-time and guarantee safer and more reliable online spaces.
To identify social media fraud, the project aims to develop an adaptable deep learning architecture that can get around issues with user activity sequences, high-dimensional data, and class imbalance. The framework combines a Temporal Convolutional Network (TCN) to learn behavioral sequences, GAN-based data augmentation to enhance minority classes, and Autoencoder-based feature extraction for dimensionality reduction. The Seagull Optimization Algorithm (SOA) is used to optimize hyperparameters to improve performance. The study aims to obtain a higher accuracy level, a reduced level of false positives, and an enhanced level of robustness in fraudulent account detection by comparing the model to real-world datasets and state-of-the-art methods. The primary contributions of this study are as follows:
We propose a hybrid deep learning architecture that integrates TCN, GAN-based data augmentation, and Autoencoder-based feature extraction to effectively process sequential, imbalanced, and high-dimensional social media data.
We introduce the use of GAN-based augmentation for fraud detection, enhancing the representation of minority classes and improving overall detection performance.
We employ the SOA algorithm for hyperparameter optimization, achieving an effective balance between search efficiency and convergence rate.
We conduct large-scale empirical analyses on real-world datasets, demonstrating that our approach achieves higher accuracy, lower false-positive rates, and greater robustness compared to state-of-the-art techniques.
The remainder of this paper is organized as follows. An overview of relevant work is included in Section “Literature review”. In Section “Methodology”, the suggested technique is explained. Section “Experimental setup” describes the experimental setup. Section “Results and discussion” presents the findings and comments. Section "Conclusion" concludes the study by summarizing the results and suggesting the next directions.
Literature review
Qu et al.9 proposed a Sybil detection system for identifying fake reviews in mobile social networks, leveraging the power of GANs. The framework integrates TextCNN as a feature extractor, a domain classifier to facilitate cross-domain representation learning, and a Sybil detector formulated within a minimax game structure. Their results emphasized better generalization and accuracy in detecting Sybil attacks, but generally, their study was more of an effective study than an explanation of possible weaknesses and implementation issues. In a comparable study, Al-Alshaqi and Rawat10 introduced a disinformation classification model that is transformer-based, and it had a significant advantage over Hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) baselines in the detection of fake news. Although transformers enhanced contextual awareness, the work raised questions regarding the risk of misclassification and the inability to come up with completely automated fake news detection systems.
Based on GAN applications, Bordbar et al.11 used user similarity metrics in a GAN model to identify fake accounts in social media with an 80% AUC rate. Despite its ability to detect anomalies, the model was affected by the scalability problem because GANs are computationally intensive and are not easily interpretable. Vallileka et al.12 introduced DeepTweet, a transformer-based fake news detector that uses a new attention mechanism to identify linguistic cues, which is better than the previous methods. Nevertheless, the authors did not discuss the limitations of the datasets, scalability, and assumptions that can influence the application in practice. Liu et al.13 demonstrated a Hierarchical Attention-based Graph Neural Network (HA-GNN), which enhanced the performance of fraud detection by 3.21–9.97% in terms of RUC. The research showed that the weighted adjacency matrices were useful in all relations, but dense ties between fraudsters would mask the fraudulent behaviour.
In other words, GAN-based fraud detection was highlighted in different areas. Zhu et al.14 used GANs to detect anomalies in transaction data, emphasizing the fact that the new technology is capable of modeling complicated data distributions and enhancing the security of fraud detection. The strategy used adversarial verification graphs and succeeded in developing a fraud detection system that integrated deep learning. However, it was limited to real-world usage due to its dependence on the quality and scalability of training data. Shukla et al.15 scaled GAN to social media bot detection, reducing mode collapse by using multiple discriminators, and their results were better than state-of-the-art models. The generator itself was, however, dangerous in terms of adversarial abuse, the ability of the bots to evade detection, and the model also lacked explainability, making governance challenging.
Hybrid models and deep learning have also been well adopted. Babatope and Anil16 adopted CNNs, RNNs, LSTMs, autoencoders, and GANs to identify anomalous social media behavior since they believe that simple patterns cannot be recognized by rule-based and statistical models. Lubis et al.17 used CNNs and transfer learning to identify fake accounts on Twitter, and it was more accurate than traditional methods (51 to 93.9). Despite these gains notwithstanding, the model was vulnerable to high-level bots because it relied on profile/activity features and did not handle imbalances. Mohawesh et al.18 used RoBERTa and LSTM to detect fake reviews, with 96.03% accuracy and improved interpretability with SHAP and attention mechanism, but this method was limited by the fact that it only relies on linguistic features. On the same note, Hyder et al.19 introduced DeceptiveBERT, which had the highest accuracy at 84.79% when detecting deceptive reviews, but the model was computationally intensive and had memory limitations. Table 1 provides an overview of the literature review.
Table 1. An overview of the literature Review.
Reference | Method Used | Findings | Limitations |
|---|---|---|---|
Qu et al. (2022)9 | GAN with TextCNN, domain classifier, Sybil detector | Enhanced Sybil detection accuracy in fake reviews; improved generalization via a minimax game | Focused on effectiveness; lacks discussion of limitations or implementation challenges |
Al-Alshaqi & Rawat (2023)10 | Transformer-based classification | Outperformed Hybrid CNN and RNN in fake news detection | Risk of misclassification; concerns about automated solution reliability |
Bordbar et al. (2023)11 | GAN with user similarity measures | Achieved 80% AUC in fake account detection | Scalability issues; interpretability challenges |
Vallileka et al. (2023)12 | Transformer-based DeepTweet with novel attention | Outperformed existing fake news detection methods; effective linguistic cue identification | No discussion of weaknesses; dataset and scalability constraints |
Liu et al. (2023)13 | Hierarchical Attention-based Graph Neural Network (HA-GNN) | Achieved 3.21–9.97% RUC improvement using weighted adjacency matrices | Fraudsters can evade detection with dense, legitimate-looking connections |
Zhu et al. (2024)14 | GANs with adversarial verification graphs | Effective anomaly detection in fraud; improved transaction security | Data dependency; scalability issues for real-time applications |
Shukla et al. (2024)15 | Dropout-GAN with multiple discriminators | Superior accuracy; mitigated mode collapse | Generator misuse risk; lack of model explainability |
Babatope & Anil (2024)16 | CNNs, RNNs, LSTMs, Autoencoders, GANs | Improved anomalous user behavior detection | Traditional models are inadequate; they have limited feature generalization. |
Lubis et al. (2024)17 | CNN with transfer learning | Improved fake account detection from 51% to 93.9% accuracy | Relies heavily on profile/activity features; lacks imbalance handling |
Mohawesh et al. (2024)18 | RoBERTa + LSTM with SHAP | Achieved 96.03% accuracy; transparent classification explanations | Focus on linguistic features; ignores semantic context |
Hyder et al. (2024)19 | DeceptiveBERT with transfer learning | Accuracy up to 84.79%; effective deceptive review detection | High computational cost; memory constraints |
Joshi (2024)20 | Transformers (BERT, RoBERTa, XLNet) with BiLSTM, CNN | RoBERTa achieved 97.1% accuracy in fake review detection | Long training times; varying error rates across models |
Li et al. (2024)21 | Multimodal Aggregation Portrait Model (MAPM) + SIDN | 27% improvement in fake user detection on the Weibo dataset | Transient methods are prone to misjudgment; neglect of time-series features |
Patil et al. (2024)22 | Ensemble ML (Decision Trees, RF, XGBoost, etc.) with Majority Voting | Achieved 99.12% accuracy and precision in fake profile detection | Risk of overfitting; heavy dependency on profile features |
Taneja et al. (2025)23 | Fraud-BERT (Transformer-based contextual framework) | Robust recruitment fraud detection; F1-score 0.93 | Domain-specific (job data only); computationally expensive |
Recent work is still dominated by transformer-based methods. Joshi20 pitted BERT and RoBERTa, XLNet, BiLSTM, and CNN models against each other in the detection of fake reviews, with RoBERTa scoring the highest 97.1% accuracy, but noting that it requires more time to train than the others. Li et al.21 proposed a multimodal Aggregation Portrait Model (MAPM) that uses a Sequence Interval Detection Net (SIDN) to learn temporal behaviors, which greatly enhanced fake user detection on Weibo by 27%. Nevertheless, previous methods of transient detection were likely to make erroneous decisions, which highlights the significance of the inclusion of time-series characteristics. The majority voting technique coupled with ensemble machine learning was used by Patil et al.22, who reached the impressive accuracy of fake profile detection at 99.12%. However, the findings were concerned with overfitting and reliance on features of user profiles, which constrained the extrapolation to noisy real-world conditions. Lastly, Taneja et al.23 proposed Fraud-BERT, a BERT-based contextual model of recruitment fraud detection. The model proved to be quite strong in working with imbalanced data, with an F1-score of 0.93, but its specific training and high level of computation limited its generalizability.
Problem statement and research gap
The process of identifying fraud in social media is a complicated and dynamic task because of the dynamism of user operations and the volume of data produced24. Bad actors pretend to act like good users, and in many cases, it is not easy to differentiate between bad actors and bad users. One of the biggest problems is to model sequential user behavior patterns, which are critical to the representation of time dependency in actions like posting, liking, sharing, or commenting25,26. These complex temporal structures are usually not well learned with traditional sequence models, resulting in less accurate detection. Also, social media data is in itself of a high-dimensional nature, consisting of a variety of features, such as text, images, user interactions, and network structures27. High dimensions not only add complexity to computations, but also add redundancy and noise, and it becomes hard to find discriminative features using models28. All these issues collectively point to the necessity of sophisticated fraud detection systems that are able to handle sequential behavior, deal with the issue of the imbalance of classes without overfitting, and achieve dimensionality reduction without losing important information28.
The existing research in the area of social media fraud detection has demonstrated encouraging findings, yet it has some limitations. The majority of the deep learning-based and GAN-based methods lack scalability and come at a high cost, and thus are inapplicable to large-scale or real-time fraud detection. The other issue that keeps recurring is the problem of class imbalance, since it is usually much lower than the number of legitimate accounts, and this could cause biased forecasting and a large number of false negatives. Also, although sequential and time-dependent user behavior is important in detecting fraud, typical sequence models do not represent the more intricate temporal dependencies, which leads to a lower detection rate. Other conventional oversampling techniques, like SMOTE, also carry the risk of overfitting due to the creation of overlapping or unrealistic synthetic samples, which inhibit generalization. Moreover, the data in social media is high-dimensional and heterogeneous, and the current models usually do not learn small, significant features, which results in redundancy and inefficiency. Moreover, some models are either platform-specific and hence cannot be considered in other platforms, or they may not be robust and understandable, a factor that casts doubt on their reliability when applied in real-life settings.
To address these gaps, this study suggests a new hybrid deep learning structure to combine a TCN to model sequence user behaviors, GAN-based data augmentations to generate realistic minority classes, and an Autoencoder-based feature extraction to reduce dimensions. This design is more scalable and efficient to compute, and the features are also better. To enhance the robustness further, the framework utilizes the SOA to tune the hyperparameters, with the cost of exploration and convergence, to obtain a more consistent and precise output. The study demonstrates that the model applies to a broad variety of social media data, has a low false positive rate, and is more accurate; as a result, it makes a significant contribution to the advancement of fraud detection research.
Methodology
Figure 1 displays the suggested framework’s flowchart. GAN-based augmentation is used to preprocess and balance raw datasets and reduce the number of features with an autoencoder. The minimized features are categorized by a TCN, whose parameters are optimized with the help of SOA. The last model is tested using ROC-AUC, Precision-Recall curves, confusion matrices, and runtime analysis to make the model accurate and efficient.
Fig. 1 [Images not available. See PDF.]
Workflow of the Proposed Framework.
Dataset
In this paper, Cresci 201729 and TwiBot-2230 were used as benchmark datasets to test the suggested social media fraud detection framework. Cresci’s (2017) data has around 14,368 Twitter accounts (3,474 of which are human and 10,894 of which are bots). It has detailed user metadata such as the frequency of tweets, retweet behavior, information about followers and following, and other features of the profile level. This database has been extensively utilized in studies to classify bots, detect spam, and analyze fraud in social media. Launched in 2022, TwiBot-22 is a large-scale, graph-based dataset for Twitter bot identification. It deals with the shortcomings of the past datasets, including their small size, missing graph structure, and poor quality of annotations.
TwiBot-22 consists of one million labeled users, of which 860,057 are human users and 139,943 are bots, which is the largest such dataset. The dataset describes Twitter as a heterogeneous graph, where the graph entities are of four types (User, Tweet, List, and Hashtag) and relation types are fourteen (follows, posts, likes, retweets, and mentions). This rich structure enables complex interactions and behavioral patterns of users to be modeled.
The summary of the statistics of the two datasets in terms of the number of users, tweets, human and bot tweets, and network edges is summarized in Table 2. The two datasets offer a complete basis on which to train and test strong social media fraud detection models.
Table 2. Statistics of the Datasets.
Dataset | Cresci 2017 | TwiBot-22 |
|---|---|---|
Number of Human Accounts | 3,474 | 860,057 |
Number of Bot Accounts | 10,894 | 139,943 |
Total Number of Users | 14,368 | 1,000,000 |
Total Number of Tweets | 6,637,615 | 88,217,457 |
Human Tweets | 2,839,361 | 81,250,102 |
Bot Tweets | 3,798,254 | 6,967,355 |
Number of Edges | 6,637,615 | 170,185,937 |
Table 3 summarizes the datasets in terms of the number of samples, features, and distribution of classes:
Table 3. Summary of datasets Used.
Dataset Name | Number of Samples | Features | Class Distribution |
|---|---|---|---|
Cresci 2017 | 14,368 users | User metadata, tweet frequency, retweet patterns, follower/following info, profile-level features | 3,474 humans, 10,894 bots |
TwiBot-22 | 1,000,000 users | User profiles, tweets, heterogeneous graph (User, Tweet, List, Hashtag), network relations (follows, likes, retweets, mentions) | 860,057 humans, 139,943 bots |
Data preprocessing and augmentation
In order to have a high-quality input in the proposed social media fraud detection framework, a systematic preprocessing and augmentation of the datasets were performed. The preprocessing pipeline covers missing values, scales feature values, and alleviates class imbalance. It consists of two important phases, namely, GAN-based augmentation and Autoencoder-based feature extraction.
GAN-based augmentation
In order to alleviate the imbalance of classes in social media data, a Generative Adversarial Network (GAN)31 is used to create synthetic images of fraudulent accounts that are realistic. GANs, proposed by Goodfellow et al.42, are two neural networks trained adversarially, whereby one of them is the Generator (G) and the other is the Discriminator (D). In order to create synthetic data samples that are identical to actual data, the generator creates random noise instances of synthetic data. As shown in Fig. 2, the discriminator assesses the legitimacy of these situations and distinguishes between real and fraudulent data.
Fig. 2 [Images not available. See PDF.]
Structure of the GAN-Based Augmentation.
In a minimax game, where the performance of one network is dictated by the parameter of the other, the discriminator and generator optimize their loss functions throughout training until a Nash equilibrium is reached. The loss at the discriminator is given by:
1
where represents the loss for real samples and represents the loss for synthetic samples.
The overall GAN objective is formulated as:
2
In this case, and are the real data distribution and noise prior distribution, respectively. The discriminator is optimized to maximise this loss, and the generator is optimised to reduce it, forming a two-player minimax optimisation problem. Upon training, the generator will generate true-to-life samples of minority classes, which are then concatenated to the original dataset to constitute an augmented dataset :
3
The given augmentation method maintains the natural distribution of the minority group, decreases the imbalance of the classes, and enhances the generalization of the resulting TCN model on social media fraud detection.
Feature extraction using autoencoders
Autoencoder32 is an unsupervised machine learning algorithm, which is an artificial neural network, that is trained to learn efficient representations of training data by reconstructing the results so that it is as close to the original input as possible. Figure 3 illustrates the three layers that comprise an average autoencoder: an input layer, a hidden layer (usually of lower size than the input), and an output layer of the same dimension as the input.
Fig. 3 [Images not available. See PDF.]
Structure of the Autoencoder.
Autoencoders are stronger than classical non-linear dimensionality reduction algorithms like Principal Component Analysis (PCA)33 since they are capable of learning non-linear mappings, which reflect the complex structure in the data. The encoder-decoder paradigm is followed in the training process, in which the input X is first encoded into a lower-dimensional representation Y using the encoder and then decoded back to using the decoder.
The encoder function can be defined as:
4
and the decoder function reconstructs the input as:
5
in which and are network weights and biases, and is the activation function. The latent feature representation is the output of the hidden layer Y, which is used in classification tasks.
The Autoencoder will be trained to reduce the reconstruction loss, which is the difference between the input and its reconstruction . The overall objective functional role is:
6
To compute the loss in the case of linear reconstruction, the mean squared error (L1 loss) is usually defined as:
7
In non-linear functions, the cross-entropy loss (L2 loss) is commonly employed:
8
The Autoencoder can be trained by reducing these loss functions to learn compact, informative representations of the user activity and network features, which are subsequently input into the TCN to be modeled sequentially, detecting fraud. The process is efficient in terms of dimensionality reduction and preservation of critical behavioral patterns that enhance training efficiency and detection accuracy.
Model architecture
The suggested model is based on a Temporal Convolutional Network (TCN)34 to capture the sequential dynamics of social media user attention. TCN is a sequence modeling network that is a special and one-dimensional convolutional network. Dilated convolutions and residual blocks, two key components of the TCN that set it apart from conventional CNNs, allow the network to learn long-range correlations in sequential data without encountering any kind of instability while training deep networks. Figure 4 depicts the TCN’s architecture.
The dimension of the input and output layer of the TCN is , where is the total number of time steps and Is the number of feature channels. When applied to social media fraud detection, , the number of latent features of the Autoencoder. The TCN can deal with multi-dimensional inputs and is hence adaptable to sequences with more than one feature channel based on user activity and network interactions.
Causal Dilated Convolution.
The TCN utilises 1D fully-convolutional layers, in which the final fully connected layer that is characteristic of CNNs is substituted with a convolutional layer. Both layers use a dilated causal convolution, meaning that the result at a particular step only relies on the present and past inputs, and no future information is leaked. The causal convolution definition is dilated to the following:
9
In which is the size of the kernel, is the dilation factor, in the previous layer is the input, and is the filter weights. The dilation factor is usually exponentially increased by each layer to widen the effective receptive field, which is defined as:
10
in which i is the number of layers. Zero padding is added at the start of the input sequence to make the length of the output sequence equal to that of the input sequence at every layer.
Fig. 4 [Images not available. See PDF.]
Architecture of the TCN.
Residual Blocks.
In order to make deep networks easier to train and to avoid performance degradation, the TCN incorporates residual blocks (as shown in Fig. 5), which are inspired by ResNet. The block of residual layers has two layers of causal-dilated convolutions with ReLU activation and a shortcut connection that sums the input to the output:
11
12
In this case, and are the convolutional weights, and is the ReLU activation function. The residual connections enable the network to retain its power to model linear associations in sequential data, as well as the ability to identify complicated nonlinear trends. In contrast to the traditional CNNs, the TCN does not use pooling or fully connected layers, and thus, the network does not compromise the temporal resolution.
The TCN can capture sequential dependencies of user activity, which is why, by using causal dilated convolutions and residual blocks, the proposed framework can accurately and robustly detect fraudulent social media accounts.
Fig. 5 [Images not available. See PDF.]
Residual Block in TCN Architecture.
Hyperparameter optimization
Seagull Optimization Algorithm (SOA)35 is a bio-inspired metaheuristic that emulates the migration and spiral attacking behavior of seagulls, and thus it is also good at balancing exploration and exploitation when optimizing hyperparameters. SOA is embraced in this study to optimize the most important hyperparameters of the TCN model, including filter size, dilation factor, depth of the residual block, dimension of the hidden layer, and the learning rate.
Migration Stage (exploration).
In this stage, agents (seagulls) search the search space, and they do not collide with each other. The new search agent position is provided by:
13
where is the current position at iteration x, and A controls movement:
14
with , decreasing linearly to balance exploration and exploitation. The movement toward the best neighbor is modeled as:
15
16
and is a random variable. The updated position becomes:
17
Attacking Stage (Exploitation).
During this stage, seagulls orbit around the optimal solution, and this allows local search to be accurate. The spiral motion is calculated with respect to three planes:
18
19
20
where , with constants u, v controlling spiral shape, and . The agent’s position is then updated as:
21
where is the global best solution.
The algorithm starts with a randomly created population and constantly changes the position of agents. The parameter decreases linearly between and 0, which allows an exploration to become exploitation, and the parameter controls the adaptive movement. This equilibrium allows SOA to escape premature convergence and attain strong global optimization. Figure 6 shows the flowchart of the SOA.
Fig. 6 [Images not available. See PDF.]
Flowchart of the SOA.
The fitness function in this study is that of the validation accuracy of the TCN-based fraud detection model. In this way, SOA makes sure that the chosen hyperparameters are optimal to maximize the model performance and the generalization in both TwiBot-22 and Cresci datasets.
In a nutshell, to represent the end-to-end architecture of the suggested TCN-GAN-SOA structure, the pseudocode is shown in Algorithm 1.
Computational complexity
The computational efficiency of the proposed framework is examined through the prism of time complexity and space complexity, which provides an asymptotic view of the framework’s scalability. The four significant elements of the system, namely GAN-based augmentation, Autoencoder feature extraction, TCN sequence modeling, and SOA hyperparameter optimization, are analyzed.
Time complexity
In the case of the GAN-based augmentation stage, the discriminator and the generator network are trained iteratively on all training samples. The time complexity is represented as:
22
where k denotes the number of training epochs, n the number of samples, and d the feature dimensionality.
The autoencoder feature extraction step entails the encoding and decoding of data in terms of hidden layers that have lower dimensions. Its calculation cost is as follows:
23
where ℎ represents the latent dimension of the Autoencoder.
Causal dilated convolutions are used in several layers in the TCN sequence modeling stage. In a network of L layers, kernel size k, and input sequence length T, the time complexity can be shown as:
24
Such formulation emphasizes the efficiency of TCNs in contrast to recurrent models because the convolutional operations can be performed simultaneously.
During the SOA-based hyperparameter optimization step, the solutions of a population are assessed one after another. The corresponding time complexity is
25
where is the population size, is the number of iterations, and the cost of computing one model configuration.
Space complexity
The storage of network parameters in the key components of the framework is the main cause of the memory requirements of the framework. In the case of the GAN, the space complexity is , determined by the weight matrices connecting the input dimension d with the latent representation ℎ. Likewise, the Autoencoder occupies space to store the parameters of the encoder and decoder.
The convolutional filters of L layers with kernel size k and hidden dimension ℎ in the case of the TCN, lead to a space complexity of . In the case of the SOA, the memory footprint is because the population of candidate hyperparameter sets may be maintained, which can be expressed as , where is the number of hyperparameters under optimization.
Experimental setup
Software and hardware tools
A workstation equipped with an Intel Core i9 CPU, 64 GB of RAM, and an NVIDIA RTX 4090 with 24 GB of RAM was used for all of the tests. Python 3.10 and the PyTorch deep learning package both made advantage of the provided framework. Other libraries, like scikit-learn, were utilized in the preprocessing and evaluation, and TensorBoard was utilized in monitoring training performance.
Training procedure and hyperparameter configuration
The datasets (Cresci 2017 and TwiBot-22) were initially preprocessed with the GAN-based augmentation to equalize classes and the Autoencoder-based feature extraction to decrease dimensions. To ensure a fair and impartial evaluation, the processed data was then divided into training, validation, and testing sets. After being preprocessed, the latent features were supplied to the TCN for sequential modeling. Training was done using the Adam optimizer with an initial learning rate tuned using SOA. To avoid overfitting, a mini-batch size of 128 was used, and early halting was used with a 10-epoch patience period. To make it robust, each experiment was run five times, and the average results were presented.
Table 4 presents the settings of the hyperparameters of every component of the proposed framework. The selection of fixed hyperparameters (learning rates, batch sizes, network depths, etc.) was founded on common practices and initial experiments, whilst important parameters were optimized with the help of the SOA.
Table 4. Hyperparameter settings for the proposed Framework.
Component | Hyperparameter | Value/Range |
|---|---|---|
GAN | Learning rate | 0.0002 |
Batch size | 128 | |
Latent vector dimension (z) | 100 | |
Optimizer | Adam (β1 = 0.5, β2 = 0.999) | |
Training epochs | 100 | |
Autoencoder | Learning rate | 0.001 |
Hidden layer size (h) | 128 | |
Batch size | 64 | |
Activation function | ReLU (encoder), Sigmoid (decoder) | |
Epochs | 50 | |
TCN | Kernel size (k) | 3 |
Number of layers (L) | 6 | |
Dilation factors | [1, 2, 4, 8, 16, 32] | |
Residual connections | Enabled | |
Dropout rate | 0.2 | |
SOA | Population size (P) | 30 |
Max iterations (I) | 50 | |
Search range (LR) | [1e-5, 1e-2] | |
Search range (batch size) | [32, 256] | |
Objective function | Maximize F1-score / ROC-AUC |
Evaluation metrics
To evaluate the work of the proposed framework, several evaluation metrics were applied:
Accuracy: Refers to the general correctness of classification.
26
Precision: This is the ratio between the number of predicted fraudulent accounts that become actually fraudulent and the total number of predicted fraudulent accounts.
27
Recall (Sensitivity): The measure of the percentage of real fraudulent accounts that are identified.
28
F1-score: Harmonic mean of precision and recall, balancing both metrics.
29
Area Under the ROC Curve (AUC): Measures the sensitivity-specificity trade-off at classification levels.
The combination of these metrics would give an in-depth analysis of the suggested fraud detection framework, as it would not only be accurate but also capable of detecting fraudulent accounts in highly imbalanced datasets.
Results and discussion
Performance evaluation
The experimental analysis has been performed on two benchmark datasets, namely Cresci 2017 and TwiBot-22. The proposed TCN-GAN-SOA framework was benchmarked with the existing baselines, such as Autoregressive Integrated Moving Average (ARIMA)36, Multi-Layer Perceptron (MLP)37, Long Short-Term Memory (LSTM)38, Gated Recurrent Unit (GRU)39, CNN-LSTM40, and Transformer41. The accuracy, precision, recall, F1-score, and ROC-AUC were used as measures of the performance.
The initial experiments were done on the Cresci 2017 dataset, and the findings are summarized in Table 5.
Table 5. Performance comparison on the cresci 2017 dataset.
Model | Accuracy | Precision | Recall | F1-score | ROC-AUC |
|---|---|---|---|---|---|
ARIMA | 0.84 | 0.82 | 0.80 | 0.81 | 0.85 |
MLP | 0.87 | 0.85 | 0.83 | 0.84 | 0.88 |
LSTM | 0.89 | 0.87 | 0.85 | 0.86 | 0.90 |
GRU | 0.90 | 0.88 | 0.87 | 0.87 | 0.91 |
CNN–LSTM | 0.91 | 0.89 | 0.88 | 0.88 | 0.92 |
Transformer | 0.92 | 0.91 | 0.89 | 0.90 | 0.93 |
Proposed | 0.95 | 0.94 | 0.93 | 0.94 | 0.96 |
The latter experiments were conducted on the TwiBot-22 dataset, which is much larger and more complicated, as shown in Table 6.
Table 6. Performance comparison on TwiBot-22 dataset.
Model | Accuracy | Precision | Recall | F1-score | ROC-AUC |
|---|---|---|---|---|---|
ARIMA | 0.82 | 0.80 | 0.78 | 0.79 | 0.83 |
MLP | 0.85 | 0.83 | 0.81 | 0.82 | 0.86 |
LSTM | 0.88 | 0.86 | 0.84 | 0.85 | 0.89 |
GRU | 0.89 | 0.87 | 0.85 | 0.86 | 0.90 |
CNN–LSTM | 0.90 | 0.88 | 0.87 | 0.87 | 0.91 |
Transformer | 0.91 | 0.90 | 0.88 | 0.89 | 0.92 |
Proposed | 0.94 | 0.93 | 0.91 | 0.92 | 0.95 |
Figure 7 demonstrates the baseline models and the proposed TCN-GAN-SOA framework comparison of accuracy on the Cresci 2017 and TwiBot-22 databases. The findings clearly reveal that traditional statistical and shallow learning models, including ARIMA and MLP, have relatively lower accuracy levels, which are below 0.88 on both datasets. Baseline models, such as LSTM, GRU, CNN–LSTM, and Transformer, demonstrate constant gains, with the latter achieving 0.92 on Cresci 2017 and 0.91 on TwiBot-22. Nevertheless, the suggested TCN-GAN-SOA model always achieves higher scores than any of the baselines, with 0.95 on Cresci 2017 and 0.94 on TwiBot-22. This proves that combining GAN-based augmentation, Autoencoder-based feature extraction, temporal modeling using TCN, and SOA-based hyperparameter optimization results in high classification performance on datasets of different scales and complexity.
Fig. 7 [Images not available. See PDF.]
Accuracy Comparison of Baseline Models and the Proposed Framework Across Datasets.
Figure 8 shows the baseline models versus the proposed TCN-GAN-SOA framework comparison of the precision of the baseline models and the proposed model in the Cresci 2017 and TwiBot-22 datasets. The conventional approaches, including ARIMA and MLP, have lower values in terms of precision, with values of less than 0.85 in both datasets. The baselines, such as LSTM, GRU, CNN-LSTM, and Transformer, show a steady increase with the best score of 0.91 on Cresci 2017 and 0.90 on TwiBot-22. The suggested model, nevertheless, outperforms all baselines, with a precision of 0.94 on Cresci 2017 and 0.93 on TwiBot-22. Such outcomes suggest that the proposed framework reduces the number of false positives compared to the competing models to a smaller degree, which guarantees that the framework is more reliable in the distinction between fraud and legitimate accounts.
Fig. 8 [Images not available. See PDF.]
Precision Comparison of Baseline Models and the Proposed Framework Across Datasets.
Using the Cresci 2017 and TwiBot-22 datasets, Fig. 9 compares the recall of the baseline models with the suggested framework. The lowest values of recall are observed with traditional methods like ARIMA and MLP, which are below 0.83 on Cresci 2017 and 0.81 on TwiBot-22, which means that they are not as effective in detecting fraudulent accounts in a comprehensive way. The baselines baseline models, such as LSTM, GRU, CNN-LSTM, and Transformer, are getting successively better, with the Transformer scoring 0.89 on Cresci 2017 and 0.88 on TwiBot-22. However, the proposed framework has always shown better results than others, with a recall of 0.93 on Cresci 2017 and 0.91 on TwiBot-22. These findings prove that the framework is better in terms of its sensitivity, as it allows it to reveal a more significant share of fraudulent accounts without compromising the overall performance.
Fig. 9 [Images not available. See PDF.]
Recall Comparison of Baseline Models and the Proposed Framework Across Datasets.
The baseline models and the suggested framework are contrasted in Fig. 10 using the F1-score of the TwiBot-22 and Cresci 2017 datasets. Classical methods like ARIMA and MLP have lower F1-scores and are not above 0.84 on both datasets, and can be characterized as incapable of finding a compromise between precision and recall. Baseline models such as LSTM, GRU, CNN-LSTM, and Transformer show gradually increasing improvements, with the highest result of 0.90 on Cresci 2017 and 0.89 on TwiBot-22. The suggested framework performs better than any of the baselines, with an F1-score of 0.94 on Cresci 2017 and 0.92 on TwiBot-22. These findings point to the fact that the combination of GAN-based augmentation, feature extraction based on Autoencoder, TCN sequence modeling, and SOA optimization offers a balanced increment in sensitivity and specificity, and thus more reliable fraud detection.
Fig. 10 [Images not available. See PDF.]
F1-score Comparison of Baseline Models and the Proposed Framework Across Datasets.
Figure 11 shows the ROC curves of various models that were tested on the Cresci 2017 dataset. These findings are a clear indication of better performance of the proposed framework, which recorded the best ROC-AUC of 0.96. Deep learning models, such as LSTM (0.90), GRU (0.91), CNN-LSTM (0.92), and Transformer (0.93), were much improved over traditional statistical methods like ARIMA (0.85) and classical machine learning models, like MLP (0.88). However, the proposed model achieved better results than all the baselines, which underscores its strength in separating the fraudulent and the real accounts on the Cresci 2017 dataset.
Fig. 11 [Images not available. See PDF.]
ROC Curves of Different Models on the Cresci 2017 Dataset.
Figure 12 shows the ROC curves of the TwiBot-22 dataset, which proves even more the generalizability of the models. Just as the Cresci 2017 findings, the proposed TCN–GAN–SOA model recorded the highest ROC-AUC of 0.95, which is higher than the baseline models. ARIMA (0.83) and MLP (0.86) had relatively lower performance, whereas LSTM (0.89), GRU (0.90), CNN-LSTM (0.91), and Transformer (0.92) showed better performance in terms of detection ability. The high ROC-AUC of the proposed model shows its ability to fit a wide range of datasets, which guarantees the use of the model in fraud detection activities on large social media platforms with high reliability.
Fig. 12 [Images not available. See PDF.]
ROC Curves of Different Models on the TwiBot-22 Dataset.
Ablation study
To assess the role of each component in the suggested framework, an ablation study was performed by sequentially ablating GAN-based augmentation, Autoencoder feature extraction, and SOA-based hyperparameter optimization. Tables 7 and 8 show the results of the Cresci 2017 and TwiBot-22 datasets, respectively.
Table 7. Ablation study on cresci 2017 Dataset.
Configuration | Accuracy | Precision | Recall | F1-score | ROC-AUC |
|---|---|---|---|---|---|
Without GAN Augmentation | 0.91 | 0.90 | 0.87 | 0.88 | 0.91 |
Without Autoencoder Features | 0.92 | 0.91 | 0.88 | 0.89 | 0.92 |
Without SOA Optimization | 0.93 | 0.92 | 0.90 | 0.91 | 0.93 |
Full Model (Proposed TCN–GAN–SOA) | 0.95 | 0.94 | 0.93 | 0.94 | 0.96 |
The ablation study findings on the Cresci 2017 dataset are displayed in Fig. 13 and show the value of each component of the proposed TCN–GAN–SOA framework. It is crucial to address the problem of class imbalance since the model’s performance drops to 0.91 and 0.88 accuracy and F1-score, respectively, without GAN-based augmentation. With an accuracy of 0.92 and an F1-score of 0.89, the Autoencoder-based feature extraction is removed, producing somewhat better results but falling short of the whole model. Excluding SOA-based hyperparameter optimization further improves the model’s accuracy, yielding an accuracy of 0.93 and an F1-score of 0.91, which is still less than the accuracy of the whole model, which is 0.95 and has an F1-score of 0.94. These findings substantiate the idea that every module is a part of the strength of the framework, and their integration results in the highest overall performance.
Fig. 13 [Images not available. See PDF.]
Ablation Study of the Proposed Framework on the Cresci 2017 Dataset.
Table 8. Ablation study on Twibot-22 Dataset.
Configuration | Accuracy | Precision | Recall | F1-score | ROC-AUC |
|---|---|---|---|---|---|
Without GAN Augmentation | 0.90 | 0.89 | 0.86 | 0.87 | 0.91 |
Without Autoencoder Features | 0.91 | 0.90 | 0.87 | 0.88 | 0.92 |
Without SOA Optimization | 0.92 | 0.91 | 0.89 | 0.90 | 0.93 |
Full Model (Proposed TCN–GAN–SOA) | 0.94 | 0.93 | 0.91 | 0.92 | 0.95 |
Figure 14 represents the ablation experiment of the TwiBot-22 dataset, which once again confirms the usefulness of the separate parts of the suggested framework. Without GAN augmentation, the model will not be able to deal with the class imbalance, which will lead to an accuracy of 0.90 and a score of 0.87 F1. The accuracy and F1-score are 0.91 and 0.88, respectively, and the performance is marginally better than the whole model when Autoencoder-based feature extraction is excluded. With an accuracy of 0.92 and an F1-score of 0.90, the results of omitting the SOA optimization are better than those of the entire model. With an accuracy of 0.94 and an F1-score of 0.92, the whole TCN-GAN-SOA model performs the best. These results support the fact that every element plays an active role in the system, and their combination provides the most accurate and balanced results in terms of precision and recall.
Fig. 14 [Images not available. See PDF.]
Ablation Study of The Proposed Framework on the Twibot-22 Dataset.
Confusion matrix analysis
The confusion matrices of the proposed framework, presented in Fig. 15(a) with Cresci 2017 and Fig. 15(b) with TwiBot-22, give more information about the classification behaviour of the model. The framework had a true positive of 10,140 fraudulent accounts and a false negative of 754 bots on the Cresci 2017 dataset. In human accounts, the number of correctly classified (true negatives) accounts was 3,054, and the misclassifications (false positives) were relatively low (420). The framework proved to be very scalable on the larger TwiBot-22 dataset, identifying 127,600 bots accurately and missing 12,343, and a true negative rate of 823,500 using a low false positive rate of 6,500. These findings imply that the model has a high true positive rate and a low false alarm rate, which is good to strike a balance between the sensitivity and specificity of fraud detection in both small imbalanced databases and large-scale heterogeneous graph-based databases.
Fig. 15 [Images not available. See PDF.]
Confusion Matrix of the Proposed Framework on the (a) Cresci 2017 Dataset, and (b) Twibot-22 Dataset.
Empirical training and inference times
In order to supplement the theoretical analysis, the empirical training and inference time of the suggested TCN-GAN-SOA structure were estimated on an Intel i9 processor with 64 GB RAM and an NVIDIA RTX 4090 graphics card. Table 9 summarizes the findings of both the Cresci 2017 and TwiBot-22 datasets.
Table 9. Empirical training and inference times of the proposed Framework.
Process | Cresci 2017 | TwiBot-22 |
|---|---|---|
GAN augmentation | ~ 25 min | ~ 2.1 h |
Autoencoder training | ~ 8 min | ~ 35 min |
TCN training (per epoch) | ~ 12 s | ~ 55 s |
SOA optimization | ~ 3.5 h | ~ 14.8 h |
Inference speed | ~ 4,000 samples/s | ~ 3,500 samples/s |
The findings emphasize the importance of an increase in training times, depending on the dataset size, which is expected since TwiBot-22 takes longer periods to perform GAN augmentation, Autoencoder training, and SOA optimization than Cresci 2017. Nevertheless, the speed of inference is still efficient, and the framework can reach about 4,000 samples per second on Cresci 2017 and 3,500 samples per second on TwiBot-22. These findings show that despite GAN and SOA being more expensive to train, the framework is very scalable and is viable to be implemented in real-time social media fraud detection systems.
Attention-Based interpretability
To enhance interpretability, the proposed framework incorporates an attention mechanism, which emphasizes the most significant behavioral features in the process of differentiating between bots and humans. The attention layer gives the weights of activities that are important, and this helps the model to give clear details about how it made its decisions.
To represent these patterns in an attention weight heatmap, the Cresci 2017 and TwiBot-22 datasets were prepared, and the result is presented in Fig. 16. It is evident that the visualization focuses more on repetitive features like retweet frequency and fixed posting interval in bot accounts than on the organic ones, like the diversity of hashtags and balanced ratios of followers and following in human accounts. In this interpretability analysis, it has been found that the model does not just learn to classify fraudulent accounts with high accuracy but also gives meaningful cues, which are consistent with known behavioral differences.
Fig. 16 [Images not available. See PDF.]
Attention Weight Heatmap Visualization for Interpretability on Cresci-2017 and TwiBot-22.
Cross-Platform generalization performance analysis
The TCN GAN SOA framework proposed and tested on Cresci 2017 and TwiBot-22 can also be generalized to other platforms where fraud is common. Table 10 gives an overview of the baseline performance of Weibo, Facebook, and Instagram against the performance of our framework on Twitter.
Table 10. Reported baseline results from Non-Twitter platforms and comparison with the proposed Framework.
Platform | Study / Method | Reported Baseline Result | Proposed Framework (Twitter ROC-AUC) | Generalization Potential |
|---|---|---|---|---|
Aljabri et al.42\, Random Forest | F-measure = 0.944 | 0.95–0.96 | Likely to match or surpass | |
Arega et al.43 FRAppE (IIETA) | Accuracy = 99.5%, TPR = 95.9% | 0.95–0.96 | Adaptable to graph contexts | |
Chelas et al.44, ML models | Accuracy ≈ 91% | 0.95–0.96 | Requires multimodal extension |
Aljabri et al.42 obtained an F-measure of 0.944 with the help of the Random Forest on Weibo, whereas our framework obtained the ROC-AUC values of 0.95–0.96, indicating at least similar performance. Arega et al.43 used the FRAppE system, which achieved 99.5% accuracy and 95.9% TPR on malicious app detection on Facebook [IIETA]; our modular framework allows adapting to such graph-based settings. Chelas et al.44 achieved an accuracy of approximately 91% on metadata-based ML models on Instagram, meaning that multimedia-rich interaction platforms pose more challenges. However, when using Autoencoders to extend our framework with multimodality, it will be competitive among ecosystems.
Case studies
To test the soundness of the suggested model, two case studies were performed based on the Cresci 2017 and TwiBot-22 datasets. These case studies evaluate the capability of the framework in addressing issues of class imbalance, large-scale non-homogeneous data, and real-world fraud detection problems.
The Cresci 2017 data is imbalanced, having 14,368 users (only 3,474 of them are human and 10,894 are robots). Conventional methods tend to have trouble with this imbalance, resulting in large false negative rates. Previous results showed that the proposed framework had a sensitivity of 0.93, a precision of 0.94, and an ROC-AUC of 0.96. The GAN-based augmentation was effective because it produced synthetic yet diverse examples to balance the minority human population, whereas the Autoencoder eliminated noise and extracted compact features. SOA optimized the TCN; it managed to capture sequential behavioral patterns, and it resulted in better performance in fraud detection.
The largest Twitter bot detecting benchmark is Tweetbot-22, which includes 1 million labeled users and more than 88 million tweets. This data brings forth other issues of scale, heterogeneity, and graphical relations. The proposed framework was found to be accurate (0.94), specific (0.99), and ROC-AUC (0.95), which proved to be scalable to real-life contexts. The Autoencoder allowed performing proper feature compression, minimizing the dimensionality, and keeping important behavioral cues. The TCN was efficient in modeling long-range user interactions using dilated causal convolutions, and SOA was used to tune hyperparameters optimally even in high-dimensional search spaces.
Practical implications
The suggested TCN-GAN-SOA model can be highly useful in practice with the goal of enhancing the safety and reliability of online ecosystems. It allows detecting fraudulent accounts in real time, which helps platforms to decrease spam, misinformation, and malicious campaigns. The incorporation of an attention mechanism brings in interpretability, and the moderators can know why they flag the accounts, and it builds confidence in automated systems.
To users, reduced fraudulent accounts increase the integrity of online interactions, whereby they are not scammed, phished, or manipulated. To policy-makers, the framework will aid in meeting the digital trust and data protection standards, as well as in curbing the threat of disinformation on a large scale. In addition to Twitter, its cross-platform flexibility enables it to be used in an ecosystem, such as Facebook and Instagram, to provide a scalable and unified fraud detection solution.
Limitations and future work
Although it has a good performance, the study has two major limitations. First, the analysis was limited to the Twitter datasets (Cresci 2017 and TwiBot-22), and even though the cross-platform generalization was mentioned, the validation of the study on other platforms, including Facebook and Instagram, is future research. Second, in spite of the fact that the attention mechanism enhanced the interpretability, the framework continues to depend mostly on behavioral and textual characteristics, and its performance on multimodal data (e.g., images, videos) is yet to be thoroughly assessed.
The research that will be conducted in the future will concentrate on two major areas where the proposed framework can be extended. First, to establish the cross-platform generalization, we will perform testing on other datasets other than Twitter, like Weibo, Facebook, and Instagram. This will give empirical data on flexibility to various interaction patterns, graph structures, and content modalities. Second, we seek to add multimodal feature integration through the extension of Autoencoders and GAN-based augmentation to handle images, videos, and hybrid content. This will make the framework more applicable to the platforms where visual and multimedia data are central to the core of the framework to achieve more comprehensive fraud detection in various social ecosystems.
Conclusion
This study presented an innovative TCN-GAN-SOA model of fraudulent accounts recognition on social media networks. The framework, combined with GAN-based augmentation to deal with class imbalance, Autoencoder-based dimensionality reduction based on feature extraction, a Temporal Convolutional Network that models sequential activities, and SOA that optimizes hyperparameters, outperformed more established baselines. Experiments with Cresci 2017 and TwiBot-22 showed that it was effective, and the accuracy, F1-score, and ROC-AUC had a consistent increase, and confusion matrices, sensitivity-specificity analysis, and attention-based interpretability proved its robustness and transparency.
The results indicate that the temporal and behavioral patterns of fraudulent accounts are different, which the proposed model is able to effectively capture. Furthermore, the cross-platform generalization analysis indicates a strong adaptability of the model to other ecosystems such as, Weibo, Facebook, and Instagram, assuming that the multimodal extensions are performed.
Overall, this research paper presents a powerful detection model that not only identifies fraudulent actions but also offers interpretability of their underlying patterns, thereby providing a scalable and reliable solution for fraud prevention in online social networks.
Author contributions
Prashant Kumar Shukla : Idea and concept generation, Bala Dhandayuthapani V. and Santosh Reddy Addula : Methodology, Noha Alduaiji : Drafting and reviewing, Santosh Reddy Addula: Literature Review, Ankur Pandey: Supervision, Piyush Kumar Shukla : Drafting, reviewing, and supervising.
Funding
Open access funding provided by Manipal University Jaipur.
Data availability
The data that support the findings of this study are available on request from the corresponding author.
Declarations
Competing interests
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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