Search Results (30)

Fake social media profiles increasingly resemble ordinary accounts, combining believable images, fluent biographies, copied posts, and selective engagement, which makes single signal detection unreliable. This study develops and evaluates a forensic natural language processing (NLP) framework that treats profile text as digital evidence rather than raw model input. Profiles were collected from Facebook, Instagram, X, Truth Social, and LinkedIn through network expansion and vocabulary guided scraping, then anonymized, merged, cleaned, and organized into structured text and profile records. Ground truth came from human eye annotation (referred to throughout as human eye review, human review, or manual review) supported by three LLM reviewers (referred to throughout as LLM assisted review), OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini 1.5 Pro, with strict agreement logic producing confidence-tiered labels across platform-specific label spaces covering scam, fraud, harmful, and legitimate behavior. Each model received a defined set of label categories, was instructed to return a single label with a one sentence rationale, and worked independently with no cross-model communication. Multidimensional features, including TF-IDF representations, sentence embeddings, scam lexicon counts, sentiment and emotion scores, and behavioral indicators, were extracted through a pipeline built to keep links, hashtags, contact markers, and repeated phrasing as forensic signals rather than noise. Traditional classifiers, transformer models, and ensembles were trained on verified labels, and the best model per platform was applied to the full dataset of over 309,108 records under confidence filtered classification, with predictions below a 0.70 probability threshold flagged for human review. The framework links classification outputs to textual indicators through explainable AI, achieving weighted F1 scores between 0.86 and 0.96 across five platforms. These results show that profile text alone provides strong, production-relevant evidence for detecting fake and harmful accounts, even though the framework treats this as one evidence stream to be weighed alongside image and behavioral signals rather than as a final determination on its own. The accompanying evidence trail means an investigator or a trust and safety team can see why a profile was flagged rather than being handed a bare score, which is what turns a classifier into a tool that can be checked, challenged, and used directly in an investigation. Full article

Detecting fake news is a challenging task and an important area of research for social media researchers. This task also involves clarifying accountability mechanisms that demonstrate the credibility of quotable sources, such as networks that document the spread of misinformation. Deep learning techniques, particularly neural networks that rely on popular graph representation techniques such as graph convolutional networks (GCNs), are increasingly being utilized to detect fake news, fake accounts, and rumors spreading through social media. In this paper, features were extracted using TF-IDF, Bag-of-Words, and bigrams. The evaluation was conducted using the standard Kaggle/ISOT and GossipCop datasets, which include news headlines and published models. Using the extracted features, the proposed GCN-based model/classifier achieved a high detection accuracy of 95% by combining TF-IDF and Bag-of-Words representations. The results demonstrate that the extracted features improve the efficiency of the detection model. Full article

Due to the capability of graph structures to model complex relationships, graph anomaly detection has significant application value in various domains, including financial fraud detection, network security, and fake account identification. Traditional graph anomaly detection methods follow a specialized paradigm of “one dataset, one model”, which requires retraining or fine-tuning models for each new domain. This approach faces critical challenges in practical applications, namely high deployment costs and limited generalization capability. To address this problem, generalist graph anomaly detection aims to achieve the goal of “train once, apply across domains”. However, existing generalist methods primarily rely on graph neural networks to implicitly learn structural information, where the learned structural representations are tightly coupled with specific topology distributions, resulting in limited structural stability under domain shifts. To address this limitation, we propose DualGAD, a generalist graph anomaly detection method via a dual-encoder architecture. In particular, DualGAD introduces explicit structural modeling that characterizes the relative topological deviation of nodes with respect to the overall graph structure, thereby enhancing structural invariance across heterogeneous domains. This method separately models node attribute information and explicit graph structural information via an attribute feature encoder and an explicit structural feature encoder, and adopts an “attribute-dominant, structure-complementary” fusion strategy to achieve collaborative modeling. Experiments on eight real datasets demonstrate that DualGAD achieves an average improvement of 3.12% in AUROC compared to the strongest baseline methods, exhibiting significant cross-domain generalization capability. Full article

The proliferation of fake reviews on e-commerce and social platforms has severely undermined consumer trust and market integrity, necessitating robust and interpretable real-time detection mechanisms with multi-sensor data fusion capabilities. While traditional machine learning approaches have shown promise in identifying fraudulent reviews, they often lack transparency and fail to leverage the rich contextual knowledge embedded in large-scale datasets. In this paper, we propose KE-MLLM (Knowledge-Enhanced Multimodal Large Language Model), a unified framework that integrates knowledge-enhanced prompting with parameter-efficient fine-tuning for explainable fake review detection. Our approach employs LoRA (Low-Rank Adaptation) to fine-tune lightweight large language models (LLaMA-3-8B) on review text, while incorporating multimodal behavioral sensor signals including temporal patterns, user metadata, and social network characteristics for comprehensive anomaly sensing. To address the critical need for interpretability in fraud detection systems, we implement a Chain-of-Thought (CoT) reasoning module that generates human-understandable explanations for classification decisions, highlighting linguistic anomalies, sentiment inconsistencies, and behavioral red flags. We enhance the model’s discriminative capability through a knowledge distillation strategy that transfers domain-specific expertise from larger teacher models while maintaining computational efficiency suitable for edge sensing devices. Extensive experiments on two benchmark datasets—YelpChi and Amazon Reviews from the DGL Fraud Dataset—show that KE-MLLM achieves strong performance, reaching an F1-score of 94.3% and an AUC-ROC of 96.7% on YelpChi and outperforming the strongest baseline in our comparison by 5.8 and 4.2 percentage points, respectively. Furthermore, human evaluation indicates that the generated explanations achieve 89.5% consistency with expert annotations, suggesting that the framework can improve the interpretability and practical usefulness of automated fraud detection systems. The proposed framework provides a useful step toward more accurate and interpretable fake review detection and offers a practical reference for building more transparent and accountable AI systems in high-stakes applications. Full article

This brief survey acts as a fundamental resource for researchers beginning their exploration into fake news detection. It emphasizes the importance of dataset quality and diversity in enhancing the effectiveness of detection models, detailing key features, labeling systems, and prevalent biases. It also presents the challenges and limitations. By addressing ethical considerations (such as privacy and consent, societal impacts, transparency, and accountability) and best practices (annotation methodologies, real-world dynamics, reliability, and validity), we offer a thorough overview of current datasets. Additionally, our contribution includes a GitHub repository that aggregates publicly available datasets into a single, easily accessible portal, thereby supporting further research and development in the fight against fake news. Full article

Social media platforms are currently confronted with a substantial problem concerning the presence of fake accounts, which pose a threat by spreading harmful content, spam, and misinformation. This study aims to address the problem by differentiating between fake and real X accounts (formerly Twitter). The need to mitigate the negative impact of fake accounts on online communities serves as the driving force for this work, with the goal of developing an effective method for identifying fake accounts and their fraudulent activities, such as posting harmful links, engaging in spamming behaviors, and disrupting online communities. The scope of this work focuses specifically on fake Twitter account detection. A comprehensive approach is taken, leveraging user information and tweets to discern between genuine and fake accounts. Various deep learning architectures are proposed and implemented, utilizing different optimizers and evaluating performance metrics. The models are trained and tested using a collected dataset, augmented to cover diverse real-life scenarios. The results show promising progress in distinguishing between fake and real accounts, revealing that the inclusion of tweet content along with user metadata does not significantly improve the classification of fake accounts. It also highlights the importance of selecting appropriate optimizers. The implications of this study are relevant to social media platforms, users, and researchers. The findings provide insights into combating fake accounts and their fraudulent activities, contributing to the enhancement of online community safety. While the research is specific to Twitter, the methodology and insights gained may be potentially generalizable to other social media platforms. Full article

This paper presents a multi-level annotation model for detecting fake news in Kazakh and Russian languages, aiming to enhance understanding of disinformation strategies in multilingual digital media environments. Unlike traditional binary models, our approach captures the complexity of disinformation by accounting for both linguistic and cultural factors. To support this, a corpus of over 5000 news texts was manually annotated using the Label Studio platform. The annotation scheme consists of seven interrelated categories: CLAIM, SOURCE, EVIDENCE, DISINFORMATION_TECHNIQUE, AUTHOR_INTENT, TARGET_AUDIENCE, and TIMESTAMP. Inter-annotator agreement, evaluated using Cohen’s Kappa, ranged from 0.72 to 0.81, indicating substantial consistency. The annotated data reveals recurring patterns of disinformation, such as emotional manipulation, targeting of vulnerable individuals, and the strategic concealment of intent. Semantic relations between entities, such as CLAIM → EVIDENCE and CLAIM → AUTHOR_INTENT were formalized to represent disinformation narratives as knowledge graphs. This study contributes the first linguistically and culturally adapted annotation model for Kazakh and Russian languages, providing a robust and empirical resource for building interpretable and context-aware fake news detection systems. The resulting annotated corpus and its semantic structure offer valuable empirical material for further research in natural language processing, computational linguistics, and media studies in low-resource language environments. Full article

Text-to-image (T2I) synthesis, driven by advancements in deep learning and generative models, has seen significant improvements, enabling the creation of highly realistic images from textual descriptions. However, this rapid development brings challenges in distinguishing synthetic images from genuine ones, raising concerns in critical areas such as security, privacy, and digital forensics. To address these concerns and ensure the reliability and authenticity of data, this paper conducts a systematic study on detecting fake images generated by text-to-image synthesis models. Specifically, it evaluates the effectiveness of deep learning methods that leverage ensemble learning for detecting fake images. Additionally, it introduces a multi-classification technique to attribute fake images to their source models, thereby enabling accountability for model misuse. The effectiveness of these methods is assessed through extensive simulations and proof-of-concept experiments. The results reveal that these methods can effectively detect fake images and associate them with their respective generation models, achieving impressive accuracy rates ranging from 98.00% to 99.87% on our custom dataset, “DeepGuardDB”. These findings highlight the potential of the proposed techniques to mitigate synthetic media risks, ensuring a safer digital space with preserved authenticity across various domains, including journalism, legal forensics, and public safety. Full article

This paper focuses on the detection of fake accounts on Instagram and proposes a novel solution that aims to address this problem. More specifically, a machine learning-based solution is introduced that can be employed by Instagram-based applications to combat this phenomenon. To accomplish this, publicly available data from Instagram users are collected and processed. After making the necessary feature additions to and removals from these data, they are fed into machine learning algorithms with the aim of detecting fake Instagram accounts. The interest of the results presented in this study lies in their comparability with the results of other similar studies, with the difference that fewer input features have been used. This addresses significant research challenges regarding the performance of and confidence in obtained results in this specific problem domain. Full article

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. However, the authenticity of reviews is highly debated in the field of Internet-based process-of-life service consumption. In recent years, due to the rapid growth of these industries, the detection of fake reviews has gained increasing attention. Fake reviews seriously mislead customers and damage the authenticity of online reviews. Various fake review classifiers have been developed, taking into account the content of the reviews and the behavior involved in the reviews, such as rating, time, etc. However, there has been no research considering the credibility of reviewers and merchants as part of identifying fake reviews. In order to improve the accuracy of existing fake review classification and detection methods, this study utilizes a comment text processing module to model the content of reviews, utilizes a reviewer behavior processing module and a reviewed merchant behavior processing module to model consumer review behavior sequences that imply reviewer credibility and merchant review behavior sequences that imply merchant credibility, respectively, and finally merges the two features for fake review classification. The experimental results show that, compared to other models, the model proposed in this paper improves the classification performance by simultaneously modeling the content of reviews and the credibility of reviewers and merchants. Full article

An online social media platform such as Instagram has become a popular communication channel that millions of people are using today. However, this media also becomes an avenue where fake accounts are used to inflate the number of followers on a targeted account. Fake accounts tend to alter the concepts of popularity and influence on the Instagram media platform and significantly impact the economy, politics, and society, which is considered cybercrime. This paper proposes a framework to classify fake and real accounts on Instagram based on a deep learning approach called the Long Short-Term Memory (LSTM) network. Experiments and comparisons with existing machine and deep learning frameworks demonstrate considerable improvement in the proposed framework. It achieved a detection accuracy of 97.42% and 94.21% on two publicly available Instagram datasets, with F-measure scores of 92.17% and 89.55%, respectively. Further experiments on the Twitter dataset reveal the effectiveness of the proposed framework by achieving an impressive accuracy rate of 99.42%. Full article

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWOPSO), improved the performance in accurately detecting fake accounts on Instagram. Full article

Today, social media are an integral part of everyone’s life. In addition to their traditional uses of creating and maintaining relationships, they are also used to exchange views and all kinds of content. With the development of these media, they have become the target of various attacks. In particular, the existence of fake accounts on social networks can lead to many types of abuse, such as phishing or disinformation, which is a big challenge nowadays. In this work, we present a solution for detecting fake accounts on the X portal (formerly Twitter). The main goal behind the developed solution was to use images of X portal accounts and perform image classification using machine learning. As a result, it was possible to detect real and fake accounts and indicate the type of a particular account. The created solution was trained and tested on an adequately prepared dataset containing 15,000 generated accounts and real X portal accounts. The CNN model performing with accuracy above 92% and manual test results allow us to conclude that the proposed solution can be used to detect false accounts on the X portal. Full article

In the context of the increasing spread of misinformation via social network services, in this study, we addressed the critical challenge of detecting and explaining the spread of fake news. Early detection methods focused on content analysis, whereas recent approaches have exploited the distinctive propagation patterns of fake news to analyze network graphs of news sharing. However, these accurate methods lack accountability and provide little insight into the reasoning behind their classifications. We aimed to fill this gap by elucidating the structural differences in the spread of fake and real news, with a focus on opinion consensus within these structures. We present a novel method that improves the interpretability of graph-based propagation detectors by visualizing article topics and propagation structures using BERTopic for topic classification and analyzing the effect of topic agreement on propagation patterns. By applying this method to a real-world dataset and conducting a comprehensive case study, we not only demonstrated the effectiveness of the method in identifying characteristic propagation paths but also propose new metrics for evaluating the interpretability of the detection methods. Our results provide valuable insights into the structural behavior and patterns of news propagation, contributing to the development of more transparent and explainable fake news detection systems. Full article

In recent discussions in the European Parliament, the need for regulations for so-called high-risk artificial intelligence (AI) systems was identified, which are currently codified in the upcoming EU Artificial Intelligence Act (AIA) and approved by the European Parliament. The AIA is the first document to be turned into European Law. This initiative focuses on turning AI systems in decision support systems (human-in-the-loop and human-in-command), where the human operator remains in control of the system. While this supposedly solves accountability issues, it includes, on one hand, the necessary human–computer interaction as a potential new source of errors; on the other hand, it is potentially a very effective approach for decision interpretation and verification. This paper discusses the necessary requirements for high-risk AI systems once the AIA comes into force. Particular attention is paid to the opportunities and limitations that result from the decision support system and increasing the explainability of the system. This is illustrated using the example of the media forensic task of DeepFake detection. Full article