  As social media continues to grow rapidly, the prevalence of harassment on
these platforms has also increased. This has piqued the interest of researchers
in the field of fake detection. Social media data, often forms complex graphs
with numerous nodes, posing several challenges. These challenges and
limitations include dealing with a significant amount of irrelevant features in
matrices and addressing issues such as high data dispersion and an imbalanced
class distribution within the dataset. To overcome these challenges and
limitations, researchers have employed auto-encoders and a combination of
semi-supervised learning with a GAN algorithm, referred to as SGAN. Our
proposed method utilizes auto-encoders for feature extraction and incorporates
SGAN. By leveraging an unlabeled dataset, the unsupervised layer of SGAN
compensates for the limited availability of labeled data, making efficient use
of the limited number of labeled instances. Multiple evaluation metrics were
employed, including the Confusion Matrix and the ROC curve. The dataset was
divided into training and testing sets, with 100 labeled samples for training
and 1,000 samples for testing. The novelty of our research lies in applying
SGAN to address the issue of imbalanced datasets in fake account detection. By
optimizing the use of a smaller number of labeled instances and reducing the
need for extensive computational power, our method offers a more efficient
solution. Additionally, our study contributes to the field by achieving an 81%
accuracy in detecting fake accounts using only 100 labeled samples. This
demonstrates the potential of SGAN as a powerful tool for handling minority
classes and addressing big data challenges in fake account detection.
