Search Results (17)

Social media has greatly accelerated the speed of information dissemination, but it has also inevitably led to a proliferation of rumors. Due to the deceptive nature of rumors, people often find it difficult to distinguish truth from falsehood, resulting in economic losses and social panic. Existing pre-trained models often focus on keywords while neglecting logic, making them prone to semantic traps. To address this, we propose the Logic-Reasoning Augmented DeBERTa (LoRAD) model. LoRAD utilizes an LLM to generate logical evidence and leverages DeBERTa’s disentangled attention mechanism to effectively integrate this evidence with the source text. We evaluate our method on three public datasets and a newly constructed dataset, Twitter26-Mini. Results show that LoRAD achieves state-of-the-art performance on all datasets. Furthermore, experiments demonstrate that LoRAD offers better performance and robustness than large language models (LLMs) while maintaining high inference speed, making it suitable for real-time rumor detection. Full article

Phishing and spam emails continue to pose a serious cybersecurity threat, leading to financial loss, information leakage, and reputational damage. Traditional email filtering approaches struggle to keep pace with increasingly sophisticated attack strategies, particularly those involving malicious content and deceptive attachments. This study proposes a dual-layer deep learning architecture designed to enhance email security by improving the detection of phishing and spam messages. The first layer employs deep learning models, including LSTM- and transformer-based classifiers, to analyze email content and structural features across legitimate, phishing, and spam emails. The second layer focuses on spam emails containing attachments and applies advanced transformer models, such as GPT-2 and XLM-RoBERTa, to assess contextual and semantic patterns associated with malicious attachments. By integrating textual analysis with attachment-level inspection, the proposed architecture overcomes limitations of single-layer approaches that rely solely on email body content. Experimental evaluation using accuracy and F1-score demonstrates that the dual-layer framework achieves a minimum F1-score of 98.75 percent in spam–ham classification and attains an attachment detection accuracy of up to 99.46 percent. These results indicate that the proposed approach offers a reliable and scalable solution for enhancing real-world email security systems. Full article

Phishing attacks continue to evolve and exploit fundamental human impulses, such as trust and the need for a rapid response, as well as emotional triggers. This makes the human mind both a valuable asset and a significant vulnerability. The proliferation of zero-day vulnerabilities has been identified as a significant exacerbating factor in this threat landscape. To address these evolving challenges, we introduce CrossPhire: a multimodal deep learning framework with an end-to-end architecture that captures semantic and visual cues from multiple data modalities, while also providing methodological insights for anti-phishing multimodal learning. First, we demonstrate that markup-free semantic text encoding captures linguistic deception patterns more effectively than DOM-based approaches, achieving 96–97% accuracy using textual content alone and providing the strongest single-modality signal through sentence transformers applied to HTML text stripped of structural markup. Second, through controlled comparison of fusion strategies, we show that simple concatenation outperforms a sophisticated gating mechanism so-called Mixture-of-Experts by 0.5–10% when modalities provide complementary, non-redundant security evidence. We validate these insights through rigorous experimentation on five datasets, achieving competitive same-dataset performance (97.96–100%) while demonstrating promising cross-dataset generalization (85–96% accuracy under distribution shift). Additionally, we contribute Phish360, a rigorously curated multimodal benchmark with 10,748 samples addressing quality issues in existing datasets (96.63% unique phishing HTML vs. 16–61% in prior benchmarks), and provide LIME-based explainability tools that decompose predictions into modality-specific contributions. The rapid inference time (0.08 s) and high accuracy results position CrossPhire as a promising solution in the fight against phishing attacks. Full article

The widespread dissemination of misleading news presents serious challenges to public discourse, democratic institutions, and societal trust. Misleading-news classification (MNC) has been extensively studied through deep neural models that rely mainly on semantic understanding or large-scale pretrained language models. However, these methods often lack interpretability and are computationally expensive, limiting their practical use in real-time or resource-constrained environments. Existing approaches can be broadly categorized into transformer-based text encoders, hybrid CNN–LSTM frameworks, and fuzzy-logic fusion networks. To advance research on MNC, this study presents a lightweight multimodal framework that extends the Fuzzy Deep Hybrid Network (FDHN) paradigm by introducing a linguistic and behavioral biometric perspective to MNC. We reinterpret the FDHN architecture to incorporate linguistic cues such as lexical diversity, subjectivity, and contradiction scores as behavioral signatures of deception. These features are processed and fused with semantic embeddings, resulting in a model that captures both what is written and how it is written. The design of the proposed method ensures the trade-off between feature complexity and model generalizability. Experimental results demonstrate that the inclusion of lightweight linguistic and behavioral biometric features significantly enhance model performance, yielding a test accuracy of 71.91 ± 0.23% and a macro F1 score of 71.17 ± 0.26%, outperforming the state-of-the-art method. The findings of the study underscore the utility of stylistic and affective cues in MNC while highlighting the need for model simplicity to maintain robustness and adaptability. Full article

Detecting deception in emotionally grounded natural language remains a significant challenge due to the subtlety and context dependence of deceptive intent. In this work, we use a structured behavioral dataset in which participants produce truthful and deceptive statements under emotional and social constraints. To maintain label accuracy and semantic consistency, we propose a multilayer validation pipeline combining selfconsistency prompting with feedback-guided revision, implemented through the CoTAM (Chain-of-Thought Assisted Modification) method. Our results demonstrate that this framework enhances deception detection by leveraging a sentence decomposition strategy that highlights subtle emotional and strategic cues, improving interpretability for both models and human annotators. Full article

Advances in multimodal artificial intelligence enable new sensor-inspired approaches to lie detection by combining behavioral perception with generative reasoning. This study presents a deception detection framework that integrates deep video and audio processing with large language models guided by chain-of-thought (CoT) prompting. We interpret neural architectures such as ViViT (for video) and HuBERT (for speech) as digital behavioral sensors that extract implicit emotional and cognitive cues, including micro-expressions, vocal stress, and timing irregularities. We further incorporate a GPT-5-based prompt-level fusion approach for video–language–emotion alignment and zero-shot inference. This method jointly processes visual frames, textual transcripts, and emotion recognition outputs, enabling the system to generate interpretable deception hypotheses without any task-specific fine-tuning. Facial expressions are treated as high-resolution affective signals captured via visual sensors, while audio encodes prosodic markers of stress. Our experimental setup is based on the DOLOS dataset, which provides high-quality multimodal recordings of deceptive and truthful behavior. We also evaluate a continual learning setup that transfers emotional understanding to deception classification. Results indicate that multimodal fusion and CoT-based reasoning increase classification accuracy and interpretability. The proposed system bridges the gap between raw behavioral data and semantic inference, laying a foundation for AI-driven lie detection with interpretable sensor analogues. Full article

The proliferation of fake profiles on social media presents critical cybersecurity and misinformation challenges, necessitating robust and scalable detection mechanisms. Such profiles weaken consumer trust, reduce user engagement, and ultimately harm brand reputation and platform credibility. As adversarial tactics and synthetic identity generation evolve, traditional rule-based and machine learning approaches struggle to detect evolving and deceptive behavioral patterns embedded in dynamic user-generated content. This study aims to develop an AI-driven, multi-modal deep learning-based detection system for identifying fake profiles that fuses textual, visual, and social network features to enhance detection accuracy. It also seeks to ensure scalability, adversarial robustness, and real-time threat detection capabilities suitable for practical deployment in industrial cybersecurity environments. To achieve these objectives, the current study proposes an integrated AI system that combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) for deep semantic textual analysis, ConvNeXt for high-resolution profile image verification, and Heterogeneous Graph Attention Networks (Hetero-GAT) for modeling complex social interactions. The extracted features from all three modalities are fused through an attention-based late fusion strategy, enhancing interpretability, robustness, and cross-modal learning. Experimental evaluations on large-scale social media datasets demonstrate that the proposed RoBERTa-ConvNeXt-HeteroGAT model significantly outperforms baseline models, including Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). Performance achieves 98.9% accuracy, 98.4% precision, and a 98.6% F1-score, with a per-profile speed of 15.7 milliseconds, enabling real-time applicability. Moreover, the model proves to be resilient against various types of attacks on text, images, and network activity. This study advances the application of AI in cybersecurity by introducing a highly interpretable, multi-modal detection system that strengthens digital trust, supports identity verification, and enhances the security of social media platforms. This alignment of technical robustness with brand trust highlights the system’s value not only in cybersecurity but also in sustaining platform credibility and consumer confidence. This system provides practical value to a wide range of stakeholders, including platform providers, AI researchers, cybersecurity professionals, and public sector regulators, by enabling real-time detection, improving operational efficiency, and safeguarding online ecosystems. Full article

In recent years, the proliferation of fake news and misinformation has grown exponentially, far surpassing that of genuine news and posing a serious threat to social stability. Existing research in fake news detection primarily applies contrastive learning methods with a single-hot labeling strategy. The issue does not lie with contrastive learning as a technique but with its current application in fake news detection systems. Specifically, these systems penalize all negative samples equally due to the use of single-hot labeling, thus overlooking the underlying semantic relationships among negative samples. As a result, contrastive learning models tend to learn from simple samples while neglecting highly deceptive samples located at the boundary between true and false, as well as the heterogeneity of text-image features, which complicates cross-modal fusion. To mitigate these known limitations in current applications, this paper proposes a fake news detection method based on contrastive learning and cross-modal interaction. First, a consistency-aware soft-label contrastive learning mechanism based on semantic similarity is designed to provide more granular supervision signals for contrastive learning. Secondly, a difficult negative sample mining strategy based on a similarity matrix is designed to optimize the symmetry alignment of image and text features, which effectively improves the model’s ability to discriminate boundary samples. To further optimize the feature fusion process, a cross-modal interaction module is designed to learn the symmetric interaction relationship between image and text features. Finally, an attention mechanism is designed to adaptively adjust the contributions of text-image features and interaction features, forming the final multimodal feature representation. Experiments are conducted on two major social media platform datasets, and compared with existing methods, the proposed method effectively improves the detection capability of fake news. Full article

In order to address the pressing challenge posed by the proliferation of fake news in the digital age, we emphasize its profound and harmful impact on societal structures, including the misguidance of public opinion, the erosion of social trust, and the exacerbation of social polarization. Current fake news detection methods are largely limited to superficial text analysis or basic text–image integration, which face significant limitations in accurately identifying deceptive information. To bridge this gap, we propose the UC-CMAF framework, which comprehensively integrates news text, images, and user comments through an adaptive co-attention fusion mechanism. The UC-CMAF workflow consists of four key subprocesses: multimodal feature extraction, cross-modal adaptive collaborative attention fusion of news text and images, cross-modal attention fusion of user comments with news text and images, and finally, input of fusion features into a fake news detector. Specifically, we introduce multi-head cross-modal attention heatmaps and comment importance visualizations to provide interpretability support for the model’s predictions, revealing key semantic areas and user perspectives that influence judgments. Through the cross-modal adaptive collaborative attention mechanism, UC-CMAF achieves deep semantic alignment between news text and images and uses social signals from user comments to build an enhanced credibility evaluation path, offering a new paradigm for interpretable fake information detection. Experimental results demonstrate that UC-CMAF consistently outperforms 15 baseline models across two benchmark datasets, achieving F1 Scores of 0.894 and 0.909. These results validate the effectiveness of its adaptive cross-modal attention mechanism and the incorporation of user comments in enhancing both detection accuracy and interpretability. Full article

Online reviews significantly influence consumer behavior and business reputations. Detecting fake reviews is important for maintaining trust and integrity in these platforms. We present an aspect-based sentiment analysis approach, referred to as FakeDetectionGCN, to distinguish genuine feedback from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph convolutional networks are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated (genuine) as well as computer-generated (fake) reviews. It has been evaluated on two types of datasets and has shown strong performance across both. Through this work, we contribute to the effective detection of fake reviews and maintaining a trustworthy online review ecosystem. Full article

Phishing emails are malicious email messages that aim to deceive users into revealing sensitive information by imitating legitimate emails. These emails are usually among the first steps in most cyberattacks, often appearing as an urgent message, seemingly from reputable sources, in order to provoke an immediate action from the recipient. Their manipulative nature leverages social engineering techniques to exploit human psychological weaknesses, personality traits, and a range of cognitive, behavioral, and technical vulnerabilities. In this review, the factors that contribute to users’ susceptibility to phishing attacks were investigated. The study focuses on exploring how demographic and psychological factors influence individuals’ vulnerability to phishing emails, with the goal of identifying and categorizing the key factors that increase susceptibility. Twenty-seven studies were examined, revealing that demographic factors, behavioral tendencies, psychological traits and contextual elements play a key role on the users’ susceptibility in phishing emails. The results vary according to the type of methodology that has been used, indicating a need for further investigation and refinement in each respective procedure. Significant investigation has been conducted in identifying the factors contributing to users’ susceptibility to phishing emails, and existing studies do not fully cover the complexity of the topic. There is more to be studied regarding these factors, especially in understanding their complex interactions and impacts across different contexts. Further research is essential so that we may be able to more accurately predict users’ characteristics and the factors that make someone more susceptible to phishing and thus more vulnerable to phishing email attacks. Full article

The aim of this study is to characterize the cognitive aspect of the semantic space of hope in patients in the terminal stage of cancer. This was confirmed in the research on hope by C. R. Snyder and B. Schrank. Hope is of great importance in all the great world religions and belief systems, both as regards a personal God or impersonal deities. Hoping is a human capacity with varying affective, cognitive and behavioral dimensions. Psychological, pedagogical (particularly in the framework of special needs pedagogy and thanatological pedagogy) and theological reflection on hope can provide support for dying people. In order to conduct the research, the semantic differential research method was selected. The research technique employed was a therapeutic conversation, and the research tool was the B.L. Block’s DSN-3 test. The DSN-3 test allows one to assess hope in the semantic space in three aspects: cognitive, emotional and functional. For the purposes of this study, only the cognitive aspect was taken into account. The study was begun on 1 April 2010 and ended in the last days of December 2020. It included 110 male patients in the terminal stage of cancer. The youngest respondent was 19 years old and the oldest was 94 years old. The surveyed men most often perceived hope in the semantic space in the cognitive aspect as more true, wise, meaningful and real than false, stupid, meaningless and deceptive. Their attitude to hope was, therefore, more affirmative than negative. The research did not reveal the importance of the age of the respondents on the degree of affirmation/negation of hope in the cognitive aspect in the semantic space; however, men in the period of late maturity and professional activity expressed the lowest level of the affirmation of hope. It is worthwhile to conduct further research concerning hope in other aspects (especially emotional and functional) in the semantic space in order to use the obtained results to consider what to take into account when providing patients in the terminal stage of cancer with better personalized holistic care than before. Full article

Social media usage for news, feedback on services, and even shopping is increasing. Hotel services, food cleanliness and staff behavior are also discussed online. Hotels are reviewed by the public via comments on their websites and social media accounts. This assists potential customers before they book the services of a hotel, but it also creates an opportunity for abuse. Scammers leave deceptive reviews regarding services they never received, or inject fake promotions or fake feedback to lower the ranking of competitors. These malicious attacks will only increase in the future and will become a serious problem not only for merchants but also for hotel customers. To rectify the problem, many artificial intelligence–based studies have performed discourse analysis on reviews to validate their genuineness. However, it is still a challenge to find a precise, robust, and deployable automated solution to perform discourse analysis. A credibility check via discourse analysis would help create a safer social media environment. The proposed study is conducted to perform discourse analysis on fake and real reviews automatically. It uses a dataset of real hotel reviews, containing both positive and negative reviews. Under investigation is the hypothesis that strong, fact-based, realistic words are used in truthful reviews, whereas deceptive reviews lack coherent, structural context. Therefore, frequency weight–based and semantically aware features were used in the proposed study, and a comparative analysis was performed. The semantically aware features have shown strength against the current study hypothesis. Further, holdout and k-fold methods were applied for validation of the proposed methods. The final results indicate that semantically aware features inspire more confidence to detect deception in text. Full article

The advancement of the Internet has changed people’s ways of expressing and sharing their views with the world. Moreover, user-generated content has become a primary guide for customer purchasing decisions. Therefore, motivated by commercial interest, some sellers have started manipulating Internet ratings by writing false positive reviews to encourage the sale of their goods and writing false negative reviews to discredit competitors. These reviews are generally referred to as deceptive reviews. Deceptive reviews mislead customers in purchasing goods that are inconsistent with online information and thus obstruct fair competition among businesses. To protect the right of consumers and sellers, an effective method is required to automate the detection of misleading reviews. Previously developed methods of translating text into feature vectors usually fail to interpret polysemous words, which leads to such functions being obstructed. By using dynamic feature vectors, the present study developed several misleading review-detection models for the Chinese language. The developed models were then compared with the standard detection-efficiency models. The deceptive reviews collected from various online forums in Taiwan by previous studies were used to test the models. The results showed that the models proposed in this study can achieve 0.92 in terms of precision, 0.91 in terms of recall, and 0.91 in terms of F1-score. The improvement rate of our proposal is higher than 20%. Accordingly, we prove that our proposal demonstrated improved performance in detecting misleading reviews, and the models based on dynamic feature vectors were capable of more accurately capturing semantic terms than the conventional models based on the static feature vectors, thereby enhancing effectiveness. Full article